lme4/0000755000176000001440000000000012273504153011145 5ustar ripleyuserslme4/inst/0000755000176000001440000000000012232467522012125 5ustar ripleyuserslme4/inst/tests/0000755000176000001440000000000012232467522013267 5ustar ripleyuserslme4/inst/tests/test-resids.R0000644000176000001440000000342612174475414015671 0ustar ripleyuserslibrary("testthat") library("lme4") context("residuals") test_that("lmer", { fm1 <- lmer(Reaction ~ Days + (Days|Subject),sleepstudy) expect_equal(range(resid(fm1)), c(-101.1789,132.5466), tol=1e-6) expect_equal(range(resid(fm1, scaled=TRUE)), c(-3.953567,5.179260), tol=1e-6) expect_equal(resid(fm1,"response"),resid(fm1)) expect_equal(resid(fm1,"response"),resid(fm1,type="working")) expect_equal(resid(fm1,"deviance"),resid(fm1,type="pearson")) expect_equal(resid(fm1),resid(fm1,type="pearson")) ## because no weights given sleepstudyNA <- sleepstudy na_ind <- c(10,50) sleepstudyNA[na_ind,"Days"] <- NA fm1NA <- update(fm1,data=sleepstudyNA) fm1NA_exclude <- update(fm1,data=sleepstudyNA,na.action="na.exclude") expect_equal(length(resid(fm1)),length(resid(fm1NA_exclude))) expect_true(all(is.na(resid(fm1NA_exclude)[na_ind]))) expect_true(!any(is.na(resid(fm1NA_exclude)[-na_ind]))) }) test_that("glmer", { gm1 <- glmer(incidence/size ~ period + (1|herd), cbpp, family=binomial, weights=size) expect_equal(range(resid(gm1)), c(-3.197512,2.356677), tol=1e-6) expect_equal(range(resid(gm1, "response")), c(-0.1946736,0.3184579), tol=1e-6) expect_equal(range(resid(gm1, "pearson")), c(-2.381643,2.879069),tol=1e-6) expect_equal(range(resid(gm1, "working")), c(-1.241733,5.410587),tol=1e-6) expect_equal(resid(gm1),resid(gm1,scaled=TRUE)) ## since sigma==1 cbppNA <- cbpp na_ind <- c(10,50) cbppNA[na_ind,"period"] <- NA gm1NA <- update(gm1,data=cbppNA) gm1NA_exclude <- update(gm1,data=cbppNA,na.action="na.exclude") expect_equal(length(resid(gm1)),length(resid(gm1NA_exclude))) expect_true(all(is.na(resid(gm1NA_exclude)[na_ind]))) expect_true(!any(is.na(resid(gm1NA_exclude)[-na_ind]))) }) lme4/inst/tests/test-factors.R0000644000176000001440000000066712156422373016041 0ustar ripleyuserslibrary("testthat") library("lme4") context("factor handling in grouping variables") test_that("factors", { set.seed(101) d <- data.frame(x=runif(1000),y=runif(1000),f1=rep(1:10,each=100),f2=rep(1:10,100)) d2 <- transform(d,f1=factor(f1),f2=factor(f2)) expect_that(lm1 <- lmer(y~x+(1|f1/f2),data=d), is_a("lmerMod")) expect_that(lm2 <- lmer(y~x+(1|f1/f2),data=d2),is_a("lmerMod")) expect_equivalent(lm1,lm2) }) lme4/inst/tests/test-oldRZXfailure.R0000644000176000001440000000076212203577041017122 0ustar ripleyuserslibrary(lme4) library(testthat) load(system.file("testdata","crabs_randdata00.Rda",package="lme4")) test_that('RZX is being calculated properly', { # this is a test for an old problem, documented here: # http://stevencarlislewalker.github.io/notebook/RZX_problems.html fr <- cbind(final.snail.density, snails.lost) ~ crab.speciesS + crab.sizeS + crab.speciesS:crab.sizeS + (snail.size | plot) m <- glmer(fr, data = randdata00, family = binomial) expect_that(m, is_a("glmerMod")) }) lme4/inst/tests/test-glmer.R0000644000176000001440000001643612232467522015507 0ustar ripleyuserslibrary("testthat") library("lme4") testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1 context("fitting glmer models") test_that("glmer", { expect_warning(glmer(z~ 1|f, family=binomial, method="abc"),"Use the nAGQ argument") expect_warning(glmer(z~ 1|f, family=binomial, method="Laplace"),"Use the nAGQ argument") expect_warning(glmer(z~ 1|f, sparseX=TRUE),"has no effect at present") expect_that(gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial), is_a("glmerMod")) expect_that(gm1@resp, is_a("glmResp")) expect_that(gm1@pp, is_a("merPredD")) expect_equal(ge1 <- unname(fixef(gm1)), c(-1.39854982537216, -0.992335519118859, -1.12867532780426, -1.58030423764517), tol=5e-4) expect_equal(c(VarCorr(gm1)[[1]]), 0.41245527438386, tol=6e-4) ### expect_that(family(gm1), equals(binomial())) ### ?? binomial() has an 'initialize' component ... and the order is different expect_equal(deviance(gm1), 184.052674598026, tol=1e-5) expect_equal(sigma(gm1), 1) expect_equal(extractAIC(gm1), c(5, 194.052674598026), tol=1e-5) expect_equal(theta <- unname(getME(gm1, "theta")), 0.642226809144453, tol=6e-4) ###expect_that(X <- getME(gm1, "X"), is_equivalent_to(array(1, c(1, 30)))) expect_that(Zt <- getME(gm1, "Zt"), is_a("dgCMatrix")) expect_equal(dim(Zt), c(15L, 56L)) expect_equal(length(Zt@x), 56L) expect_equal(Zt@x, rep.int(1, 56L)) expect_that(Lambdat <- getME(gm1, "Lambdat"), is_a("dgCMatrix")) expect_equivalent(as(Lambdat, "matrix"), diag(theta, 15L, 15L)) expect_error(glFormula(cbind(incidence, size - incidence) ~ period + (1 | herd), data = subset(cbpp, herd==levels(herd)[1]), family = binomial), "must have > 1") expect_warning(glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = subset(cbpp, herd %in% levels(herd)[1:4]), family = binomial, control=glmerControl(check.nlev.gtreq.5="warning")), "< 5 sampled levels") expect_warning(fm1. <- glmer(Reaction ~ Days + (Days|Subject), sleepstudy), regexp="calling .* with family=gaussian .* as a shortcut") options(warn=2) cbppX <- transform(cbpp,prop=incidence/size) expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, start=NULL), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, verbose=0L), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, subset=TRUE), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, na.action="na.exclude"), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, offset=rep(0,nrow(cbppX))), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, contrasts=NULL), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, devFunOnly=FALSE), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, control=glmerControl(optimizer="Nelder_Mead")), "glmerMod") expect_is(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, control=glmerControl()), "glmerMod") options(warn=0) expect_warning(glmer(prop ~ period + (1 | herd), data = cbppX, family = binomial, weights=size, junkArg=TRUE), "extra argument.*disregarded") expect_warning(glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial, control=list()), "instead of passing a list of class") expect_warning(glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial, control=lmerControl()), "instead of passing a list of class") ## load(system.file("testdata","radinger_dat.RData",package="lme4")) mod <- glmer(presabs~predictor+(1|species),family=binomial, radinger_dat) expect_is(mod,"merMod") ## TODO: is this reliable across platforms or do we have to loosen? expect_equal(unname(fixef(mod)),c(0.5425528,6.4289962)) set.seed(101) d <- data.frame(y=rbinom(1000,size=1,p=0.5), x=runif(1000), f=factor(rep(1:20,each=50)), x2=rep(0:1,c(999,1))) mod2 <- glmer(y~x+x2+(1|f),data=d,family=binomial) expect_equal(unname(fixef(mod2))[1:2], c(-0.10036244,0.03548523),tol=1e-4) expect_true(unname(fixef(mod2)[3]<(-10))) mod3 <- update(mod2,family=binomial(link="probit")) expect_equal(unname(fixef(mod3))[1:2], c(-0.06288878,0.02224270),tol=1e-4) expect_true(unname(fixef(mod3)[3]<(-4))) mod4 <- update(mod2,family=binomial(link="cauchit")) ## on-the-fly creation of index variables if (FALSE) { ## FIXME: fails in testthat context -- 'd' is not found ## in the parent environment of glmer() -- but works fine ## otherwise ... set.seed(101) d <- data.frame(y1=rpois(100,1), x=rnorm(100), ID=1:100) fit1 <- glmer(y1 ~ x+(1|ID),data=d,family=poisson) fit2 <- update(fit1, .~ x+(1|rownames(d))) expect_equal(unname(unlist(VarCorr(fit1))), unname(unlist(VarCorr(fit2)))) } ## ?? testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1 if (testLevel>1) { load(system.file("testdata","mastitis.rda",package="lme4")) t1 <- system.time(g1 <- glmer(NCM ~ birth + calvingYear + (1|sire) + (1|herd),mastitis,poisson)) t2 <- system.time(g2 <- update(g1, control=glmerControl(optimizer="bobyqa"))) ## 20 seconds N-M vs 8 seconds bobyqa ... ## problem is fairly ill-conditioned so parameters ## are relatively far apart even though likelihoods are OK expect_equal(logLik(g1),logLik(g2),tol=1e-7) } }) lme4/inst/tests/test-summary.R0000644000176000001440000000075112211706636016066 0ustar ripleyuserslibrary("testthat") library("lme4") context("summarizing/printing models") test_that("lmer", { set.seed(0) J <- 8 n <- 10 N <- J * n beta <- c(5, 2, 4) u <- matrix(rnorm(J * 3), J, 3) x.1 <- rnorm(N) x.2 <- rnorm(N) g <- rep(1:J, rep(n, J)) y <- 1 * (beta[1] + u[g,1]) + x.1 * (beta[2] + u[g,2]) + x.2 * (beta[3] + u[g,3]) + rnorm(N) summary(lmer(y ~ x.1 + x.2 + (1 + x.1 | g))) summary(lmer(y ~ x.1 + x.2 + (1 + x.1 + x.2 | g))) }); lme4/inst/tests/test-rank.R0000644000176000001440000000431112156422373015321 0ustar ripleyuserslibrary("testthat") library("lme4") context("testing fixed-effect design matrices for full rank") test_that("lmerRank", { set.seed(101) n <- 20 x <- y <- rnorm(n) z <- rnorm(n) r <- sample(1:5, size=n, replace=TRUE) d <- data.frame(x,y,z,r) d$y2 <- d$y + c(0.001,rep(0,n-1)) expect_error(lmer( z ~ x + y + (1|r), data=d),"rank of X") ## should work: expect_that(lmer( z ~ x + y2 + (1|r), data=d), is_a("lmerMod")) d2 <- expand.grid(a=factor(1:4),b=factor(1:4),rep=1:10) n <- nrow(d2) d2 <- transform(d2,r=sample(1:5, size=n, replace=TRUE), z=rnorm(n)) d2 <- subset(d2,!(a=="4" & b=="4")) expect_error(lmer( z ~ a*b + (1|r), data=d2),"rank of X") d2 <- transform(d2, ab=droplevels(interaction(a,b))) ## should work: expect_that(lmer( z ~ ab + (1|r), data=d2), is_a("lmerMod")) }) test_that("glmerRank", { set.seed(101) n <- 100 x <- y <- rnorm(n) z <- rbinom(n,size=1,prob=0.5) r <- sample(1:5, size=n, replace=TRUE) d <- data.frame(x,y,z,r) ## d$y2 <- d$y + c(0.001,rep(0,n-1)) ## too small: get convergence failures ## FIXME: figure out how small a difference will still fail? d$y2 <- rnorm(n) expect_error(glmer( z ~ x + y + (1|r), data=d, family=binomial), "rank of X") expect_that(glmer( z ~ x + y2 + (1|r), data=d, family=binomial),is_a("glmerMod")) }) test_that("nlmerRank", { set.seed(101) n <- 1000 nblock <- 15 x <- abs(rnorm(n)) y <- rnorm(n) z <- rnorm(n,mean=x^y) r <- sample(1:nblock, size=n, replace=TRUE) d <- data.frame(x,y,z,r) ## save("d","nlmerRank.RData") ## see what's going on with difference in contexts fModel <- function(a,b) (exp(a)*x)^(b*y) fModf <- deriv(body(fModel), namevec = c("a","b"), func = fModel) fModel2 <- function(a,b,c) (exp(a+c)*x)^(b*y) fModf2 <- deriv(body(fModel2), namevec = c("a","b","c"), func = fModel2) ## should be OK: fails in test mode? nlmer(y ~ fModf(a,b) ~ a|r, d, start = c(a=1,b=1)) ## FIXME: this doesn't get caught where I expected expect_error(nlmer(y ~ fModf2(a,b,c) ~ a|r, d, start = c(a=1,b=1,c=1)),"Downdated VtV") }) lme4/inst/tests/test-start.R0000644000176000001440000000473112211706636015530 0ustar ripleyuserslibrary("testthat") library("lme4") context("specifying starting values") test_that("lmer", { frm <- as.formula("Reaction ~ Days + (Days|Subject)") ctrl <- lmerControl(optCtrl=list(maxfun=50)) x <- suppressWarnings(lmer(frm, data=sleepstudy, control=ctrl, REML=FALSE)) x2 <- suppressWarnings(update(x,start=c(1,0,1))) x3 <- suppressWarnings(update(x,start=list(theta=c(1,0,1)))) ff <- update(x,devFunOnly=TRUE) x2@call <- x3@call <- x@call ## hack call component expect_equal(x,x2) expect_equal(x,x3) expect_error(update(x,start=c("a")),"start must be a list or a numeric vector") expect_error(update(x,start=list(Theta=c(1,0,1))),"incorrect components") th0 <- getME(x,"theta") y <- suppressWarnings(update(x,start=th0)) expect_equal(AIC(x), 1768.025, tol=1e-6) expect_equal(AIC(y), 1763.949, tol=1e-6) expect_equal(suppressWarnings(optimizeLmer(ff,control=list(maxfun=50),start=c(1,0,1))$fval), unname(deviance(x))) expect_equal(suppressWarnings(optimizeLmer(ff,control=list(maxfun=50),start=th0)$fval), unname(deviance(y))) }) test_that("glmer", { ctrl <- glmerControl(optCtrl=list(maxfun=50)) x <- suppressWarnings(glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial, control=ctrl)) ## theta only x2 <- suppressWarnings(update(x,start=c(1))) x3 <- suppressWarnings(update(x,start=list(theta=c(1)))) ff <- update(x,devFunOnly=TRUE) x2@call <- x3@call <- x@call ## hack call component expect_equal(x,x2) expect_equal(x,x3) expect_error(update(x,start=c("a")),"start must be a list or a numeric vector") expect_error(update(x,start=list(Theta=c(1))),"incorrect components") th0 <- getME(x,"theta") y <- suppressWarnings(update(x,start=th0)) ## theta and beta x0 <- update(x,nAGQ=0) x4 <- suppressWarnings(update(x,start=list(theta=1,fixef=fixef(x0)))) x4@call <- x@call expect_equal(x,x4) x5 <- suppressWarnings(update(x,start=list(theta=1,fixef=rep(0,4)))) expect_equal(AIC(x5),221.5823,tol=1e-6) x6 <- expect_error(update(x,start=list(theta=1,fixef=rep(0,5))), "incorrect number of fixef components") ## beta only x7 <- suppressWarnings(update(x,start=list(fixef=fixef(x0)))) x7@call <- x@call expect_equal(x,x7) x8 <- suppressWarnings(update(x,start=list(fixef=rep(0,4)))) x8@call <- x5@call expect_equal(x5,x8) }) lme4/inst/tests/test-formulaEval.R0000644000176000001440000001224612174352200016640 0ustar ripleyuserslibrary("testthat") library("lme4") context("data= argument and formula evaluation") test_that("glmerFormX", { set.seed(101) n <- 50 x <- rbinom(n, 1, 1/2) y <- rnorm(n) z <- rnorm(n) r <- sample(1:5, size=n, replace=TRUE) d <- data.frame(x,y,z,r) F <- "z" rF <- "(1|r)" modStr <- (paste("x ~", "y +", F, "+", rF)) modForm <- as.formula(modStr) expect_that(m_data.3 <- glmer( modStr , data=d, family="binomial"), is_a("glmerMod")) expect_error(drop1(m_data.3),"'data' not found") expect_that(m_data.4 <- glmer( "x ~ y + z + (1|r)" , data=d, family="binomial"), is_a("glmerMod")) expect_error(drop1(m_data.4),"'data' not found") }) test_that("glmerForm", { set.seed(101) n <- 50 x <- rbinom(n, 1, 1/2) y <- rnorm(n) z <- rnorm(n) r <- sample(1:5, size=n, replace=TRUE) d <- data.frame(x,y,z,r) F <- "z" rF <- "(1|r)" modStr <- (paste("x ~", "y +", F, "+", rF)) modForm <- as.formula(modStr) ## formulas have environments associated, but character vectors don't ## data argument not specified: ## should work, but documentation warns against it expect_that(m_nodata.0 <- glmer( x ~ y + z + (1|r) , family="binomial"), is_a("glmerMod")) expect_that(m_nodata.1 <- glmer( as.formula(modStr) , family="binomial"), is_a("glmerMod")) expect_that(m_nodata.2 <- glmer( modForm , family="binomial"), is_a("glmerMod")) expect_that(m_nodata.3 <- glmer( modStr , family="binomial"), is_a("glmerMod")) expect_that(m_nodata.4 <- glmer( "x ~ y + z + (1|r)" , family="binomial"), is_a("glmerMod")) ## apply drop1 to all of these ... m_nodata_List <- list(m_nodata.0,m_nodata.1,m_nodata.2,m_nodata.3,m_nodata.4) d_nodata_List <- lapply(m_nodata_List,drop1) rm(list=c("x","y","z","r")) ## data argument specified expect_that(m_data.0 <- glmer( x ~ y + z + (1|r) , data=d, family="binomial"), is_a("glmerMod")) expect_that(m_data.1 <- glmer( as.formula(modStr) , data=d, family="binomial"), is_a("glmerMod")) expect_that(m_data.2 <- glmer( modForm , data=d, family="binomial"), is_a("glmerMod")) expect_that(m_data.3 <- glmer( modStr , data=d, family="binomial"), is_a("glmerMod")) expect_that(m_data.4 <- glmer( "x ~ y + z + (1|r)" , data=d, family="binomial"), is_a("glmerMod")) ff <- function() { set.seed(101) n <- 50 x <- rbinom(n, 1, 1/2) y <- rnorm(n) z <- rnorm(n) r <- sample(1:5, size=n, replace=TRUE) d2 <- data.frame(x,y,z,r) glmer( x ~ y + z + (1|r), data=d2, family="binomial") } m_data.5 <- ff() ff2 <- function() { set.seed(101) n <- 50 x <- rbinom(n, 1, 1/2) y <- rnorm(n) z <- rnorm(n) r <- sample(1:5, size=n, replace=TRUE) glmer( x ~ y + z + (1|r), family="binomial") } m_data.6 <- ff2() m_data_List <- list(m_data.0,m_data.1,m_data.2,m_data.3,m_data.4,m_data.5,m_data.6) badNums <- 4:5 d_data_List <- lapply(m_data_List[-badNums],drop1) ## these do NOT fail if there is a variable 'd' living in the global environment -- ## they DO fail in the testthat context expect_error(drop1(m_data.3),"'data' not found") expect_error(drop1(m_data.4),"'data' not found") ## expect_error(lapply(m_data_List[4],drop1)) ## expect_error(lapply(m_data_List[5],drop1)) ## d_data_List <- lapply(m_data_List,drop1,evalhack="parent") ## fails on element 1 ## d_data_List <- lapply(m_data_List,drop1,evalhack="formulaenv") ## fails on element 4 ## d_data_List <- lapply(m_data_List,drop1,evalhack="nulldata") ## succeeds ## drop1(m_data.5,evalhack="parent") ## 'd2' not found ## drop1(m_data.5,evalhack="nulldata") ## 'x' not found (d2 is in environment ...) ## should we try to make update smarter ... ?? ## test equivalence of (i vs i+1) for all models, all drop1() results for (i in 1:(length(m_nodata_List)-1)) { expect_equivalent(m_nodata_List[[i]],m_nodata_List[[i+1]]) expect_equivalent(d_nodata_List[[i]],d_nodata_List[[i+1]]) } expect_equivalent(m_nodata_List[[1]],m_data_List[[1]]) expect_equivalent(d_nodata_List[[1]],d_data_List[[1]]) for (i in 1:(length(m_data_List)-1)) { expect_equivalent(m_data_List[[i]],m_data_List[[i+1]]) } ## allow for dropped 'bad' vals for (i in 1:(length(d_data_List)-1)) { expect_equivalent(d_data_List[[i]],d_data_List[[i+1]]) } }) test_that("lmerForm", { set.seed(101) x <- rnorm(10) y <- rnorm(10) z <- rnorm(10) r <- sample(1:3, size=10, replace=TRUE) d <- data.frame(x,y,z,r) ## example from Joehanes Roeby m2 <- suppressWarnings(lmer(x ~ y + z + (1|r), data=d)) ff <- function() { m1 <- suppressWarnings(lmer(x ~ y + z + (1|r), data=d)) return(anova(m1)) } ff1 <- Reaction ~ Days + (Days|Subject) fm1 <- lmer(ff1, sleepstudy) fun <- function () { ff1 <- Reaction ~ Days + (Days|Subject) fm1 <- suppressWarnings(lmer(ff1, sleepstudy)) return (anova(fm1)) } anova(m2) ff() expect_equal(anova(m2),ff()) anova(fm1) fun() expect_equal(anova(fm1),fun()) }) lme4/inst/tests/test-catch.R0000644000176000001440000000105712211706636015453 0ustar ripleyuserslibrary("testthat") library("lme4") context("storing warnings, convergence status, etc.") test_that("storewarning", { gCtrl <- glmerControl(optimizer = "Nelder_Mead", optCtrl = list(maxfun=3)) expect_warning(gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data=cbpp, family=binomial, control=gCtrl), "failure to converge in 3") expect_equal(gm1@optinfo$warnings[[1]],"failure to converge in 3 evaluations") ## FIXME: why is conv==0 here? }) lme4/inst/tests/test-glmmFail.R0000644000176000001440000000347012172000024016102 0ustar ripleyuserslibrary("testthat") library("lme4") set.seed(101) d <- expand.grid(block=LETTERS[1:26], rep=1:100, KEEP.OUT.ATTRS = FALSE) d$x <- runif(nrow(d)) ## sd=1 reff_f <- rnorm(length(levels(d$block)),sd=1) ## set intercept large enough to create a constant response d$eta0 <- 4+3*d$x ## fixed effects only d$eta <- d$eta0+reff_f[d$block] dBc <- d cc <- binomial(link="cloglog") dBc$mu <- cc$linkinv(d$eta) dBc$y <- rbinom(nrow(d),dBc$mu,size=1) m1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp) context("Errors and warnings from glmer") test_that("glmer", { expect_error(glmer(y ~ 1 + (1|block), data=dBc, family=binomial(link="cloglog")), "Response is constant") expect_warning(lmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp), "calling lmer with .*family.* is deprecated.*") ## expect_equal(m1,m2) expect_warning(glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp, REML=TRUE), "extra argument.*REML") expect_warning(glmer(Reaction ~ Days + (Days|Subject), sleepstudy),"calling glmer.*family=gaussian.*deprecated") expect_warning(glmer(Reaction ~ Days + (Days|Subject), sleepstudy, family=gaussian), "calling glmer.*family=gaussian.*deprecated") m3 <- suppressWarnings(glmer(Reaction ~ Days + (Days|Subject), sleepstudy)) m4 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) m5 <- suppressWarnings(glmer(Reaction ~ Days + (Days|Subject), sleepstudy, family=gaussian)) expect_equal(fixef(m3),fixef(m5)) m3@call[[1]] <- m5@call[[1]] <- quote(lmer) ## hack call expect_equal(m3,m4) expect_equal(m3,m5) }) lme4/inst/tests/test-glmFamily.R0000644000176000001440000001165212156422373016315 0ustar ripleyuserslibrary("testthat") library("lme4") eps <- .Machine$double.eps oneMeps <- 1 - eps set.seed(1) ## sample linear predictor values for the unconstrained families etas <- list(seq.int(-8, 8, by=1), # equal spacing to asymptotic area runif(17, -8, 8), # random sample from wide uniform dist rnorm(17, 0, 8), # random sample from wide normal dist c(-10^30, rnorm(15, 0, 4), 10^30)) ## sample linear predictor values for the families in which eta must be positive etapos <- list(seq.int(1, 20, by=1), rexp(20), rgamma(20, 3), pmax(.Machine$double.eps, rnorm(20, 2, 1))) ## values of mu in the (0,1) interval mubinom <- list(runif(100, 0, 1), rbeta(100, 1, 3), pmin(pmax(eps, rbeta(100, 0.1, 3)), oneMeps), pmin(pmax(eps, rbeta(100, 3, 0.1)), oneMeps)) context("glmFamily linkInv and muEta") test_that("inverse link and muEta functions", { tst.lnki <- function(fam, frm) { ff <- glmFamily$new(family=fam) sapply(frm, function(x) expect_that(fam$linkinv(x), equals(ff$linkInv(x)))) } tst.muEta <- function(fam, frm) { ff <- glmFamily$new(family=fam) sapply(frm, function(x) expect_that(fam$mu.eta(x), equals(ff$muEta(x)))) } tst.lnki(binomial(), etas) # binomial with logit link tst.muEta(binomial(), etas) tst.lnki(binomial("probit"), etas) # binomial with probit link tst.muEta(binomial("probit"), etas) tst.lnki(binomial("cloglog"), etas) # binomial with cloglog link tst.muEta(binomial("cloglog"), etas) tst.lnki(binomial("cauchit"), etas) # binomial with cauchit link tst.muEta(binomial("cauchit"), etas) tst.lnki(poisson(), etas) # Poisson with log link tst.muEta(poisson(), etas) tst.lnki(gaussian(), etas) # Gaussian with identity link tst.muEta(gaussian(), etas) tst.lnki(Gamma(), etapos) # gamma family tst.muEta(Gamma(), etapos) tst.lnki(inverse.gaussian(), etapos) # inverse Gaussian tst.muEta(inverse.gaussian(), etapos) }) context("glmFamily linkFun and variance") test_that("link and variance functions", { tst.link <- function(fam, frm) { ff <- glmFamily$new(family=fam) sapply(frm, function(x) expect_that(fam$linkfun(x), equals(ff$link(x)))) } tst.variance <- function(fam, frm) { ff <- glmFamily$new(family=fam) sapply(frm, function(x) expect_that(fam$variance(x), equals(ff$variance(x)))) } tst.link( binomial(), mubinom) tst.variance(binomial(), mubinom) tst.link( binomial("probit"), mubinom) tst.link( binomial("cauchit"), mubinom) tst.link( gaussian(), etas) tst.variance(gaussian(), etas) tst.link( Gamma(), etapos) tst.variance(Gamma(), etapos) tst.link( inverse.gaussian(), etapos) tst.variance(inverse.gaussian(), etapos) tst.variance(MASS::negative.binomial(1), etapos) tst.variance(MASS::negative.binomial(0.5), etapos) tst.link( poisson(), etapos) tst.variance(poisson(), etapos) }) context("glmFamily devResid and aic") test_that("devResid and aic", { tst.devres <- function(fam, frm) { ff <- glmFamily$new(family=fam) sapply(frm, function(x) { nn <- length(x) wt <- rep.int(1, nn) n <- wt y <- switch(fam$family, binomial = rbinom(nn, 1L, x), gaussian = rnorm(nn, x), poisson = rpois(nn, x), error("Unknown family")) dev <- ff$devResid(y, x, wt) expect_that(fam$dev.resids(y, x, wt), equals(dev)) dd <- sum(dev) expect_that(fam$aic(y, n, x, wt, dd), equals(ff$aic(y, n, x, wt, dd))) }) } tst.devres(binomial(), mubinom) tst.devres(gaussian(), etas) tst.devres(poisson(), etapos) }) context("negative binomial") test_that("variance", { tst.variance <- function(fam, frm) { ff <- glmFamily$new(family=fam) sapply(frm, function(x) expect_that(fam$variance(x), equals(ff$variance(x)))) } tst.variance(MASS::negative.binomial(1.), etapos) nb1 <- MASS::negative.binomial(1.) cppnb1 <- glmFamily$new(family=nb1) expect_that(cppnb1$theta(), equals(1)) nb2 <- MASS::negative.binomial(2.) cppnb1$setTheta(2) sapply(etapos, function(x) expect_that(cppnb1$variance(x), equals(nb2$variance(x)))) bfam <- glmFamily$new(family=binomial()) expect_error(bfam$theta())#, "theta accessor applies only to negative binomial") expect_error(bfam$setTheta(2))#, "setTheta applies only to negative binomial") }) lme4/inst/tests/test-stepHalving.R0000644000176000001440000000061012156422373016650 0ustar ripleyuserslibrary(lme4) library(testthat) load(system.file("testdata","survdat_reduced.Rda",package="lme4")) test_that('Step-halving works properly', { # this example is known to require step-halving (or at least has in the past # required step-halving) form <- survprop~(1|nobs) m <- glmer(form,weights=eggs,data=survdat_reduced,family=binomial,nAGQ=1L) expect_that(m, is_a("glmerMod")) }) lme4/inst/tests/test-lmer.R0000644000176000001440000001533012232467515015332 0ustar ripleyuserslibrary("testthat") library("lme4") context("fitting lmer models") test_that("lmer", { expect_warning(lmer(z~ 1|f, method="abc"),"Use the REML argument") expect_warning(lmer(z~ 1|f, method="Laplace"),"Use the REML argument") expect_warning(lmer(z~ 1|f, sparseX=TRUE),"has no effect at present") expect_that(fm1 <- lmer(Yield ~ 1|Batch, Dyestuff), is_a("lmerMod")) expect_that(fm1@resp, is_a("lmerResp")) expect_that(fm1@pp, is_a("merPredD")) expect_that(fe1 <- fixef(fm1), is_equivalent_to(1527.5)) expect_that(VarCorr(fm1)[[1]][1,1], equals(1764.07265427677)) expect_that(isREML(fm1), equals(TRUE)) expect_that(REMLfun <- as.function(fm1), is_a("function")) expect_that(REMLfun(1), equals(319.792389042002)) expect_that(REMLfun(0), equals(326.023232155879)) expect_that(family(fm1), equals(gaussian())) expect_that(isREML(fm1ML <- refitML(fm1)), equals(FALSE)) ## expect_that(is.na(deviance(fm1)), equals(TRUE)) expect_that(deviance(fm1ML), equals(327.327059881135)) expect_that(sigma(fm1), equals(49.5100503990048)) expect_that(sigma(fm1ML), equals(49.5100999308089)) expect_that(extractAIC(fm1), equals(c(3, 333.327059881135))) expect_that(extractAIC(fm1ML), equals(c(3, 333.327059881135))) expect_that(vcov(fm1)[1,1], equals(375.720278729861)) expect_that(vcov(fm1ML)[1,1], equals(313.097218742665, #was 313.097224695739 tol = 1e-7)) expect_that(fm2 <- refit(fm1, Dyestuff2$Yield), is_a("lmerMod")) expect_that(fixef(fm2), is_equivalent_to(5.6656)) expect_that(VarCorr(fm2)[[1]][1,1], is_equivalent_to(0)) expect_that(getME(fm2, "theta"), is_equivalent_to(0)) expect_that(X <- getME(fm1, "X"), is_equivalent_to(array(1, c(1, 30)))) expect_that(Zt <- getME(fm1, "Zt"), is_a("dgCMatrix")) expect_that(dim(Zt), equals(c(6L, 30L))) expect_that(length(Zt@x), equals(30L)) expect_that(Zt@x, equals(rep.int(1, 30L))) expect_that(theta <- getME(fm1, "theta"), is_equivalent_to(0.848330078125)) expect_that(Lambdat <- getME(fm1, "Lambdat"), is_a("dgCMatrix")) expect_that(as(Lambdat, "matrix"), is_equivalent_to(diag(theta, 6L, 6L))) expect_that(fm3 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy), is_a("lmerMod")) expect_that(getME(fm3,"n_rtrms"), equals(2L)) expect_that(getME(fm3,"n_rfacs"), equals(1L)) expect_error(fm4 <- lmer(Reaction ~ Days + (1|Subject), subset(sleepstudy,Subject==levels(Subject)[1])), "must have > 1") expect_warning(fm4 <- lFormula(Reaction ~ Days + (1|Subject), subset(sleepstudy,Subject==levels(Subject)[1]), control=lmerControl(check.nlev.gtr.1="warning")), "must have > 1") expect_warning(fm4 <- lmer(Reaction ~ Days + (1|Subject), subset(sleepstudy,Subject %in% levels(Subject)[1:4]), control=lmerControl(check.nlev.gtreq.5="warning")), "< 5 sampled levels") sstudy9 <- subset(sleepstudy, Days == 1 | Days == 9) expect_warning(m1 <- lmer(Reaction ~ 1 + Days + (1 + Days | Subject), data = sleepstudy, subset = (Days == 1 | Days == 9)), "number of observations.*rank.*unidentifiable") expect_warning(lFormula(Reaction ~ 1 + Days + (1 + Days | Subject), data = sleepstudy, subset = (Days == 1 | Days == 9)), "number of observations.*rank.*unidentifiable") ## test arguments: promote warning to error so that any errors will stop the test options(warn=2) expect_that(lmer(Yield ~ 1|Batch, Dyestuff, REML=TRUE), is_a("lmerMod")) expect_that(lmer(Yield ~ 1|Batch, Dyestuff, start=NULL), is_a("lmerMod")) expect_that(lmer(Yield ~ 1|Batch, Dyestuff, verbose=0L), is_a("lmerMod")) expect_that(lmer(Yield ~ 1|Batch, Dyestuff, subset=TRUE), is_a("lmerMod")) expect_that(lmer(Yield ~ 1|Batch, Dyestuff, weights=rep(1,nrow(Dyestuff))), is_a("lmerMod")) expect_that(lmer(Yield ~ 1|Batch, Dyestuff, na.action="na.exclude"), is_a("lmerMod")) expect_that(lmer(Yield ~ 1|Batch, Dyestuff, offset=rep(0,nrow(Dyestuff))), is_a("lmerMod")) expect_that(lmer(Yield ~ 1|Batch, Dyestuff, contrasts=NULL), is_a("lmerMod")) expect_that(lmer(Yield ~ 1|Batch, Dyestuff, devFunOnly=FALSE), is_a("lmerMod")) expect_that(lmer(Yield ~ 1|Batch, Dyestuff, control=lmerControl(optimizer="Nelder_Mead")), is_a("lmerMod")) expect_that(lmer(Yield ~ 1|Batch, Dyestuff, control=lmerControl()), is_a("lmerMod")) ## disable test ... should be no warning expect_is(lmer(Reaction ~ 1 + Days + (1 + Days | Subject), data = sleepstudy, subset = (Days == 1 | Days == 9), control=lmerControl(check.nobs.vs.rankZ="ignore")), "merMod") expect_error(lmer(Reaction ~ 1 + Days + (1|obs), data = transform(sleepstudy,obs=seq(nrow(sleepstudy))), "number of levels of each grouping factor")) expect_is(lmer(Reaction ~ 1 + Days + (1|obs), data = transform(sleepstudy,obs=seq(nrow(sleepstudy))), control=lmerControl(check.nobs.vs.nlev="ignore", check.nobs.vs.rankZ="ignore")), "merMod") ## disable warning via options options(lmerControl=list(check.nlev.gtreq.5="ignore",check.nobs.vs.rankZ="ignore")) expect_is(fm4 <- lmer(Reaction ~ Days + (1|Subject), subset(sleepstudy,Subject %in% levels(Subject)[1:4])), "merMod") expect_is(lmer(Reaction ~ 1 + Days + (1 + Days | Subject), data = sleepstudy, subset = (Days == 1 | Days == 9)), "merMod") options(lmerControl=NULL) options(warn=0) expect_warning(lmer(Yield ~ 1|Batch, Dyestuff, junkArg=TRUE),"extra argument.*disregarded") expect_warning(lmer(Yield ~ 1|Batch, Dyestuff, control=list()), "passing control as list is deprecated") expect_warning(lmer(Yield ~ 1|Batch, Dyestuff, control=glmerControl()), "passing control as list is deprecated") }) lme4/inst/tests/test-lmerResp.R0000644000176000001440000000433712156422373016167 0ustar ripleyuserslibrary("testthat") data(Dyestuff, package="lme4") n <- nrow(Dyestuff) ones <- rep.int(1, n) zeros <- rep.int(0, n) YY <- Dyestuff$Yield mYY <- mean(YY) context("lmerResp objects") test_that("lmerResp", { mres <- YY - mYY rr <- lmerResp$new(y=YY) expect_that(rr$weights, equals(ones)) expect_that(rr$sqrtrwt, equals(ones)) expect_that(rr$sqrtXwt, equals(ones)) expect_that(rr$offset, equals(zeros)) expect_that(rr$mu, equals(zeros)) expect_that(rr$wtres, equals(YY)) expect_that(rr$wrss(), equals(sum(YY^2))) expect_that(rr$updateMu(rep.int(mYY, n)), equals(sum(mres^2))) expect_that(rr$REML, equals(0L)) rr$REML <- 1L expect_that(rr$REML, equals(1L)) }) mlYY <- mean(log(YY)) gmeanYY <- exp(mlYY) # geometric mean context("glmResp objects") test_that("glmResp", { mres <- YY - gmeanYY gmean <- rep.int(gmeanYY, n) rr <- glmResp$new(family=poisson(), y=YY) expect_that(rr$weights, equals(ones)) expect_that(rr$sqrtrwt, equals(ones)) expect_that(rr$sqrtXwt, equals(ones)) expect_that(rr$offset, equals(zeros)) expect_that(rr$mu, equals(zeros)) expect_that(rr$wtres, equals(YY)) expect_that(rr$n, equals(ones)) ## wrss() causes an update of mu which becomes ones, wtres also changes expect_that(rr$wrss(), equals(sum((YY-1)^2))) expect_that(rr$mu, equals(ones)) expect_that(rr$wtres, equals(YY-ones)) expect_that(rr$updateMu(rep.int(mlYY, n)), equals(sum(mres^2))) expect_that(rr$mu, equals(gmean)) expect_that(rr$muEta(), equals(gmean)) expect_that(rr$variance(), equals(gmean)) rr$updateWts() expect_that(1/sqrt(rr$variance()), equals(rr$sqrtrwt)) expect_that(as.vector(rr$sqrtXwt), equals(rr$sqrtrwt * rr$muEta())) }) lme4/inst/tests/test-methods.R0000644000176000001440000000327712232467515016045 0ustar ripleyuserslibrary("testthat") library("lme4") L <- load(system.file("testdata/lme-tst-fits.rda", package="lme4", mustWork=TRUE)) fm1 <- fit_sleepstudy_1 fm0 <- fit_sleepstudy_0 context("summary") test_that("summary", { ## test for multiple-correlation-warning bug cc <- capture.output(summary(fit_agridat_archbold)) expect_true(length(g <- grep("not shown by default",cc))==0 || g<=1) }) context("anova") test_that("lmer", { expect_that(anova(fm0,fm1), is_a("anova")) expect_warning(do.call(anova,list(fm0,fm1)),"assigning generic names") }) context("bootMer") test_that("bootMer", { ## testing bug-fix for ordering of sd/cor components in sd/cor matrix with >2 rows m1 <- lmer(strength~1+(cask|batch),Pastes) bb <- suppressWarnings(confint(m1,method="boot",nsim=3,quiet=TRUE)) corvals <- bb[grep("^cor_",rownames(bb)),] expect_true(all(abs(corvals)<=1)) }) context("confint") test_that("confint", { load(system.file("testdata","gotway_hessianfly.rda",package="lme4")) ## gotway_hessianfly_fit <- glmer(cbind(y, n-y) ~ gen + (1|block), ## data=gotway.hessianfly, family=binomial, ## control=glmerControl(check.nlev.gtreq.5="ignore")) ## gotway_hessianfly_prof <- profile(gotway_hessianfly_fit,which=1) ## save(list=ls(pattern="gotway"),file="gotway_hessianfly.rda") ## expect_equal(confint(gotway_hessianfly_prof)[1,1],0) ## FIXME: should add tests for {-1,1} bounds on correlations as well }) context("refit") test_that("refit", { s1 <- simulate(fm1) expect_is(refit(fm1,s1),"merMod") s2 <- simulate(fm1,2) expect_error(refit(fm1,s2),"refit not implemented for lists") }) lme4/inst/tests/test-glmer.Rout0000644000176000001440000001403612203607156016226 0ustar ripleyusers R Under development (unstable) (2013-08-16 r63597) -- "Unsuffered Consequences" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("testthat") > library("lme4") Loading required package: lattice Loading required package: Matrix > > context("fitting glmer models") > test_that("glmer", { + expect_warning(glmer(z~ 1|f, family=binomial, method="abc"),"Use the nAGQ argument") + expect_warning(glmer(z~ 1|f, family=binomial, method="Laplace"),"Use the nAGQ argument") + expect_warning(glmer(z~ 1|f, sparseX=TRUE),"has no effect at present") + expect_that(gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), + data = cbpp, family = binomial), is_a("glmerMod")) + expect_that(gm1@resp, is_a("glmResp")) + expect_that(gm1@pp, is_a("merPredD")) + expect_equal(ge1 <- unname(fixef(gm1)), c(-1.39854982537216, -0.992335519118859, + -1.12867532780426, -1.58030423764517), + tol=5e-4) + expect_equal(c(VarCorr(gm1)[[1]]), 0.41245527438386, tol=6e-4) + ### expect_that(family(gm1), equals(binomial())) + ### ?? binomial() has an 'initialize' component ... and the order is different + expect_equal(deviance(gm1), 184.052674598026, tol=1e-5) + expect_equal(sigma(gm1), 1) + expect_equal(extractAIC(gm1), c(5, 194.052674598026), tol=1e-5) + + expect_equal(theta <- unname(getME(gm1, "theta")), 0.642226809144453, tol=6e-4) + ###expect_that(X <- getME(gm1, "X"), is_equivalent_to(array(1, c(1, 30)))) + expect_that(Zt <- getME(gm1, "Zt"), is_a("dgCMatrix")) + expect_equal(dim(Zt), c(15L, 56L)) + expect_equal(length(Zt@x), 56L) + expect_equal(Zt@x, rep.int(1, 56L)) + expect_that(Lambdat <- getME(gm1, "Lambdat"), is_a("dgCMatrix")) + expect_equivalent(as(Lambdat, "matrix"), diag(theta, 15L, 15L)) + expect_error(glFormula(cbind(incidence, size - incidence) ~ period + (1 | herd), + data = subset(cbpp, herd==levels(herd)[1]), family = binomial), + "must have > 1") + expect_warning(glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), + data = subset(cbpp, herd %in% levels(herd)[1:4]), + family = binomial, + control=glmerControl(check.nlev.gtreq.5="warning")), + "< 5 sampled levels") + expect_warning(fm1. <- glmer(Reaction ~ Days + (Days|Subject), sleepstudy), + regexp="calling .* with family=gaussian .* as a shortcut") + options(warn=2) + cbppX <- transform(cbpp,prop=incidence/size) + expect_is(glmer(prop ~ period + (1 | herd), + data = cbppX, family = binomial, weights=size), "glmerMod") + expect_is(glmer(prop ~ period + (1 | herd), + data = cbppX, family = binomial, weights=size, start=NULL), + "glmerMod") + expect_is(glmer(prop ~ period + (1 | herd), + data = cbppX, family = binomial, weights=size, verbose=0L), + "glmerMod") + expect_is(glmer(prop ~ period + (1 | herd), + data = cbppX, family = binomial, weights=size, subset=TRUE), + "glmerMod") + expect_is(glmer(prop ~ period + (1 | herd), + data = cbppX, family = binomial, weights=size, na.action="na.exclude"), + "glmerMod") + expect_is(glmer(prop ~ period + (1 | herd), + data = cbppX, family = binomial, weights=size, offset=rep(0,nrow(cbppX))), + "glmerMod") + expect_is(glmer(prop ~ period + (1 | herd), + data = cbppX, family = binomial, weights=size, contrasts=NULL), + "glmerMod") + expect_is(glmer(prop ~ period + (1 | herd), + data = cbppX, family = binomial, weights=size, devFunOnly=FALSE), + "glmerMod") + expect_is(glmer(prop ~ period + (1 | herd), + data = cbppX, family = binomial, weights=size, + control=glmerControl(optimizer="Nelder_Mead")), + "glmerMod") + expect_is(glmer(prop ~ period + (1 | herd), + data = cbppX, family = binomial, weights=size, control=glmerControl()), + "glmerMod") + options(warn=0) + expect_warning(glmer(prop ~ period + (1 | herd), + data = cbppX, family = binomial, weights=size, junkArg=TRUE), + "extra argument.*disregarded") + expect_warning(glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), + data = cbpp, family = binomial, + control=list()), + "instead of passing a list of class") + expect_warning(glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), + data = cbpp, family = binomial, + control=lmerControl()), + "instead of passing a list of class") + + }) > > > proc.time() user system elapsed 8.220 3.216 11.787 lme4/inst/NEWS.Rd0000644000176000001440000003604212232467522013175 0ustar ripleyusers\newcommand{\PR}{\Sexpr[results=rd]{tools:::Rd_expr_PR(#1)}} \name{NEWS} \title{lme4 News} \encoding{UTF-8} \section{CHANGES IN VERSION 1.0-5 (2013-10-24)}{ \subsection{USER-VISIBLE CHANGES}{ \itemize{ \item \code{confint.merMod} and \code{vcov.merMod} are now exported, for downstream package-author convenience \item the package now depends on Matrix >=1.1-0 and RcppEigen >=0.3.1.2.3 \item new \code{rename.response} option for \code{refit} (see BUG FIXES section) } } \subsection{BUG FIXES}{ \itemize{ \item eliminated redundant messages about suppressed fixed-effect correlation matrices when p>20 \item most inverse-link functions are now bounded where appropriate by \code{.Machine$double.eps}, allowing fitting of GLMMs with extreme parameter values \item \code{merMod} objects created with \code{refit} did not work with \code{update}: optional \code{rename.response} option added to \code{refit.merMod}, to allow this (but the default is still \code{FALSE}, for back-compatibility (reported by A. Kuznetsova) \item fixed buglet preventing on-the-fly creation of index variables, e.g. \code{y~1+(1|rownames(data))} (reported by J. Dushoff) \item \code{predict} now works properly for \code{glmer} models with basis-creating terms (e.g. \code{poly}, \code{ns}) \item step sizes determined from fixed effect coefficient standard errors after first state of \code{glmer} fitting are now bounded, allowing some additional models to be fitted } } } \section{CHANGES IN VERSION 1.0-4 (2013-09-08)}{ \subsection{BUG FIXES}{ \itemize{ \item \code{refit()} now works, again, with lists of length 1, so that e.g. \code{refit(.,simulate(.))} works. (Reported by Gustaf Granath) \item \code{getME(.,"ST")} was returning a list containing the Cholesky factorizations that get repeated in Lambda. But this was inconsistent with what \code{ST} represents in \code{lme4.0}. This inconsistency has now been fixed and \code{getME(.,"ST")} is now consistent with the definition of the \code{ST} matrix in \code{lme4.0}. See \code{https://github.com/lme4/lme4/issues/111} for more detail. Thanks to Vince Dorie. \item Corrected order of unpacking of standard deviation/correlation components, which affected results from \code{confint(.,method="boot")}. (Reported by Reinhold Kliegl) \item fixed a copying bug that made \code{refitML()} modify the original model } } } \section{CHANGES IN VERSION 1.0-1 (2013-08-17)}{ \subsection{MINOR USER-VISIBLE CHANGES}{ \itemize{ \item \code{check.numobs.*} and \code{check.numlev.*} in \code{(g)lmerControl} have been changed (from recent development versions) to \code{check.nobs.*} and \code{check.nlev.*} respectively, and the default values of \code{check.nlev.gtreq.5} and \code{check.nobs.vs.rankZ} have been changed to \code{"ignore"} and \code{"warningSmall"} respectively \item in \code{(g)lmerControl}, arguments to the optimizer should be passed as a list called \code{optCtrl}, rather than specified as additional (ungrouped) arguments \item the \code{postVar} argument to \code{ranef} has been changed to the (more sensible) \code{condVar} ("posterior variance" was a misnomer, "conditional variance" -- short for "variance of the conditional mode" -- is preferred) \item the \code{REform} argument to \code{predict} has been changed to \code{ReForm} for consistency \item the \code{tnames} function, briefly exported, has been unexported \item \code{getME(.,"cnms")} added \item \code{print} method for \code{merMod} objects is now more terse, and different from \code{summary.merMod} \item the \code{objective} method for the \code{respMod} reference class now takes an optional \code{sigma.sq} parameter (defaulting to \code{NULL}) to allow calculation of the objective function with a residual variance different from the profiled value (Vince Dorie) } } } \section{CHANGES IN VERSION 1.0-0 (2013-08-01)}{ \subsection{MAJOR USER-VISIBLE CHANGES}{ \itemize{ \item Because the internal computational machinery has changed, results from the newest version of \code{lme4} will not be numerically identical to those from previous versions. For reasonably well- defined fits, they will be extremely close (within numerical tolerances of 1e-4 or so), but for unstable or poorly-defined fits the results may change, and very unstable fits may fail when they (apparently) succeeded with previous versions. Similarly, some fits may be slower with the new version, although on average the new version should be faster and more stable. More numerical tuning options are now available (see below); non-default settings may restore the speed and/or ability to fit a particular model without an error. If you notice significant or disturbing changes when fitting a model with the new version of \code{lme4}, \emph{please notify the maintainers}. \item \code{VarCorr} returns its results in the same format as before (as a list of variance-covariance matrices with \code{correlation} and \code{stddev} attributes, plus a \code{sc} attribute giving the residual standard deviation/scale parameter when appropriate), but prints them in a different (nicer) way. \item By default \code{residuals} gives deviance (rather than Pearson) residuals when applied to \code{glmer} fits (a side effect of matching \code{glm} behaviour more closely). \item As another side effect of matching \code{\link{glm}} behaviour, reported log-likelihoods from \code{\link{glmer}} models are no longer consistent with those from pre-1.0 \code{lme4}, but \emph{are} consistent with \code{glm}; see \code{\link{glmer}} examples. } } \subsection{MINOR USER-VISIBLE CHANGES}{ \itemize{ \item More use is made of S3 rather than S4 classes and methods: one side effect is that the \code{nlme} and \code{lme4} packages are now much more compatible; methods such as \code{fixef} no longer conflict. \item The internal optimizer has changed. \code{[gn]lmer} now has an \code{optimizer} argument; \code{"Nelder_Mead"} is the default for \code{[n]lmer}, while a combination of \code{"bobyqa"} (an alternative derivative-free method) and \code{"Nelder_Mead"} is the default for \code{glmer}. To use the \code{nlminb} optimizer as in the old version of \code{lme4}, you can use \code{optimizer="optimx"} with \code{control=list(method="nlminb")} (you will need the \code{optimx} package to be installed and loaded). See \code{\link{lmerControl}} for details. \item Families in GLMMs are no longer restricted to built-in/hard- coded families; any family described in \code{\link{family}}, or following that design, is usable (although there are some hard-coded families, which will be faster). \item \code{[gn]lmer} now produces objects of class \code{merMod} rather than class \code{mer} as before. \item the structure of the \code{Zt} (transposed random effect design matrix) as returned by \code{getME(.,"Zt")}, and the corresponding order of the random effects vector (\code{getME(.,"u")}) have changed. To retrieve \code{Zt} in the old format, use \code{do.call(Matrix::rBind,getME(.,"Ztlist"))}. \item the package checks input more thoroughly for non-identifiable or otherwise problematic cases: see \code{\link{lmerControl}} for fine control of the test behaviour. } } \subsection{NEW FEATURES}{ \itemize{ \item A general-purpose \code{\link{getME}} accessor method allows extraction of a wide variety of components of a mixed-model fit. \code{getME} also allows a vector of objects to be returned as a list of mixed-model componenets. This has been backported to be compatible with older versions of \code{lme4} that still produce \code{mer} objects rather than \code{merMod} objects. However, backporting is incomplete; some objects are only extractable in newer versions of \code{lme4}. \item Optimization information (convergence codes, warnings, etc.) is now stored in an \code{@optinfo} slot. \item \code{\link{bootMer}} provides a framework for obtaining parameter confidence intervals by parametric bootstrapping. \item \code{\link{plot.merMod}} provides diagnostic plotting methods similar to those from the \code{nlme} package (although missing \code{augPred}). \item A \code{\link{predict.merMod}} method gives predictions; it allows an effect-specific choice of conditional prediction or prediction at the population level (i.e., with random effects set to zero). \item Likelihood profiling for \code{lmer} and \code{glmer} results (see \code{link{profile-methods}}). \item Confidence intervals by likelihood profiling (default), parametric bootstrap, or Wald approximation (fixed effects only): see \code{\link{confint.merMod}} \item \code{nAGQ=0}, an option to do fast (but inaccurate) fitting of GLMMs. \item Using \code{devFunOnly=TRUE} allows the user to extract a deviance function for the model, allowing further diagnostics/customization of model results. \item The internal structure of [gn]lmer is now more modular, allowing finer control of the different steps of argument checking; construction of design matrices and data structures; parameter estimation; and construction of the final \code{merMod} object (see \code{?modular}). \item the \code{formula}, \code{model.frame}, and \code{terms} methods return full versions (including random effect terms and input variables) by default, but a \code{fixed.only} argument allows access to the fixed effect submodel. } } \subsection{EXPERIMENTAL FEATURES}{ \itemize{ \item \code{\link{glmer.nb}} provides an embryonic negative binomial fitting capability. } } \subsection{STILL NON-EXISTENT FEATURES}{ \itemize{ \item Adaptive Gaussian quadrature (AGQ) is not available for multiple and/or non-scalar random effects. \item Posterior variances of conditional models for non-scalar random effects. \item Standard errors for \code{\link{predict.merMod}} results. \item Automatic MCMC sampling based on the fit turns out to be very difficult to implement in a way that is really broadly reliable and robust; \code{mcmcsamp} will not be implemented in the near future. See \code{\link{pvalues}} for alternatives. \item "R-side" structures (within-block correlation and heteroscedasticity) are not on the current timetable. } } \subsection{BUG FIXES}{ \itemize{ \item In a development version, prior weights were not being used properly in the calculation of the residual standard deviation, but this has been fixed. Thanks to Simon Wood for pointing this out. \item In a development version, the step-halving component of the penalized iteratively reweighted least squares algorithm was not working, but this is now fixed. \item In a development version, square \code{RZX} matrices would lead to a \code{pwrssUpdate did not converge in 30 iterations} error. This has been fixed by adding an extra column of zeros to \code{RZX}. } } \subsection{DEPRECATED AND DEFUNCT}{ \itemize{ \item Previous versions of \code{lme4} provided the \code{mcmcsamp} function, which efficiently generated a Markov chain Monte Carlo sample from the posterior distribution of the parameters, assuming flat (scaled likelihood) priors. Due to difficulty in constructing a version of \code{mcmcsamp} that was reliable even in cases where the estimated random effect variances were near zero (e.g. \url{https://stat.ethz.ch/pipermail/r-sig-mixed-models/2009q4/003115.html}), \code{mcmcsamp} has been withdrawn (or more precisely, not updated to work with \code{lme4} versions >=1.0). \item Calling \code{glmer} with the default \code{gaussian} family redirects to \code{lmer}, but this is deprecated (in the future \code{glmer(...,family="gaussian")} may fit a LMM using the penalized iteratively reweighted least squares algorithm). Please call \code{lmer} directly. \item Calling \code{lmer} with a \code{family} argument redirects to \code{glmer}; this is deprecated. Please call \code{glmer} directly. } } } \section{CHANGES IN VERSION 0.999375-16 (2008-06-23)}{ \subsection{MAJOR USER-VISIBLE CHANGES}{ \itemize{ \item The underlying algorithms and representations for all the mixed-effects models fit by this package have changed - for the better, we hope. The class "mer" is a common mixed-effects model representation for linear, generalized linear, nonlinear and generalized nonlinear mixed-effects models. \item ECME iterations are no longer used at all, nor are analytic gradients. Components named 'niterEM', 'EMverbose', or 'gradient' can be included in the 'control' argument to lmer(), glmer() or nlmer() but have no effect. \item PQL iterations are no longer used in glmer() and nlmer(). Only the Laplace approximation is currently available. AGQ, for certain classes of GLMMs or NLMMs, is being added. \item The 'method' argument to lmer(), glmer() or nlmer() is deprecated. Use the 'REML = FALSE' in lmer() to obtain ML estimates. Selection of AGQ in glmer() and nlmer() will be controlled by the argument 'nAGQ', when completed. } } \subsection{NEW FEATURES}{ \itemize{ \item The representation of mixed-effects models has been dramatically changed to allow for smooth evaluation of the objective as the variance-covariance matrices for the random effects approach singularity. Beta testers found this representation to be more robust and usually faster than previous versions of lme4. \item The mcmcsamp function uses a new sampling method for the variance-covariance parameters that allows recovery from singularity. The update is not based on a sample from the Wishart distribution. It uses a redundant parameter representation and a linear least squares update. \item CAUTION: Currently the results from mcmcsamp look peculiar and are probably incorrect. I hope it is just a matter of my omitting a scaling factor but I have seen patterns such as the parameter estimate for some variance-covariance parameters being the maximum value in the chain, which is highly unlikely. \item The 'verbose' argument to lmer(), glmer() and nlmer() can be used instead of 'control = list(msVerbose = TRUE)'. } } } lme4/inst/doc/0000755000176000001440000000000012232520164012662 5ustar ripleyuserslme4/inst/doc/lme4-extras.pdf0000644000176000001440000164066312156422372015551 0ustar ripleyusers%PDF-1.4 %ÐÔÅØ 1 0 obj << /S /GoTo /D (section.1) >> endobj 4 0 obj (To do) endobj 5 0 obj << /S /GoTo /D (section.2) >> endobj 8 0 obj (Fit basic models) endobj 9 0 obj << /S /GoTo /D (section.3) >> endobj 12 0 obj (Quadratic confidence intervals on random effects parameters) endobj 13 0 obj << /S /GoTo /D (section.4) >> endobj 16 0 obj (Approximate confidence intervals on predictions) endobj 17 0 obj << /S /GoTo /D (section.5) >> endobj 20 0 obj (Poor man's MCMC) endobj 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effective than MCMC and that it has better theoretical justification than approximating degrees of freedom for t-tests and t-based intervals on the fixed effects. For the fixed-effects parameters the profiling is straightforward because they are coefficients in the linear predictor. The interesting thing about the examples is that the change in the deviance (the likelihood ratio test statistic) does not follow exactly a chisquare-1 pattern. The profile z (signed square root of the likelihood ratio test statistic) indicates wider intervals than would be determined from the standard error. In other words, the distribution appears to be overdispersed relative to the normal distribution, in the manner in which a T statistic is overdispersed. Another characteristics of the profile z for the fixed-effects is that the slope of the profile z at the parameter estimate is shallower than would be indicated by the standard error, especially the standard error calculated from the ML results. I attribute this phenomenon to an anti-conservative calculation of the standard error. It is being calculated conditional on the estimated value of the theta parameters. If instead you re-fit the model you end up with intervals on the fixed-effects parameters that more properly reflects the variability in all the parameters in the model. I think this will be very effective. The current profile method for class lmerenv profiles the fixed-effects parameters and I have a function for plotting these profiles. They can also be used to create confidence intervals and to create the p-values that everyone finds so important (although only for hypotheses related to a single coefficient). I have been working on profiling the other parameters in the model as well and this is where things get tricky. The parameters in the optimization are the theta parameters, which I don't think are directly of interest to most users. I can switch to theta and sigma at the risk of some redundancy (once you have done the work needed to calculate the profiled deviance for theta and sigma you could just as easily profile out sigma). The (unexported) R function devfun in lme4 takes a fitted model and returns a function with attributes that evaluates the profiled deviance for the composite parameter (theta, log(sigma)). However, I don't think even that is enough because it does not give profiles of the variances and covariances of the random effects directly. I think I will be better off writing a function that takes the variances, covariances (or, better, correlations) and log(sigma) to evaluate the profiled deviance. The reason that I use log(sigma) but not log of the variances of the random effects is because sigma cannot be zero except for constructed data whereas the estimates of the variance of the random effects can reasonably be zero. I need to get out to the bus stop soon so this next part might be a little too terse. It is straightforward to take the theta values and the sigma value and produce variances and covariances. That is done in VarCorr. The part that still has me pondering is how to go the other way reasonably. That is, how do I take the variances and covariances of the random effects plus sigma and get the theta parameters. Anyway, that is what I am currently contemplating. October 27, 2009 I did some more work on profiling the variance-covariance parameters in a linear mixed-effects model and now have a version I can check in. The current code is still pretty rough but it should be enough to start. There is a lot more that can be done more artistically about choosing step sizes and adapting the step sizes according to the current results but this should be enough to get going. I have been thinking about the kind of structure that should be returned by the profile method. For profile.nls the structure is a list with elements corresponding to the parameters that were profiled. Each element is a data frame containing a vector called tau and a matrix of parameter values. I'm going to stay with that for the time being although I will change the name from tau to zeta (it is the nonlinear analogue of a standard normal, not a t statistic). Eventually this object should be reconciled with the profile objects in the stats4 package. It makes sense for these to be an S4 class. lme4/inst/doc/Doxyfile0000644000176000001440000017762012156422372014414 0ustar ripleyusers# Doxyfile 1.6.3 # This file describes the settings to be used by the documentation system # doxygen (www.doxygen.org) for a project # # All text after a hash (#) is considered a comment and will be ignored # The format is: # TAG = value [value, ...] # For lists items can also be appended using: # TAG += value [value, ...] # Values that contain spaces should be placed between quotes (" ") #--------------------------------------------------------------------------- # Project related configuration options #--------------------------------------------------------------------------- # This tag specifies the encoding used for all characters in the config file # that follow. The default is UTF-8 which is also the encoding used for all # text before the first occurrence of this tag. Doxygen uses libiconv (or the # iconv built into libc) for the transcoding. 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QT_AUTOBRIEF = NO # The MULTILINE_CPP_IS_BRIEF tag can be set to YES to make Doxygen # treat a multi-line C++ special comment block (i.e. a block of //! or /// # comments) as a brief description. This used to be the default behaviour. # The new default is to treat a multi-line C++ comment block as a detailed # description. Set this tag to YES if you prefer the old behaviour instead. MULTILINE_CPP_IS_BRIEF = NO # If the INHERIT_DOCS tag is set to YES (the default) then an undocumented # member inherits the documentation from any documented member that it # re-implements. INHERIT_DOCS = YES # If the SEPARATE_MEMBER_PAGES tag is set to YES, then doxygen will produce # a new page for each member. If set to NO, the documentation of a member will # be part of the file/class/namespace that contains it. SEPARATE_MEMBER_PAGES = NO # The TAB_SIZE tag can be used to set the number of spaces in a tab. # Doxygen uses this value to replace tabs by spaces in code fragments. TAB_SIZE = 8 # This tag can be used to specify a number of aliases that acts # as commands in the documentation. An alias has the form "name=value". # For example adding "sideeffect=\par Side Effects:\n" will allow you to # put the command \sideeffect (or @sideeffect) in the documentation, which # will result in a user-defined paragraph with heading "Side Effects:". # You can put \n's in the value part of an alias to insert newlines. ALIASES = # Set the OPTIMIZE_OUTPUT_FOR_C tag to YES if your project consists of C # sources only. Doxygen will then generate output that is more tailored for C. # For instance, some of the names that are used will be different. The list # of all members will be omitted, etc. OPTIMIZE_OUTPUT_FOR_C = YES # Set the OPTIMIZE_OUTPUT_JAVA tag to YES if your project consists of Java # sources only. Doxygen will then generate output that is more tailored for # Java. For instance, namespaces will be presented as packages, qualified # scopes will look different, etc. OPTIMIZE_OUTPUT_JAVA = NO # Set the OPTIMIZE_FOR_FORTRAN tag to YES if your project consists of Fortran # sources only. Doxygen will then generate output that is more tailored for # Fortran. OPTIMIZE_FOR_FORTRAN = NO # Set the OPTIMIZE_OUTPUT_VHDL tag to YES if your project consists of VHDL # sources. Doxygen will then generate output that is tailored for # VHDL. OPTIMIZE_OUTPUT_VHDL = NO # Doxygen selects the parser to use depending on the extension of the files it parses. # With this tag you can assign which parser to use for a given extension. # Doxygen has a built-in mapping, but you can override or extend it using this tag. # The format is ext=language, where ext is a file extension, and language is one of # the parsers supported by doxygen: IDL, Java, Javascript, C#, C, C++, D, PHP, # Objective-C, Python, Fortran, VHDL, C, C++. For instance to make doxygen treat # .inc files as Fortran files (default is PHP), and .f files as C (default is Fortran), # use: inc=Fortran f=C. Note that for custom extensions you also need to set FILE_PATTERNS otherwise the files are not read by doxygen. EXTENSION_MAPPING = # If you use STL classes (i.e. std::string, std::vector, etc.) but do not want # to include (a tag file for) the STL sources as input, then you should # set this tag to YES in order to let doxygen match functions declarations and # definitions whose arguments contain STL classes (e.g. func(std::string); v.s. # func(std::string) {}). This also make the inheritance and collaboration # diagrams that involve STL classes more complete and accurate. BUILTIN_STL_SUPPORT = NO # If you use Microsoft's C++/CLI language, you should set this option to YES to # enable parsing support. CPP_CLI_SUPPORT = NO # Set the SIP_SUPPORT tag to YES if your project consists of sip sources only. # Doxygen will parse them like normal C++ but will assume all classes use public # instead of private inheritance when no explicit protection keyword is present. SIP_SUPPORT = NO # For Microsoft's IDL there are propget and propput attributes to indicate getter # and setter methods for a property. Setting this option to YES (the default) # will make doxygen to replace the get and set methods by a property in the # documentation. This will only work if the methods are indeed getting or # setting a simple type. If this is not the case, or you want to show the # methods anyway, you should set this option to NO. IDL_PROPERTY_SUPPORT = YES # If member grouping is used in the documentation and the DISTRIBUTE_GROUP_DOC # tag is set to YES, then doxygen will reuse the documentation of the first # member in the group (if any) for the other members of the group. By default # all members of a group must be documented explicitly. DISTRIBUTE_GROUP_DOC = NO # Set the SUBGROUPING tag to YES (the default) to allow class member groups of # the same type (for instance a group of public functions) to be put as a # subgroup of that type (e.g. under the Public Functions section). Set it to # NO to prevent subgrouping. Alternatively, this can be done per class using # the \nosubgrouping command. SUBGROUPING = YES # When TYPEDEF_HIDES_STRUCT is enabled, a typedef of a struct, union, or enum # is documented as struct, union, or enum with the name of the typedef. So # typedef struct TypeS {} TypeT, will appear in the documentation as a struct # with name TypeT. When disabled the typedef will appear as a member of a file, # namespace, or class. And the struct will be named TypeS. This can typically # be useful for C code in case the coding convention dictates that all compound # types are typedef'ed and only the typedef is referenced, never the tag name. TYPEDEF_HIDES_STRUCT = NO # The SYMBOL_CACHE_SIZE determines the size of the internal cache use to # determine which symbols to keep in memory and which to flush to disk. # When the cache is full, less often used symbols will be written to disk. # For small to medium size projects (<1000 input files) the default value is # probably good enough. For larger projects a too small cache size can cause # doxygen to be busy swapping symbols to and from disk most of the time # causing a significant performance penality. # If the system has enough physical memory increasing the cache will improve the # performance by keeping more symbols in memory. Note that the value works on # a logarithmic scale so increasing the size by one will rougly double the # memory usage. The cache size is given by this formula: # 2^(16+SYMBOL_CACHE_SIZE). The valid range is 0..9, the default is 0, # corresponding to a cache size of 2^16 = 65536 symbols SYMBOL_CACHE_SIZE = 0 #--------------------------------------------------------------------------- # Build related configuration options #--------------------------------------------------------------------------- # If the EXTRACT_ALL tag is set to YES doxygen will assume all entities in # documentation are documented, even if no documentation was available. # Private class members and static file members will be hidden unless # the EXTRACT_PRIVATE and EXTRACT_STATIC tags are set to YES EXTRACT_ALL = YES # If the EXTRACT_PRIVATE tag is set to YES all private members of a class # will be included in the documentation. EXTRACT_PRIVATE = YES # If the EXTRACT_STATIC tag is set to YES all static members of a file # will be included in the documentation. EXTRACT_STATIC = YES # If the EXTRACT_LOCAL_CLASSES tag is set to YES classes (and structs) # defined locally in source files will be included in the documentation. # If set to NO only classes defined in header files are included. EXTRACT_LOCAL_CLASSES = YES # This flag is only useful for Objective-C code. When set to YES local # methods, which are defined in the implementation section but not in # the interface are included in the documentation. # If set to NO (the default) only methods in the interface are included. EXTRACT_LOCAL_METHODS = NO # If this flag is set to YES, the members of anonymous namespaces will be # extracted and appear in the documentation as a namespace called # 'anonymous_namespace{file}', where file will be replaced with the base # name of the file that contains the anonymous namespace. By default # anonymous namespace are hidden. EXTRACT_ANON_NSPACES = NO # If the HIDE_UNDOC_MEMBERS tag is set to YES, Doxygen will hide all # undocumented members of documented classes, files or namespaces. # If set to NO (the default) these members will be included in the # various overviews, but no documentation section is generated. # This option has no effect if EXTRACT_ALL is enabled. HIDE_UNDOC_MEMBERS = NO # If the HIDE_UNDOC_CLASSES tag is set to YES, Doxygen will hide all # undocumented classes that are normally visible in the class hierarchy. # If set to NO (the default) these classes will be included in the various # overviews. This option has no effect if EXTRACT_ALL is enabled. HIDE_UNDOC_CLASSES = NO # If the HIDE_FRIEND_COMPOUNDS tag is set to YES, Doxygen will hide all # friend (class|struct|union) declarations. # If set to NO (the default) these declarations will be included in the # documentation. HIDE_FRIEND_COMPOUNDS = NO # If the HIDE_IN_BODY_DOCS tag is set to YES, Doxygen will hide any # documentation blocks found inside the body of a function. # If set to NO (the default) these blocks will be appended to the # function's detailed documentation block. HIDE_IN_BODY_DOCS = NO # The INTERNAL_DOCS tag determines if documentation # that is typed after a \internal command is included. If the tag is set # to NO (the default) then the documentation will be excluded. # Set it to YES to include the internal documentation. INTERNAL_DOCS = NO # If the CASE_SENSE_NAMES tag is set to NO then Doxygen will only generate # file names in lower-case letters. If set to YES upper-case letters are also # allowed. This is useful if you have classes or files whose names only differ # in case and if your file system supports case sensitive file names. Windows # and Mac users are advised to set this option to NO. CASE_SENSE_NAMES = YES # If the HIDE_SCOPE_NAMES tag is set to NO (the default) then Doxygen # will show members with their full class and namespace scopes in the # documentation. If set to YES the scope will be hidden. HIDE_SCOPE_NAMES = NO # If the SHOW_INCLUDE_FILES tag is set to YES (the default) then Doxygen # will put a list of the files that are included by a file in the documentation # of that file. SHOW_INCLUDE_FILES = YES # If the FORCE_LOCAL_INCLUDES tag is set to YES then Doxygen # will list include files with double quotes in the documentation # rather than with sharp brackets. FORCE_LOCAL_INCLUDES = NO # If the INLINE_INFO tag is set to YES (the default) then a tag [inline] # is inserted in the documentation for inline members. INLINE_INFO = YES # If the SORT_MEMBER_DOCS tag is set to YES (the default) then doxygen # will sort the (detailed) documentation of file and class members # alphabetically by member name. If set to NO the members will appear in # declaration order. SORT_MEMBER_DOCS = YES # If the SORT_BRIEF_DOCS tag is set to YES then doxygen will sort the # brief documentation of file, namespace and class members alphabetically # by member name. If set to NO (the default) the members will appear in # declaration order. SORT_BRIEF_DOCS = YES # If the SORT_MEMBERS_CTORS_1ST tag is set to YES then doxygen will sort the (brief and detailed) documentation of class members so that constructors and destructors are listed first. If set to NO (the default) the constructors will appear in the respective orders defined by SORT_MEMBER_DOCS and SORT_BRIEF_DOCS. This tag will be ignored for brief docs if SORT_BRIEF_DOCS is set to NO and ignored for detailed docs if SORT_MEMBER_DOCS is set to NO. SORT_MEMBERS_CTORS_1ST = NO # If the SORT_GROUP_NAMES tag is set to YES then doxygen will sort the # hierarchy of group names into alphabetical order. If set to NO (the default) # the group names will appear in their defined order. SORT_GROUP_NAMES = NO # If the SORT_BY_SCOPE_NAME tag is set to YES, the class list will be # sorted by fully-qualified names, including namespaces. If set to # NO (the default), the class list will be sorted only by class name, # not including the namespace part. # Note: This option is not very useful if HIDE_SCOPE_NAMES is set to YES. # Note: This option applies only to the class list, not to the # alphabetical list. SORT_BY_SCOPE_NAME = NO # The GENERATE_TODOLIST tag can be used to enable (YES) or # disable (NO) the todo list. This list is created by putting \todo # commands in the documentation. GENERATE_TODOLIST = YES # The GENERATE_TESTLIST tag can be used to enable (YES) or # disable (NO) the test list. This list is created by putting \test # commands in the documentation. GENERATE_TESTLIST = YES # The GENERATE_BUGLIST tag can be used to enable (YES) or # disable (NO) the bug list. This list is created by putting \bug # commands in the documentation. GENERATE_BUGLIST = YES # The GENERATE_DEPRECATEDLIST tag can be used to enable (YES) or # disable (NO) the deprecated list. This list is created by putting # \deprecated commands in the documentation. GENERATE_DEPRECATEDLIST= YES # The ENABLED_SECTIONS tag can be used to enable conditional # documentation sections, marked by \if sectionname ... \endif. ENABLED_SECTIONS = # The MAX_INITIALIZER_LINES tag determines the maximum number of lines # the initial value of a variable or define consists of for it to appear in # the documentation. If the initializer consists of more lines than specified # here it will be hidden. Use a value of 0 to hide initializers completely. # The appearance of the initializer of individual variables and defines in the # documentation can be controlled using \showinitializer or \hideinitializer # command in the documentation regardless of this setting. MAX_INITIALIZER_LINES = 30 # Set the SHOW_USED_FILES tag to NO to disable the list of files generated # at the bottom of the documentation of classes and structs. If set to YES the # list will mention the files that were used to generate the documentation. SHOW_USED_FILES = YES # If the sources in your project are distributed over multiple directories # then setting the SHOW_DIRECTORIES tag to YES will show the directory hierarchy # in the documentation. The default is NO. SHOW_DIRECTORIES = NO # Set the SHOW_FILES tag to NO to disable the generation of the Files page. # This will remove the Files entry from the Quick Index and from the # Folder Tree View (if specified). The default is YES. SHOW_FILES = YES # Set the SHOW_NAMESPACES tag to NO to disable the generation of the # Namespaces page. # This will remove the Namespaces entry from the Quick Index # and from the Folder Tree View (if specified). The default is YES. SHOW_NAMESPACES = YES # The FILE_VERSION_FILTER tag can be used to specify a program or script that # doxygen should invoke to get the current version for each file (typically from # the version control system). Doxygen will invoke the program by executing (via # popen()) the command , where is the value of # the FILE_VERSION_FILTER tag, and is the name of an input file # provided by doxygen. Whatever the program writes to standard output # is used as the file version. See the manual for examples. FILE_VERSION_FILTER = # The LAYOUT_FILE tag can be used to specify a layout file which will be parsed by # doxygen. The layout file controls the global structure of the generated output files # in an output format independent way. The create the layout file that represents # doxygen's defaults, run doxygen with the -l option. You can optionally specify a # file name after the option, if omitted DoxygenLayout.xml will be used as the name # of the layout file. LAYOUT_FILE = #--------------------------------------------------------------------------- # configuration options related to warning and progress messages #--------------------------------------------------------------------------- # The QUIET tag can be used to turn on/off the messages that are generated # by doxygen. Possible values are YES and NO. If left blank NO is used. QUIET = NO # The WARNINGS tag can be used to turn on/off the warning messages that are # generated by doxygen. Possible values are YES and NO. If left blank # NO is used. WARNINGS = YES # If WARN_IF_UNDOCUMENTED is set to YES, then doxygen will generate warnings # for undocumented members. If EXTRACT_ALL is set to YES then this flag will # automatically be disabled. WARN_IF_UNDOCUMENTED = YES # If WARN_IF_DOC_ERROR is set to YES, doxygen will generate warnings for # potential errors in the documentation, such as not documenting some # parameters in a documented function, or documenting parameters that # don't exist or using markup commands wrongly. WARN_IF_DOC_ERROR = YES # This WARN_NO_PARAMDOC option can be abled to get warnings for # functions that are documented, but have no documentation for their parameters # or return value. If set to NO (the default) doxygen will only warn about # wrong or incomplete parameter documentation, but not about the absence of # documentation. WARN_NO_PARAMDOC = NO # The WARN_FORMAT tag determines the format of the warning messages that # doxygen can produce. The string should contain the $file, $line, and $text # tags, which will be replaced by the file and line number from which the # warning originated and the warning text. Optionally the format may contain # $version, which will be replaced by the version of the file (if it could # be obtained via FILE_VERSION_FILTER) WARN_FORMAT = "$file:$line: $text " # The WARN_LOGFILE tag can be used to specify a file to which warning # and error messages should be written. If left blank the output is written # to stderr. WARN_LOGFILE = #--------------------------------------------------------------------------- # configuration options related to the input files #--------------------------------------------------------------------------- # The INPUT tag can be used to specify the files and/or directories that contain # documented source files. You may enter file names like "myfile.cpp" or # directories like "/usr/src/myproject". Separate the files or directories # with spaces. INPUT = ../../src # This tag can be used to specify the character encoding of the source files # that doxygen parses. Internally doxygen uses the UTF-8 encoding, which is # also the default input encoding. Doxygen uses libiconv (or the iconv built # into libc) for the transcoding. See http://www.gnu.org/software/libiconv for # the list of possible encodings. INPUT_ENCODING = UTF-8 # If the value of the INPUT tag contains directories, you can use the # FILE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp # and *.h) to filter out the source-files in the directories. If left # blank the following patterns are tested: # *.c *.cc *.cxx *.cpp *.c++ *.java *.ii *.ixx *.ipp *.i++ *.inl *.h *.hh *.hxx # *.hpp *.h++ *.idl *.odl *.cs *.php *.php3 *.inc *.m *.mm *.py *.f90 FILE_PATTERNS = # The RECURSIVE tag can be used to turn specify whether or not subdirectories # should be searched for input files as well. Possible values are YES and NO. # If left blank NO is used. RECURSIVE = NO # The EXCLUDE tag can be used to specify files and/or directories that should # excluded from the INPUT source files. This way you can easily exclude a # subdirectory from a directory tree whose root is specified with the INPUT tag. EXCLUDE = # The EXCLUDE_SYMLINKS tag can be used select whether or not files or # directories that are symbolic links (a Unix filesystem feature) are excluded # from the input. EXCLUDE_SYMLINKS = NO # If the value of the INPUT tag contains directories, you can use the # EXCLUDE_PATTERNS tag to specify one or more wildcard patterns to exclude # certain files from those directories. Note that the wildcards are matched # against the file with absolute path, so to exclude all test directories # for example use the pattern */test/* EXCLUDE_PATTERNS = # The EXCLUDE_SYMBOLS tag can be used to specify one or more symbol names # (namespaces, classes, functions, etc.) that should be excluded from the # output. The symbol name can be a fully qualified name, a word, or if the # wildcard * is used, a substring. Examples: ANamespace, AClass, # AClass::ANamespace, ANamespace::*Test EXCLUDE_SYMBOLS = # The EXAMPLE_PATH tag can be used to specify one or more files or # directories that contain example code fragments that are included (see # the \include command). EXAMPLE_PATH = # If the value of the EXAMPLE_PATH tag contains directories, you can use the # EXAMPLE_PATTERNS tag to specify one or more wildcard pattern (like *.cpp # and *.h) to filter out the source-files in the directories. If left # blank all files are included. EXAMPLE_PATTERNS = # If the EXAMPLE_RECURSIVE tag is set to YES then subdirectories will be # searched for input files to be used with the \include or \dontinclude # commands irrespective of the value of the RECURSIVE tag. # Possible values are YES and NO. If left blank NO is used. EXAMPLE_RECURSIVE = NO # The IMAGE_PATH tag can be used to specify one or more files or # directories that contain image that are included in the documentation (see # the \image command). IMAGE_PATH = # The INPUT_FILTER tag can be used to specify a program that doxygen should # invoke to filter for each input file. Doxygen will invoke the filter program # by executing (via popen()) the command , where # is the value of the INPUT_FILTER tag, and is the name of an # input file. Doxygen will then use the output that the filter program writes # to standard output. # If FILTER_PATTERNS is specified, this tag will be # ignored. INPUT_FILTER = # The FILTER_PATTERNS tag can be used to specify filters on a per file pattern # basis. # Doxygen will compare the file name with each pattern and apply the # filter if there is a match. # The filters are a list of the form: # pattern=filter (like *.cpp=my_cpp_filter). See INPUT_FILTER for further # info on how filters are used. If FILTER_PATTERNS is empty, INPUT_FILTER # is applied to all files. FILTER_PATTERNS = # If the FILTER_SOURCE_FILES tag is set to YES, the input filter (if set using # INPUT_FILTER) will be used to filter the input files when producing source # files to browse (i.e. when SOURCE_BROWSER is set to YES). FILTER_SOURCE_FILES = NO #--------------------------------------------------------------------------- # configuration options related to source browsing #--------------------------------------------------------------------------- # If the SOURCE_BROWSER tag is set to YES then a list of source files will # be generated. Documented entities will be cross-referenced with these sources. # Note: To get rid of all source code in the generated output, make sure also # VERBATIM_HEADERS is set to NO. SOURCE_BROWSER = YES # Setting the INLINE_SOURCES tag to YES will include the body # of functions and classes directly in the documentation. INLINE_SOURCES = NO # Setting the STRIP_CODE_COMMENTS tag to YES (the default) will instruct # doxygen to hide any special comment blocks from generated source code # fragments. Normal C and C++ comments will always remain visible. STRIP_CODE_COMMENTS = YES # If the REFERENCED_BY_RELATION tag is set to YES # then for each documented function all documented # functions referencing it will be listed. REFERENCED_BY_RELATION = YES # If the REFERENCES_RELATION tag is set to YES # then for each documented function all documented entities # called/used by that function will be listed. REFERENCES_RELATION = YES # If the REFERENCES_LINK_SOURCE tag is set to YES (the default) # and SOURCE_BROWSER tag is set to YES, then the hyperlinks from # functions in REFERENCES_RELATION and REFERENCED_BY_RELATION lists will # link to the source code. # Otherwise they will link to the documentation. REFERENCES_LINK_SOURCE = YES # If the USE_HTAGS tag is set to YES then the references to source code # will point to the HTML generated by the htags(1) tool instead of doxygen # built-in source browser. The htags tool is part of GNU's global source # tagging system (see http://www.gnu.org/software/global/global.html). You # will need version 4.8.6 or higher. USE_HTAGS = NO # If the VERBATIM_HEADERS tag is set to YES (the default) then Doxygen # will generate a verbatim copy of the header file for each class for # which an include is specified. Set to NO to disable this. VERBATIM_HEADERS = YES #--------------------------------------------------------------------------- # configuration options related to the alphabetical class index #--------------------------------------------------------------------------- # If the ALPHABETICAL_INDEX tag is set to YES, an alphabetical index # of all compounds will be generated. Enable this if the project # contains a lot of classes, structs, unions or interfaces. ALPHABETICAL_INDEX = YES # If the alphabetical index is enabled (see ALPHABETICAL_INDEX) then # the COLS_IN_ALPHA_INDEX tag can be used to specify the number of columns # in which this list will be split (can be a number in the range [1..20]) COLS_IN_ALPHA_INDEX = 5 # In case all classes in a project start with a common prefix, all # classes will be put under the same header in the alphabetical index. # The IGNORE_PREFIX tag can be used to specify one or more prefixes that # should be ignored while generating the index headers. IGNORE_PREFIX = #--------------------------------------------------------------------------- # configuration options related to the HTML output #--------------------------------------------------------------------------- # If the GENERATE_HTML tag is set to YES (the default) Doxygen will # generate HTML output. GENERATE_HTML = YES # The HTML_OUTPUT tag is used to specify where the HTML docs will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `html' will be used as the default path. HTML_OUTPUT = . # The HTML_FILE_EXTENSION tag can be used to specify the file extension for # each generated HTML page (for example: .htm,.php,.asp). If it is left blank # doxygen will generate files with .html extension. HTML_FILE_EXTENSION = .html # The HTML_HEADER tag can be used to specify a personal HTML header for # each generated HTML page. If it is left blank doxygen will generate a # standard header. HTML_HEADER = # The HTML_FOOTER tag can be used to specify a personal HTML footer for # each generated HTML page. If it is left blank doxygen will generate a # standard footer. HTML_FOOTER = # The HTML_STYLESHEET tag can be used to specify a user-defined cascading # style sheet that is used by each HTML page. It can be used to # fine-tune the look of the HTML output. If the tag is left blank doxygen # will generate a default style sheet. Note that doxygen will try to copy # the style sheet file to the HTML output directory, so don't put your own # stylesheet in the HTML output directory as well, or it will be erased! HTML_STYLESHEET = # If the HTML_TIMESTAMP tag is set to YES then the footer of each generated HTML # page will contain the date and time when the page was generated. Setting # this to NO can help when comparing the output of multiple runs. HTML_TIMESTAMP = YES # If the HTML_ALIGN_MEMBERS tag is set to YES, the members of classes, # files or namespaces will be aligned in HTML using tables. If set to # NO a bullet list will be used. HTML_ALIGN_MEMBERS = YES # If the HTML_DYNAMIC_SECTIONS tag is set to YES then the generated HTML # documentation will contain sections that can be hidden and shown after the # page has loaded. For this to work a browser that supports # JavaScript and DHTML is required (for instance Mozilla 1.0+, Firefox # Netscape 6.0+, Internet explorer 5.0+, Konqueror, or Safari). HTML_DYNAMIC_SECTIONS = NO # If the GENERATE_DOCSET tag is set to YES, additional index files # will be generated that can be used as input for Apple's Xcode 3 # integrated development environment, introduced with OSX 10.5 (Leopard). # To create a documentation set, doxygen will generate a Makefile in the # HTML output directory. Running make will produce the docset in that # directory and running "make install" will install the docset in # ~/Library/Developer/Shared/Documentation/DocSets so that Xcode will find # it at startup. # See http://developer.apple.com/tools/creatingdocsetswithdoxygen.html for more information. GENERATE_DOCSET = NO # When GENERATE_DOCSET tag is set to YES, this tag determines the name of the # feed. A documentation feed provides an umbrella under which multiple # documentation sets from a single provider (such as a company or product suite) # can be grouped. DOCSET_FEEDNAME = "Doxygen generated docs" # When GENERATE_DOCSET tag is set to YES, this tag specifies a string that # should uniquely identify the documentation set bundle. This should be a # reverse domain-name style string, e.g. com.mycompany.MyDocSet. Doxygen # will append .docset to the name. DOCSET_BUNDLE_ID = org.doxygen.Project # If the GENERATE_HTMLHELP tag is set to YES, additional index files # will be generated that can be used as input for tools like the # Microsoft HTML help workshop to generate a compiled HTML help file (.chm) # of the generated HTML documentation. GENERATE_HTMLHELP = NO # If the GENERATE_HTMLHELP tag is set to YES, the CHM_FILE tag can # be used to specify the file name of the resulting .chm file. You # can add a path in front of the file if the result should not be # written to the html output directory. CHM_FILE = # If the GENERATE_HTMLHELP tag is set to YES, the HHC_LOCATION tag can # be used to specify the location (absolute path including file name) of # the HTML help compiler (hhc.exe). If non-empty doxygen will try to run # the HTML help compiler on the generated index.hhp. HHC_LOCATION = # If the GENERATE_HTMLHELP tag is set to YES, the GENERATE_CHI flag # controls if a separate .chi index file is generated (YES) or that # it should be included in the master .chm file (NO). GENERATE_CHI = NO # If the GENERATE_HTMLHELP tag is set to YES, the CHM_INDEX_ENCODING # is used to encode HtmlHelp index (hhk), content (hhc) and project file # content. CHM_INDEX_ENCODING = # If the GENERATE_HTMLHELP tag is set to YES, the BINARY_TOC flag # controls whether a binary table of contents is generated (YES) or a # normal table of contents (NO) in the .chm file. BINARY_TOC = NO # The TOC_EXPAND flag can be set to YES to add extra items for group members # to the contents of the HTML help documentation and to the tree view. TOC_EXPAND = NO # If the GENERATE_QHP tag is set to YES and both QHP_NAMESPACE and QHP_VIRTUAL_FOLDER # are set, an additional index file will be generated that can be used as input for # Qt's qhelpgenerator to generate a Qt Compressed Help (.qch) of the generated # HTML documentation. GENERATE_QHP = NO # If the QHG_LOCATION tag is specified, the QCH_FILE tag can # be used to specify the file name of the resulting .qch file. # The path specified is relative to the HTML output folder. QCH_FILE = # The QHP_NAMESPACE tag specifies the namespace to use when generating # Qt Help Project output. For more information please see # http://doc.trolltech.com/qthelpproject.html#namespace QHP_NAMESPACE = # The QHP_VIRTUAL_FOLDER tag specifies the namespace to use when generating # Qt Help Project output. For more information please see # http://doc.trolltech.com/qthelpproject.html#virtual-folders QHP_VIRTUAL_FOLDER = doc # If QHP_CUST_FILTER_NAME is set, it specifies the name of a custom filter to add. # For more information please see # http://doc.trolltech.com/qthelpproject.html#custom-filters QHP_CUST_FILTER_NAME = # The QHP_CUST_FILT_ATTRS tag specifies the list of the attributes of the custom filter to add.For more information please see # Qt Help Project / Custom Filters. QHP_CUST_FILTER_ATTRS = # The QHP_SECT_FILTER_ATTRS tag specifies the list of the attributes this project's # filter section matches. # Qt Help Project / Filter Attributes. QHP_SECT_FILTER_ATTRS = # If the GENERATE_QHP tag is set to YES, the QHG_LOCATION tag can # be used to specify the location of Qt's qhelpgenerator. # If non-empty doxygen will try to run qhelpgenerator on the generated # .qhp file. QHG_LOCATION = # If the GENERATE_ECLIPSEHELP tag is set to YES, additional index files # will be generated, which together with the HTML files, form an Eclipse help # plugin. To install this plugin and make it available under the help contents # menu in Eclipse, the contents of the directory containing the HTML and XML # files needs to be copied into the plugins directory of eclipse. The name of # the directory within the plugins directory should be the same as # the ECLIPSE_DOC_ID value. After copying Eclipse needs to be restarted before the help appears. GENERATE_ECLIPSEHELP = NO # A unique identifier for the eclipse help plugin. When installing the plugin # the directory name containing the HTML and XML files should also have # this name. ECLIPSE_DOC_ID = org.doxygen.Project # The DISABLE_INDEX tag can be used to turn on/off the condensed index at # top of each HTML page. The value NO (the default) enables the index and # the value YES disables it. DISABLE_INDEX = NO # This tag can be used to set the number of enum values (range [1..20]) # that doxygen will group on one line in the generated HTML documentation. ENUM_VALUES_PER_LINE = 8 # The GENERATE_TREEVIEW tag is used to specify whether a tree-like index # structure should be generated to display hierarchical information. # If the tag value is set to YES, a side panel will be generated # containing a tree-like index structure (just like the one that # is generated for HTML Help). For this to work a browser that supports # JavaScript, DHTML, CSS and frames is required (i.e. any modern browser). # Windows users are probably better off using the HTML help feature. GENERATE_TREEVIEW = YES # By enabling USE_INLINE_TREES, doxygen will generate the Groups, Directories, # and Class Hierarchy pages using a tree view instead of an ordered list. USE_INLINE_TREES = NO # If the treeview is enabled (see GENERATE_TREEVIEW) then this tag can be # used to set the initial width (in pixels) of the frame in which the tree # is shown. TREEVIEW_WIDTH = 250 # Use this tag to change the font size of Latex formulas included # as images in the HTML documentation. The default is 10. Note that # when you change the font size after a successful doxygen run you need # to manually remove any form_*.png images from the HTML output directory # to force them to be regenerated. FORMULA_FONTSIZE = 10 # When the SEARCHENGINE tag is enabled doxygen will generate a search box for the HTML output. The underlying search engine uses javascript # and DHTML and should work on any modern browser. Note that when using HTML help (GENERATE_HTMLHELP), Qt help (GENERATE_QHP), or docsets (GENERATE_DOCSET) there is already a search function so this one should # typically be disabled. For large projects the javascript based search engine # can be slow, then enabling SERVER_BASED_SEARCH may provide a better solution. SEARCHENGINE = NO # When the SERVER_BASED_SEARCH tag is enabled the search engine will be implemented using a PHP enabled web server instead of at the web client using Javascript. Doxygen will generate the search PHP script and index # file to put on the web server. The advantage of the server based approach is that it scales better to large projects and allows full text search. The disadvances is that it is more difficult to setup # and does not have live searching capabilities. SERVER_BASED_SEARCH = NO #--------------------------------------------------------------------------- # configuration options related to the LaTeX output #--------------------------------------------------------------------------- # If the GENERATE_LATEX tag is set to YES (the default) Doxygen will # generate Latex output. GENERATE_LATEX = NO # The LATEX_OUTPUT tag is used to specify where the LaTeX docs will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `latex' will be used as the default path. LATEX_OUTPUT = latex # The LATEX_CMD_NAME tag can be used to specify the LaTeX command name to be # invoked. If left blank `latex' will be used as the default command name. # Note that when enabling USE_PDFLATEX this option is only used for # generating bitmaps for formulas in the HTML output, but not in the # Makefile that is written to the output directory. LATEX_CMD_NAME = latex # The MAKEINDEX_CMD_NAME tag can be used to specify the command name to # generate index for LaTeX. If left blank `makeindex' will be used as the # default command name. MAKEINDEX_CMD_NAME = makeindex # If the COMPACT_LATEX tag is set to YES Doxygen generates more compact # LaTeX documents. This may be useful for small projects and may help to # save some trees in general. COMPACT_LATEX = NO # The PAPER_TYPE tag can be used to set the paper type that is used # by the printer. Possible values are: a4, a4wide, letter, legal and # executive. If left blank a4wide will be used. PAPER_TYPE = letter # The EXTRA_PACKAGES tag can be to specify one or more names of LaTeX # packages that should be included in the LaTeX output. EXTRA_PACKAGES = # The LATEX_HEADER tag can be used to specify a personal LaTeX header for # the generated latex document. The header should contain everything until # the first chapter. If it is left blank doxygen will generate a # standard header. Notice: only use this tag if you know what you are doing! LATEX_HEADER = # If the PDF_HYPERLINKS tag is set to YES, the LaTeX that is generated # is prepared for conversion to pdf (using ps2pdf). The pdf file will # contain links (just like the HTML output) instead of page references # This makes the output suitable for online browsing using a pdf viewer. PDF_HYPERLINKS = NO # If the USE_PDFLATEX tag is set to YES, pdflatex will be used instead of # plain latex in the generated Makefile. Set this option to YES to get a # higher quality PDF documentation. USE_PDFLATEX = YES # If the LATEX_BATCHMODE tag is set to YES, doxygen will add the \\batchmode. # command to the generated LaTeX files. This will instruct LaTeX to keep # running if errors occur, instead of asking the user for help. # This option is also used when generating formulas in HTML. LATEX_BATCHMODE = YES # If LATEX_HIDE_INDICES is set to YES then doxygen will not # include the index chapters (such as File Index, Compound Index, etc.) # in the output. LATEX_HIDE_INDICES = NO # If LATEX_SOURCE_CODE is set to YES then doxygen will include source code with syntax highlighting in the LaTeX output. Note that which sources are shown also depends on other settings such as SOURCE_BROWSER. LATEX_SOURCE_CODE = NO #--------------------------------------------------------------------------- # configuration options related to the RTF output #--------------------------------------------------------------------------- # If the GENERATE_RTF tag is set to YES Doxygen will generate RTF output # The RTF output is optimized for Word 97 and may not look very pretty with # other RTF readers or editors. GENERATE_RTF = NO # The RTF_OUTPUT tag is used to specify where the RTF docs will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `rtf' will be used as the default path. RTF_OUTPUT = rtf # If the COMPACT_RTF tag is set to YES Doxygen generates more compact # RTF documents. This may be useful for small projects and may help to # save some trees in general. COMPACT_RTF = NO # If the RTF_HYPERLINKS tag is set to YES, the RTF that is generated # will contain hyperlink fields. The RTF file will # contain links (just like the HTML output) instead of page references. # This makes the output suitable for online browsing using WORD or other # programs which support those fields. # Note: wordpad (write) and others do not support links. RTF_HYPERLINKS = NO # Load stylesheet definitions from file. Syntax is similar to doxygen's # config file, i.e. a series of assignments. You only have to provide # replacements, missing definitions are set to their default value. RTF_STYLESHEET_FILE = # Set optional variables used in the generation of an rtf document. # Syntax is similar to doxygen's config file. RTF_EXTENSIONS_FILE = #--------------------------------------------------------------------------- # configuration options related to the man page output #--------------------------------------------------------------------------- # If the GENERATE_MAN tag is set to YES (the default) Doxygen will # generate man pages GENERATE_MAN = NO # The MAN_OUTPUT tag is used to specify where the man pages will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `man' will be used as the default path. MAN_OUTPUT = man # The MAN_EXTENSION tag determines the extension that is added to # the generated man pages (default is the subroutine's section .3) MAN_EXTENSION = .3 # If the MAN_LINKS tag is set to YES and Doxygen generates man output, # then it will generate one additional man file for each entity # documented in the real man page(s). These additional files # only source the real man page, but without them the man command # would be unable to find the correct page. The default is NO. MAN_LINKS = NO #--------------------------------------------------------------------------- # configuration options related to the XML output #--------------------------------------------------------------------------- # If the GENERATE_XML tag is set to YES Doxygen will # generate an XML file that captures the structure of # the code including all documentation. GENERATE_XML = NO # The XML_OUTPUT tag is used to specify where the XML pages will be put. # If a relative path is entered the value of OUTPUT_DIRECTORY will be # put in front of it. If left blank `xml' will be used as the default path. XML_OUTPUT = xml # The XML_SCHEMA tag can be used to specify an XML schema, # which can be used by a validating XML parser to check the # syntax of the XML files. XML_SCHEMA = # The XML_DTD tag can be used to specify an XML DTD, # which can be used by a validating XML parser to check the # syntax of the XML files. XML_DTD = # If the XML_PROGRAMLISTING tag is set to YES Doxygen will # dump the program listings (including syntax highlighting # and cross-referencing information) to the XML output. Note that # enabling this will significantly increase the size of the XML output. XML_PROGRAMLISTING = YES #--------------------------------------------------------------------------- # configuration options for the AutoGen Definitions output #--------------------------------------------------------------------------- # If the GENERATE_AUTOGEN_DEF tag is set to YES Doxygen will # generate an AutoGen Definitions (see autogen.sf.net) file # that captures the structure of the code including all # documentation. Note that this feature is still experimental # and incomplete at the moment. GENERATE_AUTOGEN_DEF = NO #--------------------------------------------------------------------------- # configuration options related to the Perl module output #--------------------------------------------------------------------------- # If the GENERATE_PERLMOD tag is set to YES Doxygen will # generate a Perl module file that captures the structure of # the code including all documentation. Note that this # feature is still experimental and incomplete at the # moment. GENERATE_PERLMOD = NO # If the PERLMOD_LATEX tag is set to YES Doxygen will generate # the necessary Makefile rules, Perl scripts and LaTeX code to be able # to generate PDF and DVI output from the Perl module output. PERLMOD_LATEX = NO # If the PERLMOD_PRETTY tag is set to YES the Perl module output will be # nicely formatted so it can be parsed by a human reader. # This is useful # if you want to understand what is going on. # On the other hand, if this # tag is set to NO the size of the Perl module output will be much smaller # and Perl will parse it just the same. PERLMOD_PRETTY = YES # The names of the make variables in the generated doxyrules.make file # are prefixed with the string contained in PERLMOD_MAKEVAR_PREFIX. # This is useful so different doxyrules.make files included by the same # Makefile don't overwrite each other's variables. PERLMOD_MAKEVAR_PREFIX = #--------------------------------------------------------------------------- # Configuration options related to the preprocessor #--------------------------------------------------------------------------- # If the ENABLE_PREPROCESSING tag is set to YES (the default) Doxygen will # evaluate all C-preprocessor directives found in the sources and include # files. ENABLE_PREPROCESSING = YES # If the MACRO_EXPANSION tag is set to YES Doxygen will expand all macro # names in the source code. If set to NO (the default) only conditional # compilation will be performed. Macro expansion can be done in a controlled # way by setting EXPAND_ONLY_PREDEF to YES. MACRO_EXPANSION = NO # If the EXPAND_ONLY_PREDEF and MACRO_EXPANSION tags are both set to YES # then the macro expansion is limited to the macros specified with the # PREDEFINED and EXPAND_AS_DEFINED tags. EXPAND_ONLY_PREDEF = NO # If the SEARCH_INCLUDES tag is set to YES (the default) the includes files # in the INCLUDE_PATH (see below) will be search if a #include is found. SEARCH_INCLUDES = YES # The INCLUDE_PATH tag can be used to specify one or more directories that # contain include files that are not input files but should be processed by # the preprocessor. INCLUDE_PATH = # You can use the INCLUDE_FILE_PATTERNS tag to specify one or more wildcard # patterns (like *.h and *.hpp) to filter out the header-files in the # directories. If left blank, the patterns specified with FILE_PATTERNS will # be used. INCLUDE_FILE_PATTERNS = # The PREDEFINED tag can be used to specify one or more macro names that # are defined before the preprocessor is started (similar to the -D option of # gcc). The argument of the tag is a list of macros of the form: name # or name=definition (no spaces). If the definition and the = are # omitted =1 is assumed. To prevent a macro definition from being # undefined via #undef or recursively expanded use the := operator # instead of the = operator. PREDEFINED = # If the MACRO_EXPANSION and EXPAND_ONLY_PREDEF tags are set to YES then # this tag can be used to specify a list of macro names that should be expanded. # The macro definition that is found in the sources will be used. # Use the PREDEFINED tag if you want to use a different macro definition. EXPAND_AS_DEFINED = # If the SKIP_FUNCTION_MACROS tag is set to YES (the default) then # doxygen's preprocessor will remove all function-like macros that are alone # on a line, have an all uppercase name, and do not end with a semicolon. Such # function macros are typically used for boiler-plate code, and will confuse # the parser if not removed. SKIP_FUNCTION_MACROS = YES #--------------------------------------------------------------------------- # Configuration::additions related to external references #--------------------------------------------------------------------------- # The TAGFILES option can be used to specify one or more tagfiles. # Optionally an initial location of the external documentation # can be added for each tagfile. The format of a tag file without # this location is as follows: # # TAGFILES = file1 file2 ... # Adding location for the tag files is done as follows: # # TAGFILES = file1=loc1 "file2 = loc2" ... # where "loc1" and "loc2" can be relative or absolute paths or # URLs. If a location is present for each tag, the installdox tool # does not have to be run to correct the links. # Note that each tag file must have a unique name # (where the name does NOT include the path) # If a tag file is not located in the directory in which doxygen # is run, you must also specify the path to the tagfile here. TAGFILES = # When a file name is specified after GENERATE_TAGFILE, doxygen will create # a tag file that is based on the input files it reads. GENERATE_TAGFILE = # If the ALLEXTERNALS tag is set to YES all external classes will be listed # in the class index. If set to NO only the inherited external classes # will be listed. ALLEXTERNALS = NO # If the EXTERNAL_GROUPS tag is set to YES all external groups will be listed # in the modules index. If set to NO, only the current project's groups will # be listed. EXTERNAL_GROUPS = YES # The PERL_PATH should be the absolute path and name of the perl script # interpreter (i.e. the result of `which perl'). PERL_PATH = /usr/bin/perl #--------------------------------------------------------------------------- # Configuration options related to the dot tool #--------------------------------------------------------------------------- # If the CLASS_DIAGRAMS tag is set to YES (the default) Doxygen will # generate a inheritance diagram (in HTML, RTF and LaTeX) for classes with base # or super classes. Setting the tag to NO turns the diagrams off. Note that # this option is superseded by the HAVE_DOT option below. This is only a # fallback. It is recommended to install and use dot, since it yields more # powerful graphs. CLASS_DIAGRAMS = NO # You can define message sequence charts within doxygen comments using the \msc # command. Doxygen will then run the mscgen tool (see # http://www.mcternan.me.uk/mscgen/) to produce the chart and insert it in the # documentation. The MSCGEN_PATH tag allows you to specify the directory where # the mscgen tool resides. If left empty the tool is assumed to be found in the # default search path. MSCGEN_PATH = # If set to YES, the inheritance and collaboration graphs will hide # inheritance and usage relations if the target is undocumented # or is not a class. HIDE_UNDOC_RELATIONS = YES # If you set the HAVE_DOT tag to YES then doxygen will assume the dot tool is # available from the path. This tool is part of Graphviz, a graph visualization # toolkit from AT&T and Lucent Bell Labs. The other options in this section # have no effect if this option is set to NO (the default) HAVE_DOT = YES # By default doxygen will write a font called FreeSans.ttf to the output # directory and reference it in all dot files that doxygen generates. This # font does not include all possible unicode characters however, so when you need # these (or just want a differently looking font) you can specify the font name # using DOT_FONTNAME. You need need to make sure dot is able to find the font, # which can be done by putting it in a standard location or by setting the # DOTFONTPATH environment variable or by setting DOT_FONTPATH to the directory # containing the font. DOT_FONTNAME = FreeSans # The DOT_FONTSIZE tag can be used to set the size of the font of dot graphs. # The default size is 10pt. DOT_FONTSIZE = 10 # By default doxygen will tell dot to use the output directory to look for the # FreeSans.ttf font (which doxygen will put there itself). If you specify a # different font using DOT_FONTNAME you can set the path where dot # can find it using this tag. DOT_FONTPATH = # If the CLASS_GRAPH and HAVE_DOT tags are set to YES then doxygen # will generate a graph for each documented class showing the direct and # indirect inheritance relations. Setting this tag to YES will force the # the CLASS_DIAGRAMS tag to NO. CLASS_GRAPH = NO # If the COLLABORATION_GRAPH and HAVE_DOT tags are set to YES then doxygen # will generate a graph for each documented class showing the direct and # indirect implementation dependencies (inheritance, containment, and # class references variables) of the class with other documented classes. COLLABORATION_GRAPH = YES # If the GROUP_GRAPHS and HAVE_DOT tags are set to YES then doxygen # will generate a graph for groups, showing the direct groups dependencies GROUP_GRAPHS = YES # If the UML_LOOK tag is set to YES doxygen will generate inheritance and # collaboration diagrams in a style similar to the OMG's Unified Modeling # Language. UML_LOOK = NO # If set to YES, the inheritance and collaboration graphs will show the # relations between templates and their instances. TEMPLATE_RELATIONS = NO # If the ENABLE_PREPROCESSING, SEARCH_INCLUDES, INCLUDE_GRAPH, and HAVE_DOT # tags are set to YES then doxygen will generate a graph for each documented # file showing the direct and indirect include dependencies of the file with # other documented files. INCLUDE_GRAPH = YES # If the ENABLE_PREPROCESSING, SEARCH_INCLUDES, INCLUDED_BY_GRAPH, and # HAVE_DOT tags are set to YES then doxygen will generate a graph for each # documented header file showing the documented files that directly or # indirectly include this file. INCLUDED_BY_GRAPH = YES # If the CALL_GRAPH and HAVE_DOT options are set to YES then # doxygen will generate a call dependency graph for every global function # or class method. Note that enabling this option will significantly increase # the time of a run. So in most cases it will be better to enable call graphs # for selected functions only using the \callgraph command. CALL_GRAPH = YES # If the CALLER_GRAPH and HAVE_DOT tags are set to YES then # doxygen will generate a caller dependency graph for every global function # or class method. Note that enabling this option will significantly increase # the time of a run. So in most cases it will be better to enable caller # graphs for selected functions only using the \callergraph command. CALLER_GRAPH = YES # If the GRAPHICAL_HIERARCHY and HAVE_DOT tags are set to YES then doxygen # will graphical hierarchy of all classes instead of a textual one. GRAPHICAL_HIERARCHY = YES # If the DIRECTORY_GRAPH, SHOW_DIRECTORIES and HAVE_DOT tags are set to YES # then doxygen will show the dependencies a directory has on other directories # in a graphical way. The dependency relations are determined by the #include # relations between the files in the directories. DIRECTORY_GRAPH = YES # The DOT_IMAGE_FORMAT tag can be used to set the image format of the images # generated by dot. Possible values are png, jpg, or gif # If left blank png will be used. DOT_IMAGE_FORMAT = png # The tag DOT_PATH can be used to specify the path where the dot tool can be # found. If left blank, it is assumed the dot tool can be found in the path. DOT_PATH = # The DOTFILE_DIRS tag can be used to specify one or more directories that # contain dot files that are included in the documentation (see the # \dotfile command). DOTFILE_DIRS = # The DOT_GRAPH_MAX_NODES tag can be used to set the maximum number of # nodes that will be shown in the graph. If the number of nodes in a graph # becomes larger than this value, doxygen will truncate the graph, which is # visualized by representing a node as a red box. Note that doxygen if the # number of direct children of the root node in a graph is already larger than # DOT_GRAPH_MAX_NODES then the graph will not be shown at all. Also note # that the size of a graph can be further restricted by MAX_DOT_GRAPH_DEPTH. DOT_GRAPH_MAX_NODES = 50 # The MAX_DOT_GRAPH_DEPTH tag can be used to set the maximum depth of the # graphs generated by dot. A depth value of 3 means that only nodes reachable # from the root by following a path via at most 3 edges will be shown. Nodes # that lay further from the root node will be omitted. Note that setting this # option to 1 or 2 may greatly reduce the computation time needed for large # code bases. Also note that the size of a graph can be further restricted by # DOT_GRAPH_MAX_NODES. Using a depth of 0 means no depth restriction. MAX_DOT_GRAPH_DEPTH = 0 # Set the DOT_TRANSPARENT tag to YES to generate images with a transparent # background. This is disabled by default, because dot on Windows does not # seem to support this out of the box. Warning: Depending on the platform used, # enabling this option may lead to badly anti-aliased labels on the edges of # a graph (i.e. they become hard to read). DOT_TRANSPARENT = NO # Set the DOT_MULTI_TARGETS tag to YES allow dot to generate multiple output # files in one run (i.e. multiple -o and -T options on the command line). This # makes dot run faster, but since only newer versions of dot (>1.8.10) # support this, this feature is disabled by default. DOT_MULTI_TARGETS = YES # If the GENERATE_LEGEND tag is set to YES (the default) Doxygen will # generate a legend page explaining the meaning of the various boxes and # arrows in the dot generated graphs. GENERATE_LEGEND = YES # If the DOT_CLEANUP tag is set to YES (the default) Doxygen will # remove the intermediate dot files that are used to generate # the various graphs. 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1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = "i", class = "factor"), Var2 = structure(c(1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 5L, 10L, 11L, 12L, 13L, 14L, 16L, 17L, 18L, 19L, 15L, 20L, 1L, 2L, 3L, 4L, 6L, 7L, 8L, 9L, 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5.33441772160316, -6.33651886787928, -1.66649793204419, 6.39995921794792, -6.68259735687546, 6.08981926235198, -3.53583947340409, -1.21103587811449, 7.24975022684127, 4.97801019029663, 3.14976379524903, 1.96758553600254, 8.43197057862052, 3.33822717991332, 0.235378273638341, -10.1630873499494, 5.76230996838168, -4.61549138480825, 8.20531094191712, 7.12941741404648, 6.00054322848315, -5.48192520344437, -9.59876928907475, 1.7683225066202, -7.1390901352502, 4.25471218768541, 1.80718924948737, -4.20866358864448, -2.44486024149221, 3.91749495580739, -0.158740143510694, 1.47791160298965, -1.34641057801835, -0.668352362532367, -8.17427466239714, -10.2034896388957, -0.860308461006896, 3.75705378224295, -0.336485137456501, 12.5132973642976, 3.49023309888176)), .Names = c("Var1", "Var2", "y"), row.names = c(NA, -400L), b = c(-4.36950232041146, -0.0424473953245598, 5.56953719092376, -5.30334683089667, -1.10285116011549, -0.252162996387152, 2.0499747831754, -1.74063623059889, 7.15605161270018, -1.33699218239019, 0.888758869258693, 3.14516697594312, -2.35262555592678, -4.94052004170718, 6.34707170732277, -10.0261204723322, 2.73257418977353, -0.638617261850626, 3.2694706345968, 0.947216484246937), class = "data.frame") slim <- c(0.1, 40) npoints <- 31 group <- 1 obs <- which(data$Var2 == levels(data$Var2)[group]) shifts <- rep(attr(data, 'b')[data$Var2[group]], npoints) scales <- seq(slim[1], slim[2], length.out = npoints) getData <- function(i) { lb <- attr(data, 'b')[data$Var2[obs[1]]] ldata <- data ldata$y[obs] <- if (length(obs) == 1) lb + shifts[i] else (ldata$y[obs] - lb)*scales[i] + shifts[i] ldata } lme4/inst/testdata/culcita_dat.RData0000644000176000001440000000046312174475414017136 0ustar ripleyusers‹å•ÍNÃ0 ÇÝ$´„Ä{Ll|Ÿ¸ð§Ý¦´dZEÖLi€+Ì ¼Æž´·†°ôkþu]ÇJ÷ña2*&(•ÀK rª[ÕAOŸtgèÂyŒã¸ ‹BŠè}â€8$r¢ þY¾­¥î[ójl‹ê´{½ÙňÅ‹KW,®Yܰ¸eqGB Ïw&îUV·<連™®‚ó¨VÈ }ïÎî?ökLmûZç_[G¶tõ}ë;©‰‰QiYñ ñÄ+:ñªqÙ´¯Ë–[A;÷õbÉa¥ ó˜BrŠ›ÆviôÂp‘ì,­«žé&_zƒ?™Ú5ä!„,¹woÎ´Þ â=nŒñ/˜Qf_‰EÁê ‚ÁRôlme4/inst/testdata/mastitis.rda0000644000176000001440000003777312232467522016304 0ustar ripleyusers‹í} Ü,WUç]êv’—…$„l=„H讽Üx°Y‚ˆ @0!˜—WÔqAETDÅÝ7DÄ]Gq_Qf\Æu÷Q`þ§ëV׫êîo{ù^È÷û¯ïéº÷ÜsÏ~«ª«ž~Ã-é‘[Žcœñÿ=š‰Ã?ks >O¾óÖ{î½ýÞÛï1Æ?Xp|žnÌì?žx:àcÏ<ð,À³·žøXÀsxàãϼp+à…€^ ¸ ðÀK/Üx9àwî¼pà•€OÜ o³{¯¼ðI€O¼ð)€O|àÓÿ ð€×>S €3—.\¸pàJÀU€«× ¸ðy€Ï¼¢Âx;@^ö,Èç›ßø_€ÿ øSÀŸþð×€ü à_1u ¨ üðÀ_4·‚Ïs`ÞÙ£sÀ€±3ð0ûÀ‚—Áƒƒ ää 'ù8ÈÄA&2q‰ƒ<äá0ŸÃ|ó9Ìç ÷Y€7Þßîÿ^/@.þ:ÀG óäßXòV„òÿ¼@V3ÈjYÍÀÓ,dèpàBÀE¬Á` óÚǸpàñÈȨƒ\ÝÿÅ|‘ÿ0Àë?ø ÀÃÜ8ž@o ô–€¿ä&ì2Ý%°»v—ÀîØ]û à?<€ùƒÈðá€Gr@¨Á쳟ø\t={]kóFtÌ€G¬Í¢maŸ²´£•õ`œýiÀÏ~üÿà·ÿ;pÿø'€ØÁI€óà)Á¼É÷Þ ø!À û€>~ Û_ S€œÂ÷~ð»Kÿ5æ‹ßøCÌ ²_x+¶fak¾ä~›tïkýÙö=Öîÿ;à׿Ž9?ð?¿øUÀo°†kH~óAö€ìü$ÀOô ÷ à= ïì\Ög3̃`œèÂ@Þæ#O|à‹_ø#ðŒ~öëß~€u:øû{ðy*>›¼óÀ‡æ°–þbK@¼ðƒ?ˆ~`ã¾@7|'óÐ2Û„Oø¬Ý[د…[ø …ßÚ¿Á¼±ƒ=9Ø“ƒ=9Ä1‡5:Œq ãþþ`Ëþ#ü6ü(àǰ%óDb¯Ä6_6ðeû3ðe#6;°£ýRüÅ~2µÐ¥C¼uˆ­±ÕÁÆxpﯰXã¿ ðí€ï|/6æßøú{èÕƒ/6™@÷ ü('ˆ âQ‚ù|8HŒþHð2?€ ™?ÀÌÿ ~èϾ <@_9Àḇ{ÈÂCzðˆ#þ1€7¾ ¶ùÕàÉ["gðœ &è2œ€½Ø[€ÄäýÄр؄øÔ2¿IîCƒ>ælúÄZ»5àÁÀÎ â¬Aü1ˆÑFâ|Õýàÿ€èÉÿ1æFIà÷ bqûJ ûö“@v‰èrÈsk ˆÏþ ‡Ÿ Ða€ü? 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../../tests/glmmWeights.R ##' General GLMM simulation for the model y ~ x + (1|block) ##' i.e. (2-way notation) Y_{ij} = \beta_1 + \beta_2 x_{ij} + B_j, ##' i = 1..nperblk, j = 1..nblk gSim <- function(nblk=26, nperblk=100, sigma=1, ## st.dev. of ran.eff B beta=c(4,3), x = runif(n),## sd = sqrt(1/12) = 0.2887; allow also x=c(0,1) shape=2, ## shape parameter for Gamma nbinom=10, ## N for binomial trials family=Gamma()) { stopifnot(nblk <= 50000)# some sanity, may increase (but remain "finite"!) ## ch.set: a potentially large set of "letters", as level-labels for 'block': nc <- length(ch.set <- c(LETTERS, letters, paste0(LETTERS,LETTERS), paste0(LETTERS,letters))) while(nblk > nc) nc <- length(ch.set <- c(paste0(ch.set, LETTERS), paste0(ch.set, letters))) stopifnot(1 <= nblk, nblk <= length(ch.set)) d <- expand.grid(block = ch.set[1:nblk], rep= 1:nperblk, KEEP.OUT.ATTRS=FALSE) stopifnot(nblk == length(levels(d$block)), (n <- nrow(d)) == nblk * nperblk, length(x) == n, length(beta) == 2) d$ x <- x reff_f <- rnorm(nblk, sd=sigma) ## need intercept large enough to avoid negative values d$ eta0 <- beta[1] + beta[2]*x ## fixed effects only d$ eta <- d$eta0 + reff_f[d$block] d$ mu <- family$linkinv(d$eta) d$ y <- switch(family$family, "Gamma" = rgamma(n,scale=d$mu/shape,shape=shape), "poisson" = rpois(n,d$mu), "binomial"= { z <- rbinom(n, prob=d$mu, size=nbinom) if (nbinom==1) z else cbind(succ = z, fail = nbinom-z) }, stop("Family ", family$family, " not supported here")) d } lme4/inst/testdata/lme-tst-fits.rda0000644000176000001440000042531412232467522016767 0ustar ripleyusers‹ì=|E÷{—^€P¤H;’`¤Pv¥ RC ˆâån“œ\îÂBÀÞþöúÙ{ùô³7TD4goˆØ+**6)%ÿ™Ý™ÝÙÙÙ½’ƒ\âí/“›úæ½7oÞ¼)[>¹¢$»"›ã8+—bÿS€7Õ þY¸T. ¼»T¹ËíÕ>—ÓÞ>GM¥×íä,)=¨t©>Ñ_ÇO/Z=ٰÜüü‰ÞVœÓ]+–!ZqÑð¡#€çp럪-.·È'VMrÛýþÉb£ºÜ*—èvJ D?¨–Ãóˆ49âê€èóØÝu•á ÖŠ>—#qƒ]ž€X-ú¨f§yìµ°½RÚ,˜2´OöZkƒ˜Þª*¿ÀÅùWúõšŸ>õg½èª® øq%õ@dôÃÒ€ÉW>eÖL™b:H”˜ëó¼†: 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>jÏk·ÓäˆÏò P«Ø>89rÜÔzãl3£f¿ùÎÔ)XÄ`PÚ:Kðo×ÀsÍw60&m'ÏOú¯¸óÖ€Ú€›·î¿4z«ørÏü¬ÿ?ÉÓožÏ¿hÎy8?­T¯Ýø˜š².Òß;€¼Õ¼Õh–e«.{ü¿5#Øê ÿó–5fkìíøÓ2Õ1¶æ£?ïxn|<ôåùx+ý¥ƒ³·ñ7¼3D¾6q!X›úõÛÇZ€Õ MsR–]UÛí:÷Î ðëŸõ×<³ ü³áÌ×ÞlŽtÚ÷OÛ¡WÀÙ_f=¾úÓ!àêé×þìeV5÷iÝcÿ$³¡Ÿ¼PgZlK¶ÆŒA]¦®­ÍÆí?2|ÆÊ"¶eÇÎyá=ß`;ö̉5«çý.ÿ­]cðÛ_/ÖöË~5l2¿çóà׳Vm{¯¸ØiÌYðíÆ·ßJøN¶5z¼ÑÇšÿÀóú==ë—@ð[Ë9ñ‹nõyïž ×'£.¿³7øpezóO‚t¯±êèO/ž¹ÍV·©[“­ãÙ:;ú'&Ö[Ã6©ز߯³ížïve¶)oòcþ9¶«Q­Ÿò‚ŠwuìÓü~õËÞ-Nåû}œ½ë«™umGû^\ù`ú¶ÕÏÖX³â¥ðQ³6ïçn¾>;°xAìc`ó©ÓqÍÒlàÇà¶Ï³o}þl¿wók¿µGü˜1cè/àòÖ×ôAy™¬úTÀÏõMØÈò??—U‰­W·íªk ï±mž:>cÕXÌ./v솵m¿³Ë_`4' îÅ>ÐtÑ×`ΓàúèjÝjWÿMø«Gì/mÀÜoÖššþžV¬2œÜ|>tØ«ÝÌ,Ø2jÄåZyÃÀž‰}¯ÔÈúð+îûâ/àüvëäüË2?öÜPg5±æº1Ú³ ÖÖΜ¾âoL_àÇiµ£ ÃõîrÿI¦,+É„¥féÓÓ ©½ }¬ìåLS½¿X¬>&­Ô¾lí2“ õë|øôó|„CMý‰Íí lme4/inst/testdata/lme-tst-fits.R0000644000176000001440000000326212232467522016414 0ustar ripleyusers#### Saved fits for lme4 testing #### ---------------------------------- fn <- system.file("testdata/lme-tst-fits.rda", package="lme4", mustWork=TRUE) if(FALSE) ### "Load" these by load(fn) library(lme4) ## intercept only in both fixed and random effects fit_sleepstudy_0 <- lmer(Reaction ~ 1 + (1|Subject), sleepstudy) ## fixed slope, intercept-only RE fit_sleepstudy_1 <- lmer(Reaction ~ Days + (1|Subject), sleepstudy) ## fixed slope, intercept & slope RE fit_sleepstudy_2 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) ## fixed slope, independent intercept & slope RE fit_sleepstudy_3 <- lmer(Reaction ~ Days + (1|Subject)+ (0+Days|Subject), sleepstudy) save(list=ls(pattern="fit_sleepstudy"),file="lme-tst-fits.rda") load("lme-tst-fits.rda") cbpp$obs <- factor(seq(nrow(cbpp))) fit_cbpp_0 <- glmer(cbind(incidence,size-incidence) ~ 1 + (1|herd), cbpp, family=binomial) fit_cbpp_1 <- update(fit_cbpp_0, . ~ . + period) fit_cbpp_2 <- update(fit_cbpp_1, . ~ . + (1|obs)) fit_cbpp_3 <- update(fit_cbpp_0, formula=incidence/size ~ period + (1 | herd), weights=size) save(list=ls(pattern="fit_"),file="lme-tst-fits.rda") ## an example with >20 fixed effects: if (require(agridat)) { dat <- archbold.apple ## Define main plot and subplot dat <- transform(dat, rep=factor(rep), spacing=factor(spacing), trt=factor(trt), mp = factor(paste(row,spacing,sep="")), sp = factor(paste(row,spacing,stock,sep=""))) fit_agridat_archbold <- lmer(yield ~ -1 + trt + (1|rep/mp/sp), dat) save(list=ls(pattern="fit_"),file="lme-tst-fits.rda") } lme4/inst/testdata/crabs_randdata2.Rda0000644000176000001440000000356212156422372017405 0ustar ripleyusers‹íKlUðù'qók“þ ”´H)˜ÄöºIi»¡I)B€Õ•à€›8’‘kÛ-PŠP/HpB¢pá@œzèâBH¨U-ÒÒ‚‰^˜÷¼3ãÝç·Î’²÷Iãy;oÞÌì¼ÙÝyœ8 3ÆüÌßüôó ²vŽÛ ©ÜÌLª”b,Ð˯û8tq˜Û7ÇD{€ø!¸Þ 8ôQf½<x+àÀ»G$þQ ïðê‡ëýÞX³±GÆ(o ð¸„ñ~å~vr÷J×h7ÞÚ<(õGÿ£\Ù.´[öêCùèç%9{ç¬tôöG;Пaœàøî<xp³¼Ö•äãýâý¡¾q+¿\ÆdŒ~Ü/aôSÚ“<·{%<`ÓÏF.Ùµ0Æ>h_Ÿd÷À÷HòÐÿ(W²‹ìfVŒ…䣌G”3$Ñ­ýÉŽ5Aœ°,vã³Rq–†ÄI³•åŽÓex.š"Nší}Ò,qâëáÿ TâäFõ6Ú~Ož»ä1—¾O]n‚çÕíÅ•qâ«ëÊÿ:/^)òš°¸ò}âöâö8öò“¥•f{Ÿ4KœxãªÆËX\'.7Á8¸½¸2N¼qu[ t2#NÄ!„UÄyƒ[×A-Äù„õn³¡‰¸[Ía-‡úmhâ¼Ãë8ÜÊa³ MìØvsèaÆ9ˆM64q¦BÜØ•ÝbC 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ÿ7ªi,gD#CV}W½ÏPá{#­—kÙݧê—¾þù<á¨fÒ…û«õV”ê¤çÌë>6ä1­_žWÍQ»øDá~f89PµPB„ž|¦ÿ²Ê[Ãï¿©„ ù‘<Ù’£ÎðeI5ä zוgb© Ãb¦ÈâÉv|¿À^Øû!Ò@ àW8‡à7H‡ 8 ™ÙpŽBƒ\ȃãpò¡ài›'¡çEŸ†"(†3P=Ó~ÏbpÎA9T@%TA5œ‡‹p jàO¨…:h„kÐ×áÜ„ž×Á¸èDõè«€ºt=4bЈA7A3 Xt+ x4âÑ·1iĤ1!ÒˆKcA¥ïÁ߀6èž6þ‡€ÁKwÀcÀÄCwB࣠1!Ó´´ôéf €Aç1H0„ >hFT@°à2#zwî ¾  ƒMó.èÁ0€±`ã`<ÃÀʘáA‹<ÖÏ"Ÿ5”aßS˜(Ïö”7 ˜ S`*X‚XÃ4˜À À‚Ç~3a`#Âbòdí€ ìa8&LÖœÀæÁ|X.°\a1,7X^à X(YX ~àA ØT±k ´w€0RêÙm™z /Š7%Hˆ¡BõnÍ$ÿ&.)clme4/ToDo0000644000176000001440000000245212156422372011742 0ustar ripleyusers----- Change the terms object to be the terms for the fixed-effects only so that the drop1 method doesn't try to drop the grouping factors for the random effects. ----- Modify the effect of the verbose setting in the Nelder-Mead optimizer. In particular, it should count evaluations but define an "iteration" as a change in the best value encountered so far. ----- The paper by Sophia Rabe-Hesketh et al describes a spherical form of the Gauss-Hermite quadrature formula. Look that up and use it. Because the Gauss-Hermite quadrature is formed as a sum, it is necessary to divide the contributions to the deviance according to the levels of the random effects. This means that it is only practical to use AGQ when the response vector can be split into sections that are conditionally independent. As far as I can see this will mean a single grouping factor only. ----- Allow for a matrix of responses in lmer so multiple fits can be performed without needing to regenerate the model matrices. ----- Determine what a "coef" function should do for multiple, possibly non-nested, grouping factors. ----- - add nicer (more realistic?) pedigree examples and tests - document print() including an example print(, corr = FALSE) and one with many fixed effects (*) and print(, symbolic.cor = TRUE) lme4/tests/0000755000176000001440000000000012273467214012315 5ustar ripleyuserslme4/tests/optimizer.Rout0000644000176000001440000000740112202775601015206 0ustar ripleyusers R Under development (unstable) (2013-07-27 r63422) -- "Unsuffered Consequences" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(lme4) Loading required package: lattice Loading required package: Matrix > (testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) [1] 1 > source(system.file("test-tools-1.R", package = "Matrix"), keep.source = FALSE) > > ## should be able to run any example with any bounds-constrained optimizer ... > ## Nelder_Mead, bobyqa built in; optimx/nlminb, optimx/L-BFGS-B > ## optimx/Rcgmin will require a bit more wrapping/interface work (requires gradient) > > fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) ## Nelder_Mead > fm1B <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, + control=lmerControl(optimizer="bobyqa")) > stopifnot(all.equal(fixef(fm1),fixef(fm1B))) > require(optimx) Loading required package: optimx Loading required package: numDeriv > lmerCtrl.optx <- function(method, ...) + lmerControl(optimizer="optimx", optControl=c(list(method=method), list(...))) > glmerCtrl.optx <- function(method, ...) + glmerControl(optimizer="optimx", optControl=c(list(method=method), list(...))) > > > ## FAILS on Windows (on r-forge only, not win-builder)... 'function is infeasible at initial parameters' > ## (can we test whether we are on r-forge??) > if (.Platform$OS.type != "windows") { + fm1C <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, + control=lmerCtrl.optx(method="nlminb")) + fm1D <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, + control=lmerCtrl.optx(method="L-BFGS-B")) + stopifnot(is.all.equal4(fixef(fm1),fixef(fm1B),fixef(fm1C),fixef(fm1D))) + + if (testLevel > 2) { + fm1E <- update(fm1,control=lmerCtrl.optx(method=c("nlminb","L-BFGS-B"))) + ## hack equivalence of call and optinfo + fm1E@call <- fm1C@call + fm1E@optinfo <- fm1C@optinfo + ## FIXME: this *should* be identical, but we have small numeric differences + all.equal(fm1C,fm1E,tol=1e-5) + } + } > > gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), + data = cbpp, family = binomial, + control=glmerControl(tolPwrss=1e-13)) > gm1B <- update(gm1,control=glmerControl (tolPwrss=1e-13, optimizer="bobyqa")) > gm1C <- update(gm1,control=glmerCtrl.optx(tolPwrss=1e-13, method="nlminb")) Warning messages: 1: In nlminb(start = par, objective = ufn, gradient = ugr, lower = lower, : unrecognized control elements named ‘tolPwrss’ ignored 2: In nlminb(start = par, objective = ufn, gradient = ugr, lower = lower, : unrecognized control elements named ‘tolPwrss’ ignored > gm1D <- update(gm1,control=glmerCtrl.optx(tolPwrss=1e-13, method="L-BFGS-B")) Warning messages: 1: In optim(par = par, fn = ufn, gr = ugr, lower = lower, upper = upper, : unknown names in control: tolPwrss 2: In optim(par = par, fn = ufn, gr = ugr, lower = lower, upper = upper, : unknown names in control: tolPwrss > stopifnot(is.all.equal4(fixef(gm1),fixef(gm1B),fixef(gm1C),fixef(gm1D),tol=1e-5)) Error: is.all.equal4(fixef(gm1), fixef(gm1B), fixef(gm1C), fixef(gm1D), .... is not TRUE Execution halted lme4/tests/offset.R0000644000176000001440000000224012156422373013721 0ustar ripleyusers## simple examples with offsets, to exercise methods etc. library(lme4) ## generate a basic Gamma/random effects sim set.seed(101) d <- expand.grid(block=LETTERS[1:26],rep=1:100) d$x <- runif(nrow(d)) ## sd=1 reff_f <- rnorm(length(levels(d$block)),sd=1) ## need intercept large enough to avoid negative values d$eta0 <- 4+3*d$x ## version without random effects d$eta <- d$eta0+reff_f[d$block] ## lmer() test: d$mu <- d$eta d$y <- rnorm(nrow(d),mean=d$mu,sd=1) fm1 <- lmer(y~x+(1|block),data=d) fm1off <- lmer(y~x+(1|block)+offset(3*x),data=d) ## check equality stopifnot(all.equal(fixef(fm1)[2]-3,fixef(fm1off)[2])) p0 <- predict(fm1) p1 <- predict(fm1,newdata=d) p2 <- predict(fm1off,newdata=d) stopifnot(all.equal(p0,unname(p1),unname(p2))) ## glmer() test: d$mu <- exp(d$eta) d$y <- rpois(nrow(d),d$mu) gm1 <- glmer(y~x+(1|block),data=d,family=poisson) gm1off <- glmer(y~x+(1|block)+offset(3*x),data=d,family=poisson) ## check equality stopifnot(all.equal(fixef(gm1)[2]-3,fixef(gm1off)[2],tol=2e-4)) p0 <- predict(gm1) p1 <- predict(gm1,newdata=d) p2 <- predict(gm1off,newdata=d) stopifnot(all.equal(p0,unname(p1),unname(p2))) ## FIXME: should also test simulations lme4/tests/varcorr.R0000644000176000001440000000102612156422373014112 0ustar ripleyuserslibrary(lme4) data(Orthodont, package="nlme") fm1 <- lmer(distance ~ age + (age|Subject), data = Orthodont) VarCorr(fm1) fm2ML <- lmer(diameter ~ 1 + (1|plate) + (1|sample), Penicillin, REML=0) VarCorr(fm2ML) gm1 <- glmer(cbind(incidence,size-incidence) ~ period + (1|herd),data=cbpp, family=binomial) VarCorr(gm1) cbpp$obs <- factor(seq(nrow(cbpp))) gm2 <- update(gm1,.~.+(1|obs)) VarCorr(gm2) if (FALSE) { ## testing lme4/lme4 incompatibility ## library(lme4) VarCorr(fm1) lme4:::VarCorr.merMod(fm1) ## OK } lme4/tests/Rplots.pdf0000644000176000001440000002240212204041406014254 0ustar ripleyusers%PDF-1.4 %âãÏÓ\r 1 0 obj << /CreationDate (D:20130817234400) /ModDate (D:20130817234400) /Title (R Graphics Output) /Producer (R 3.1.0) /Creator (R) >> endobj 2 0 obj << /Type /Catalog /Pages 3 0 R >> 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S4_2list <- function(obj) { # no longer used sn <- slotNames(obj) structure(lapply(sn, slot, object = obj), .Names = sn) } ## Is now (2010-09-03) in Matrix' test-tools.R above ## showProc.time <- local({ ## pct <- proc.time() ## function() { ## CPU elapsed __since last called__ ## ot <- pct ; pct <<- proc.time() ## cat('Time elapsed: ', (pct - ot)[1:3],'\n') ## } ## }) (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) (fm1a <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, REML = FALSE)) (fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) anova(fm1, fm2) ## Now works for glmer fm1. <- suppressWarnings(glmer(Reaction ~ Days + (Days|Subject), sleepstudy)) ## default family=gaussian/identity link -> automatically calls lmer() (but with a warning) ## hack call -- comes out unimportantly different fm1.@call[[1]] <- quote(lmer) stopifnot(all.equal(fm1, fm1.)) ## Test against previous version in lmer1 (using bobyqa for consistency) #(fm1. <- lmer1(Reaction ~ Days + (Days|Subject), sleepstudy, opt = "bobyqa")) #stopifnot(all.equal(fm1@devcomp$cmp['REML'], fm1.@devcomp$cmp['REML']), # all.equal(fixef(fm1), fixef(fm1.)), # all.equal(fm1@re@theta, fm1.@theta, tol = 1.e-7), # all.equal(ranef(fm1), ranef(fm1.))) ## compDev = FALSE no longer applies to lmer ## Test 'compDev = FALSE' (vs TRUE) ## fm1. <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, ## compDev = FALSE)#--> use R code (not C++) for deviance computation ## stopifnot(all.equal(fm1@devcomp$cmp['REML'], fm1.@devcomp$cmp['REML']), ## all.equal(fixef(fm1), fixef(fm1.)), ## all.equal(fm1@re@theta, fm1.@re@theta, tol = 1.e-7), ## all.equal(ranef(fm1), ranef(fm1.), tol = 1.e-7)) stopifnot(all.equal(fixef(fm1), fixef(fm2), tol = 1.e-13), all.equal(unname(fixef(fm1)), c(251.405104848485, 10.467285959595), tol = 1e-13), all.equal(Matrix::cov2cor(vcov(fm1))["(Intercept)", "Days"], -0.13755, tol=1e-4)) fm1ML <- refitML(fm1) fm2ML <- refitML(fm2) print(AIC(fm1ML)); print(AIC(fm2ML)) print(BIC(fm1ML)); print(BIC(fm2ML)) (fm3 <- lmer(Yield ~ 1|Batch, Dyestuff2)) stopifnot(all.equal(coef(summary(fm3)), array(c(5.6656, 0.67838803150, 8.3515624346), c(1,3), dimnames = list("(Intercept)", c("Estimate", "Std. Error", "t value"))))) showProc.time() # ### {from ../man/lmer.Rd } --- compare lmer & lmer1 --------------- (fmX1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) (fm.1 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) #(fmX2 <- lmer2(Reaction ~ Days + (Days|Subject), sleepstudy)) #(fm.2 <- lmer2(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) ## check update(, ): fm.3 <- update(fmX1, . ~ Days + (1|Subject) + (0+Days|Subject)) stopifnot(all.equal(fm.1, fm.3)) fmX1s <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, sparseX=TRUE) #fmX2s <- lmer2(Reaction ~ Days + (Days|Subject), sleepstudy, sparseX=TRUE) showProc.time() # for(nm in c("coef", "fixef", "ranef", "sigma", "model.matrix", "model.frame" , "terms")) { cat(sprintf("%15s : ", nm)) FUN <- get(nm) F.fmX1s <- FUN(fmX1s) # F.fmX2s <- FUN(fmX2s) # if(nm == "model.matrix") { # F.fmX1s <- as(F.fmX1s, "denseMatrix") # F.fmX2s <- as(F.fmX2s, "denseMatrix") # FF <- function(.) {r <- FUN(.); row.names(r) <- NULL # as(r, "generalMatrix") } # } # else FF <- FUN stopifnot( all.equal( FF(fmX1), F.fmX1s, tol = 1e-6) # , # all.equal( FF(fmX2), F.fmX2s, tol = 1e-5) # , # all.equal( FF(fm.1), F.fmX2s, tol = 9e-6) ## these are different models # , # all.equal(F.fmX2s, F.fmX1s, tol = 6e-6) # , # all.equal(FUN(fm.1), FUN(fm.2), tol = 6e-6) , TRUE) cat("[Ok]\n") } ## transformed vars should work[even if non-sensical as here;failed in 0.995-1] fm2l <- lmer(log(Reaction) ~ log(Days+1) + (log(Days+1)|Subject), data = sleepstudy, REML = FALSE) ## no need for an expand method now : xfm2 <- expand(fm2) stopifnot(dim(ranef(fm2l)[[1]]) == c(18, 2), is((c3 <- coef(fm3)), "coef.mer"), all(fixef(fm3) == c3$Batch),## <-- IFF \hat{\sigma^2} == 0 TRUE) ## Simple example by Andrew Gelman (2006-01-10) ---- n.groups <- 10 ; n.reps <- 2 n <- length(group.id <- gl(n.groups, n.reps)) ## simulate the varying parameters and the data: set.seed(0) a.group <- rnorm(n.groups, 1, 2) y <- rnorm (n, a.group[group.id], 1) ## fit and summarize the model fit.1 <- lmer (y ~ 1 + (1 | group.id)) coef (fit.1) ## check show( <"summary.mer"> ): (sf1 <- summary(fit.1)) # --> now looks as for fit.1 stopifnot(all.equal(fixef(fit.1), c("(Intercept)" = 1.571312129)), all.equal(unname(ranef(fit.1, drop=TRUE)[["group.id"]]), c(1.8046888, -1.8097665, 1.6146451, 1.5408268, -0.1331995, -3.3306655, -1.8259277, -0.8735145, -0.3591311, 3.3720441), tol = 1e-5) ) ## ranef and coef rr <- ranef(fm1) stopifnot(is.list(rr), length(rr) == 1, class(rr[[1]]) == "data.frame") print(plot(rr)) stopifnot(is(cc <- coef(fm1), "coef.mer"), is.list(cc), length(cc) == 1, class(cc[[1]]) == "data.frame") print(plot(cc)) rr <- ranef(fm2) stopifnot(is.list(rr), length(rr) == 1, class(rr[[1]]) == "data.frame") print(plot(rr)) stopifnot(is(cc <- coef(fm2), "coef.mer"), is.list(cc), length(cc) == 1, class(cc[[1]]) == "data.frame") print(plot(cc)) showProc.time() # ## Invalid factor specification -- used to seg.fault: set.seed(1) dat <- within(data.frame(lagoon = factor(rep(1:4,each = 25)), habitat = factor(rep(1:20, each = 5))), { y <- round(10*rnorm(100, m = 10*as.numeric(lagoon))) }) try(reg <- lmer(y ~ habitat + (1|habitat*lagoon), data = dat) # did seg.fault ) # now gives error ^- should be ":" r1 <- lmer(y ~ 0+habitat + (1|habitat:lagoon), data = dat) # ok, but senseless r1b <- lmer(y ~ 0+habitat + (1|habitat), data = dat) # same model, clearly indeterminable ## "TODO" : summary(r1) should ideally warn the user stopifnot(all.equal(fixef(r1), fixef(r1b), tol= 1e-15), all.equal(ranef(r1), ranef(r1b), tol= 1e-15, check.attributes=FALSE)) ## Use a more sensible model: r2.0 <- lmer(y ~ 0+lagoon + (1|habitat:lagoon), data = dat) # ok r2 <- lmer(y ~ 0+lagoon + (1|habitat), data = dat) # ok, and more clear stopifnot(all.equal(fixef(r2), fixef(r2.0), tol= 1e-15), all.equal(ranef(r2), ranef(r2.0), tol= 1e-15, check.attributes=FALSE)) V2 <- vcov(r2) assert.EQ.mat(V2, diag(x = 9.9833/3, nr = 4)) stopifnot(all.equal(unname(fixef(r2)) - (1:4)*100, c(1.72, 0.28, 1.76, 0.8), tol = 1e-13)) ## sparseX version should give same numbers: r2. <- lmer(y ~ 0+lagoon + (1|habitat), data = dat, sparseX = TRUE, verbose = TRUE) ## the summary() components we do want to compare 'dense X' vs 'sparse X': nmsSumm <- c("methTitle", "devcomp", "logLik", "ngrps", "coefficients", "sigma", "REmat", "AICtab") sr2 <- summary(r2) sr2. <- summary(r2.) sr2.$devcomp$dims['spFe'] <- 0L # to allow for comparisons below stopifnot(all.equal(sr2[nmsSumm], sr2.[nmsSumm], tol= 1e-14) , all.equal(ranef(r2), ranef(r2.), tol= 1e-14) , Matrix:::isDiagonal(vcov(r2.)) # ok , all.equal(Matrix::diag(vcov(r2.)), rep.int(V2[1,1], 4), tol= 1e-13) # , all(vcov(r2.)@factors$correlation == diag(4)) # not sure why this fails , TRUE) r2. ### mcmcsamp() : ## From: Andrew Gelman ## Date: Wed, 18 Jan 2006 22:00:53 -0500 if (FALSE) { # mcmcsamp still needs work ## NB: Need to restore coda to the Suggests: field of DESCRIPTION ## file if this code block is reinstated. ## has.coda <- require(coda) ## if(!has.coda) ## cat("'coda' package not available; some outputs will look suboptimal\n") ## Very simple example y <- 1:10 group <- gl(2,5) (M1 <- lmer (y ~ 1 + (1 | group))) # works fine (r1 <- mcmcsamp (M1)) # dito r2 <- mcmcsamp (M1, saveb = TRUE) # gave error in 0.99-* and 0.995-[12] (r10 <- mcmcsamp (M1, n = 10, saveb = TRUE)) ## another one, still simple y <- (1:20)*pi x <- (1:20)^2 group <- gl(2,10) M1 <- lmer (y ~ 1 | group) mcmcsamp (M1, n = 2, saveb=TRUE) # fine M2 <- lmer (y ~ 1 + x + (1 + x | group)) # false convergence ## should be identical (and is) M2 <- lmer (y ~ x + ( x | group))# false convergence -> simulation doesn't work: if(FALSE) ## try(..) fails here (in R CMD check) [[why ??]] mcmcsamp (M2, saveb=TRUE) ## Error: inconsistent degrees of freedom and dimension ... ## mcmc for glmer: rG1k <- mcmcsamp(m1, n = 1000) summary(rG1k) rG2 <- mcmcsamp(m1, n = 3, verbose = TRUE) } ## Spencer Graves' example (from a post to S-news, 2006-08-03) ---------------- ## it should give an error, rather than silent non-sense: tstDF <- data.frame(group = letters[1:5], y = 1:5) assertError(## Now throws an error, as desired : lmer(y ~ 1 + (1|group), data = tstDF) ) showProc.time() # ## Wrong formula gave a seg.fault at times: set.seed(2)# ! D <- data.frame(y= rnorm(12,10), ff = gl(3,2,12), x1=round(rnorm(12,3),1), x2=round(rnorm(12,7),1)) ## NB: The first two are the same, having a length-3 R.E. with 3 x 3 vcov-matrix: ## --> do need CPU ## suppressWarnings() for warning about too-few random effects levels m0 <- suppressWarnings(lmer(y ~ (x1 + x2)|ff, data = D)) m1 <- suppressWarnings(lmer(y ~ x1 + x2|ff , data = D)) m2 <- suppressWarnings(lmer(y ~ x1 + (x2|ff), data = D)) m3 <- suppressWarnings(lmer(y ~ (x2|ff) + x1, data = D)) stopifnot(all.equal(ranef(m0), ranef(m1)), all.equal(ranef(m2), ranef(m3)), inherits(tryCatch(lmer(y ~ x2|ff + x1, data = D), error = function(e)e), "error")) showProc.time() # ## Reordering of grouping factors should not change the internal structure #Pm1 <- lmer1(strength ~ (1|batch) + (1|sample), Pastes, doFit = FALSE) #Pm2 <- lmer1(strength ~ (1|sample) + (1|batch), Pastes, doFit = FALSE) #P2.1 <- lmer (strength ~ (1|batch) + (1|sample), Pastes, devFunOnly = TRUE) #P2.2 <- lmer (strength ~ (1|sample) + (1|batch), Pastes, devFunOnly = TRUE) ## The environments of Pm1 and Pm2 should be identical except for ## "call" and "frame": #stopifnot(## all.EQ(env(Pm1), env(Pm2)), # all.EQ(S4_2list(P2.1), # S4_2list(P2.2))) ## example from Kevin Thorpe: synthesized equivalent ## http://thread.gmane.org/gmane.comp.lang.r.lme4.devel/9835 ## NA issue: simpler example d <- data.frame(y=1:60,f=factor(rep(1:6,each=10))) d$y[2] <- NA d$f[3:4] <- NA lmer(y~(1|f),data=d) glmer(y~(1|f),data=d,family=poisson) ## we originally thought that these examples should be ## estimating non-zero variances, but they shouldn't ... ## number of levels with each level of replication levs <- c(800,300,150,100,50,50,50,20,20,5,2,2,2,2) n <- seq_along(levs) flevels <- seq(sum(levs)) set.seed(101) fakedat <- data.frame(DA = factor(rep(flevels,rep(n,levs))), zbmi=rnorm(sum(n*levs))) ## add NA values fakedat[sample(nrow(fakedat),100),"zbmi"] <- NA fakedat[sample(nrow(fakedat),100),"DA"] <- NA m5 <- lmer(zbmi ~ (1|DA) , data = fakedat, control=lmerControl(check.nobs.vs.rankZ="ignore")) m6 <- update(m5, data=na.omit(fakedat)) stopifnot(VarCorr(m5)[["DA"]] == 0, VarCorr(m6)[["DA"]] == 0) lme4/tests/predsim.Rout0000644000176000001440000000555012204041406014621 0ustar ripleyusers R Under development (unstable) (2013-08-18 r63609) -- "Unsuffered Consequences" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ## compare range, average, etc. of simulations to > ## conditional and unconditional prediction > library(lme4) Loading required package: lattice Loading required package: Matrix > do.plot <- FALSE > > fm1 <- lmer(Reaction~Days+(1|Subject),sleepstudy) > set.seed(101) > pp <- predict(fm1) > rr <- range(usim2 <- simulate(fm1,1,use.u=TRUE)[[1]]) > stopifnot(all.equal(rr,c(159.3896,439.1616),tol=1e-6)) > if (do.plot) { + plot(pp,ylim=rr) + lines(sleepstudy$Reaction) + points(simulate(fm1,1)[[1]],col=4) + points(usim2,col=2) + } > > set.seed(101) > > ## conditional prediction > ss <- simulate(fm1,1000,use.u=TRUE) > ss_sum <- t(apply(ss,1,quantile,c(0.025,0.5,0.975))) > plot(pp) > matlines(ss_sum,col=c(1,2,1),lty=c(2,1,2)) > stopifnot(all.equal(unname(ss_sum[,2]),pp,tolerance=5e-3)) > > ## population-level prediction > pp2 <- predict(fm1,REform=NA) > ss2 <- simulate(fm1,1000,use.u=FALSE) > ss_sum2 <- t(apply(ss2,1,quantile,c(0.025,0.5,0.975))) > > if (do.plot) { + plot(pp2,ylim=c(200,400)) + matlines(ss_sum2,col=c(1,2,1),lty=c(2,1,2)) + } > > stopifnot(all.equal(unname(ss_sum2[,2]),unname(pp2),tol=8e-3)) > > ## predict(...,newdata=...) on models with derived variables in the random effects > ## e.g. (f:g, f/g) > set.seed(101) > d <- expand.grid(f=factor(letters[1:10]),g=factor(letters[1:10]), + rep=1:10) > d$y <- rnorm(nrow(d)) > m1 <- lmer(y~(1|f:g),d) > p1A <- predict(m1) > p1B <- predict(m1,newdata=d) > stopifnot(all.equal(p1A,unname(p1B))) > m2 <- lmer(y~(1|f/g),d) > p2A <- predict(m2) > p2B <- predict(m2,newdata=d) > stopifnot(all.equal(p2A,unname(p2B))) > > ## with numeric grouping variables > dn <- transform(d,f=as.numeric(f),g=as.numeric(g)) > m1N <- update(m1,data=dn) > p1NA <- predict(m1N) > p1NB <- predict(m1N,newdata=dn) > stopifnot(all.equal(p1NA,unname(p1NB))) > > ## > set.seed(1) > s1 <- simulate(fm1) > set.seed(1) > s2 <- simulate(fm1,newdata=model.frame(fm1), + newparams=getME(fm1,c("theta","beta"))) > all.equal(s1,s2) [1] TRUE > > fm0 <- update(fm1,.~.-Days) > ## > ## sim() -> simulate() -> refit() -> deviance > ## > > > proc.time() user system elapsed 4.892 2.792 7.652 lme4/tests/getME.R0000644000176000001440000000364312174345550013445 0ustar ripleyuserslibrary(lme4) #### tests of getME() ### are names correct? -------------- if(getRversion() < "2.15") paste0 <- function(...) paste(..., sep = '') hasInms <- function(x) grepl("(Intercept", names(x), fixed=TRUE) matchNms <- function(fm, PAR) { stopifnot(is.character(vnms <- names(fm@cnms))) mapply(grepl, paste0("^", vnms), names(PAR)) } chkIMod <- function(fm) {## check "intercept only" model b1 <- getME(fm,"beta") f1 <- fixef(fm) stopifnot(hasInms(f1), f1 == b1, hasInms(t1 <- getME(fm,"theta")), matchNms(fm, t1)) } fm1 <- lmer(diameter ~ (1|plate) + (1|sample), Penicillin) chkIMod(fm1) fm2 <- lmer(angle ~ recipe * temperature + (1|recipe:replicate), cake) stopifnot(fixef(fm2) == getME(fm2,"beta")) getME(fm2,"theta") getME(fm3 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy), "theta") getME(fm4 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy), "theta") ## internal consistency check ensuring that all allowed 'name's work (and are not empty): (nmME <- eval(formals(getME)$name)) chkMEs <- function(fm, nms) { stopifnot(is.character(nms)) str(parts <- sapply(nms, getME, object = fm, simplify=FALSE)) isN <- sapply(parts, is.null) stopifnot(identical(names(isN), nms), !any(isN)) } chkMEs(fm1, nmME) chkMEs(fm2, nmME) chkMEs(fm3, nmME) chkMEs(fm4, nmME) ## multiple components can now be retrieved at once gg <- getME(fm2,c("theta","beta")) gg2 <- getME(fm2,c("theta","beta","X")) ## list of Zt for each random-effects factor lapply(getME(fm2,c("Ztlist")),dim) ## Cholesky factors returned as a list of matrices getME(fm1,"ST") getME(fm2,"ST") ## distinction between number of RE terms ## and number of RE grouping factors stopifnot(getME(fm2,"n_rtrms")==1) stopifnot(getME(fm2,"n_rfacs")==1) lapply(getME(fm4,c("Ztlist")),dim) stopifnot(getME(fm4,"n_rtrms")==2) stopifnot(getME(fm4,"n_rfacs")==1) stopifnot(getME(fm1,"sigma")==sigma(fm1)) lme4/tests/utils.R0000644000176000001440000000042612106753646013604 0ustar ripleyuserscheckSlots <- function(x,y,...) { for (i in slotNames(x)) { cat(i,"\n") print(all.equal(slot(x,i),slot(y,i),...)) } } checkElements <- function(x,y,...) { for (i in names(x)) { cat(i,"\n") print(all.equal(x[[i]],y[[i]],...)) } } lme4/tests/resids.R0000644000176000001440000000115112156422373013724 0ustar ripleyuserslibrary(lme4) ## raw residuals for LMMs fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) stopifnot(all.equal(residuals(fm1),sleepstudy$Reaction-fitted(fm1))) r1 <- residuals(fm1,type="pearson") ## deviance/Pearson residuals for GLMMs gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp) p <- fitted(gm1) n <- cbpp$size v <- n*p*(1-p) obs_p <- cbpp$incidence/cbpp$size rp <- residuals(gm1,"pearson") rp1 <- (obs_p-p)/sqrt(p*(1-p)) rp2 <- rp1*n ## FIXME:: restore this test ## stopifnot(all.equal(rp,rp2)) r2 <- residuals(gm1,type="deviance") lme4/tests/glmerWarn.R0000644000176000001440000000104312172000024014350 0ustar ripleyuserslibrary(lme4) library(testthat) m3 <- suppressWarnings(glmer(Reaction ~ Days + (Days|Subject), sleepstudy)) m4 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) m5 <- suppressWarnings(glmer(Reaction ~ Days + (Days|Subject), sleepstudy, family=gaussian)) expect_equal(fixef(m3),fixef(m5)) ## hack call -- comes out unimportantly different m4@call[[1]] <- quote(lme4::lmer) expect_equal(m3,m4) expect_equal(m3,m5) ## would like m3==m5 != m4 ?? VarCorr(m4) VarCorr(m5) ## wrong??? is this the report or the getME(m4,"theta") getME(m5,"theta") lme4/tests/test-all.Rout0000644000176000001440000001615612205470767014730 0ustar ripleyusers R Under development (unstable) (2013-08-22 r63654) -- "Unsuffered Consequences" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > if(require("testthat", quietly = TRUE)) { + pkg <- "lme4" + require(pkg, character.only=TRUE, quietly=TRUE) + test_package(pkg) + } else { + print( "package 'testthat' not available, cannot run unit tests" ) + } testing bounds : storing warnings, convergence status, etc. : 12 factor handling in grouping variables : ... data= argument and formula evaluation : ...................................... fitting glmer models : ..................3............... glmFamily linkInv and muEta : ................................................................ glmFamily linkFun and variance : ........................................................ glmFamily devResid and aic : ........................ negative binomial : ........... Errors and warnings from glmer : ........ fitting lmer models : .......4...............56.....7.89 lmerResp objects : .......... glmResp objects : ................ anova : .. atesting fixed-effect design matrices for full rank : ......b residuals : ................. specifying starting values : cd . 1. Failure: storewarning ------------------------------------------------------- gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial, control = glmerControl(optimizer = "Nelder_Mead", maxfun = 3)) no warnings given 2. Error: storewarning --------------------------------------------------------- object 'gm1' not found 1: expect_equal(gm1@optinfo$warnings[[1]], "failure to converge in 3 evaluations") 2: expect_that(object, equals(expected, label = expected.label, ...), info = info, label = label) 3: condition(object) 4: all.equal(expected, actual, ...) 5: all.equal.character(expected, actual, ...) 6: attr.all.equal(target, current, ...) 7: mode(current) 8: .handleSimpleError(function (e) { e$calls <- head(sys.calls()[-seq_len(frame + 7)], -2) signalCondition(e) }, "object 'gm1' not found", quote(mode(current))) 3. Failure: glmer -------------------------------------------------------------- glFormula(cbind(incidence, size - incidence) ~ period + (1 | herd), data = subset(cbpp, herd == levels(herd)[1]), family = binomial) does not match 'must have > 1'. Actual value: Error in checkAtAssignment("numeric", "x", "numeric") : ‘x’ is not a slot in class “numeric†4. Failure: lmer --------------------------------------------------------------- VarCorr(fm1)[[1]][1, 1] not equal to 1764.07265427677 Mean relative difference: 2.302304e-05 5. Failure: lmer --------------------------------------------------------------- VarCorr(fm2)[[1]][1, 1] not equal to expected Mean absolute difference: 9.935674 6. Failure: lmer --------------------------------------------------------------- getME(fm2, "theta") not equal to expected Mean absolute difference: 0.8483203 7. Failure: lmer --------------------------------------------------------------- theta <- getME(fm1, "theta") not equal to expected Mean relative difference: 1.151159e-05 8. Failure: lmer --------------------------------------------------------------- as(Lambdat, "matrix") not equal to expected Mean relative difference: 0.1128579 9. Error: lmer ----------------------------------------------------------------- undefined columns selected 1: expect_that(fm3 <- lmer(Reaction ~ Days + (1 | Subject) + (0 + Days | Subject), sleepstudy), is_a("lmerMod")) 2: condition(object) 3: str_c(class(x), collapse = ", ") 4: Filter(function(x) length(x) > 0, list(...)) 5: unlist(lapply(x, f)) 6: lapply(x, f) 7: lmer(Reaction ~ Days + (1 | Subject) + (0 + Days | Subject), sleepstudy) 8: eval(mc, parent.frame(1L)) 9: eval(expr, envir, enclos) 10: lme4::lFormula(formula = Reaction ~ Days + (1 | Subject) + (0 + Days | Subject), data = sleepstudy, control = structure(list(optimizer = "Nelder_Mead", restart_edge = TRUE, checkControl = structure(list(check.nobs.vs.rankZ = "stopSmall", check.numlev.gtreq.5 = "warning", check.numlev.gtr.1 = "stop"), .Names = c("check.nobs.vs.rankZ", "check.numlev.gtreq.5", "check.numlev.gtr.1")), optControl = list()), .Names = c("optimizer", "restart_edge", "checkControl", "optControl"), class = c("lmerControl", "merControl"))) 11: checkNlevels(reTrms$flist[!reTrms$special], n = n, control) 12: unlist(lapply(flist, function(x) nlevels(droplevels(x)))) 13: lapply(flist, function(x) nlevels(droplevels(x))) 14: reTrms$flist[!reTrms$special] 15: `[.data.frame`(reTrms$flist, !reTrms$special) 16: stop("undefined columns selected") 17: .handleSimpleError(function (e) { e$calls <- head(sys.calls()[-seq_len(frame + 7)], -2) signalCondition(e) }, "undefined columns selected", quote(`[.data.frame`(reTrms$flist, !reTrms$special))) a. Error: RZX is being calculated properly ------------------------------------- unique() applies only to vectors 1: glmer(fr, data = randdata00, family = binomial) 2: eval(mc, parent.frame(1L)) 3: eval(expr, envir, enclos) 4: lme4::glFormula(formula = fr, data = randdata00, family = binomial) 5: mkReTrms(findbars(formula[[3]]), fr) 6: lapply(bars, mkReTrm, fr = fr) 7: FUN(X[[1L]], ...) 8: getGrouping(bar, fr) 9: eval(substitute(factor(fac), list(fac = bar[[3]])), frloc) 10: eval(expr, envir, enclos) 11: factor(~plot) 12: unique(x, nmax = nmax) 13: unique.default(x, nmax = nmax) 14: .handleSimpleError(function (e) { e$calls <- head(sys.calls()[-seq_len(frame + 7)], -2) signalCondition(e) }, "unique() applies only to vectors", quote(unique.default(x, nmax = nmax))) b. Error: nlmerRank ------------------------------------------------------------ Lambdax size mismatch 1: nlmer(y ~ fModf(a, b) ~ a | r, d, start = c(a = 1, b = 1)) 2: devfun(rho$pp$theta) 3: stop(structure(list(message = "Lambdax size mismatch", call = NULL, cppstack = NULL), .Names = c("message", "call", "cppstack"), class = c("std::invalid_argument", "C++Error", "error", "condition"))) c. Error: lmer ----------------------------------------------------------------- unused argument (maxfun = 50) 1: lmerControl(maxfun = 50) 2: .handleSimpleError(function (e) { e$calls <- head(sys.calls()[-seq_len(frame + 7)], -2) signalCondition(e) }, "unused argument (maxfun = 50)", quote(lmerControl(maxfun = 50))) d. Error: glmer ---------------------------------------------------------------- unused argument (maxfun = 50) 1: glmerControl(maxfun = 50) 2: .handleSimpleError(function (e) { e$calls <- head(sys.calls()[-seq_len(frame + 7)], -2) signalCondition(e) }, "unused argument (maxfun = 50)", quote(glmerControl(maxfun = 50))) Error: Test failures Execution halted lme4/tests/falsezero_dorie.R0000644000176000001440000000131312156422373015607 0ustar ripleyusers## test of false zero problem reported by Vince Dorie ## (no longer occurs with current development lme4) ## https://github.com/lme4/lme4/issues/17 library(lme4) sigma.eps <- 2 sigma.the <- 0.75 mu <- 2 n <- 5 J <- 10 g <- gl(J, n) set.seed(1) theta <- rnorm(J, 0, sigma.eps * sigma.the) y <- rnorm(n * J, mu + theta[g], sigma.eps) lmerFit <- lmer(y ~ 1 + (1 | g), REML = FALSE, verbose=TRUE) y.bar <- mean(y) y.bar.j <- sapply(1:J, function(j) mean(y[g == j])) S.w <- sum((y - y.bar.j[g])^2) S.b <- n * sum((y.bar.j - y.bar)^2) R <- S.b / S.w sigma.the.hat <- sqrt(max((n - 1) * R / n - 1 / n, 0)) stopifnot(all.equal(sigma.the.hat,lme4Sigma <- unname(getME(lmerFit,"theta")), tol=2e-5)) lme4/tests/lmer-conv.R0000644000176000001440000000112312156422373014334 0ustar ripleyusers### lmer() convergence testing / monitoring / ... ## ------------------ ### The output of tests here are *not* 'diff'ed (<==> no *.Rout.save file) library(lme4) ## convergence on boundary warnings load(system.file("external/test3comp.rda", package = "Matrix")) b3 <- lmer(Y3 ~ (1|Sample) + (1|Operator/Run), test3comp, verb = TRUE) if (isTRUE(try(data(Early, package = 'mlmRev')) == 'Early')) { Early$tos <- Early$age - 0.5 # time on study b1 <- lmer(cog ~ tos + trt:tos + (tos|id), Early, verb = TRUE) } cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' lme4/tests/vcov-etc.R0000644000176000001440000000450112225036420014152 0ustar ripleyusersstopifnot(require(lme4)) ## "MEMSS" is just 'Suggest' -- must still work, when it's missing: if (suppressWarnings(!require(MEMSS,quietly=TRUE)) || (data(ergoStool, package="MEMSS") != "ergoStool")) { cat("'ergoStool' data from package 'MEMSS' is not available --> skipping test\n") quit('no') } fm1 <- lmer (effort ~ Type + (1|Subject), data = ergoStool) fm1.s <- lmer (effort ~ Type + (1|Subject), data = ergoStool, sparseX=TRUE) ## was segfaulting with sparseX (a while upto 2010-04-06) fe1 <- fixef(fm1) fe1.s <- fixef(fm1.s) s1.d <- summary(fm1) s1.s <- summary(fm1.s) stopifnot( all.equal(fe1, fe1.s, tol= 1e-12 ), all.equal(se1.d <- coef(s1.d)[,"Std. Error"], se1.s <- coef(s1.s)[,"Std. Error"]#, tol = 1e-10 ), all.equal(V.d <- vcov(fm1), V.s <- vcov(fm1.s)#, tol = 1e-9 ), all.equal(Matrix::diag(V.d), unname(se1.d)^2, tol= 1e-12) , all.equal(unname(se1.d), c(0.576011960125099, rep.int(0.518683987372292,3L)), tol = 1.e-8) ) ### -------------------------- a "large" example ------------------------- str(InstEval) if (FALSE) { # sparse X is not currently implemented ## this works system.time( fm7 <- lmer(y ~ d + service + studage + lectage + (1|s), data = InstEval, sparseX=TRUE, verbose=1L, REML=FALSE) ) if (nchar(Sys.getenv("_LME4_LONG_TESTS_")) > 0) { system.time(sfm7 <- summary(fm7)) fm7 # takes a while as it computes summary() again ! range(t.fm7 <- coef(sfm7)[,"t value"])## -10.94173 10.61535 for REML, -11.03438 10.70103 for ML m.t.7 <- mean(abs(t.fm7), trim = .01) #stopifnot(all.equal(m.t.7, 1.55326395545110, tol = 1.e-9)) ##REML value stopifnot(all.equal(m.t.7, 1.56642013605506, tol = 1.e-6)) ## ML hist.t <- cut(t.fm7, floor(min(t.fm7)) : ceiling(max(t.fm7))) cbind(table(hist.t)) system.time(fm8 <- lmer(y ~ service * dept + studage + lectage + (1|s) + (1|d), InstEval, verbose = 1L, REML=FALSE)) fm8 system.time(fm9 <- lmer(y ~ studage + lectage + (1|s) + (1|d) + (1|dept:service) + (1|dept), InstEval, verbose = 1L, REML=FALSE)) fm9 rr <- ranef(fm9, condVar = TRUE) qqmath(rr,strip=FALSE)$d dotplot(rr,strip=FALSE)$`dept:service` } } cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' lme4/tests/nlmer.Rout.save0000644000176000001440000000711012211706636015235 0ustar ripleyusers R version 3.0.1 Patched (2013-08-16 r63595) -- "Good Sport" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: x86_64-unknown-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(lme4) Loading required package: lattice Loading required package: Matrix > (testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) [1] 1 > > allEQ <- function(x,y, tolerance = 4e-4, ...) + all.equal.numeric(x,y, tolerance=tolerance, ...) > > (nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ (Asym|Tree), + Orange, start = c(Asym = 200, xmid = 725, scal = 350))) Nonlinear mixed model fit by maximum likelihood ['nlmerMod'] Formula: circumference ~ SSlogis(age, Asym, xmid, scal) ~ (Asym | Tree) Data: Orange AIC BIC logLik deviance 273.1438 280.9205 -131.5719 263.1438 Random effects: Groups Name Std.Dev. Tree Asym 31.646 Residual 7.843 Number of obs: 35, groups: Tree, 5 Fixed Effects: Asym xmid scal 192.1 727.9 348.1 > fixef(nm1) Asym xmid scal 192.0534 727.9074 348.0738 > > if (testLevel > 2) { + ## 'Theoph' Data modeling + Th.start <- c(lKe = -2.5, lKa = 0.5, lCl = -3) + + system.time(nm2 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ + (lKe+lKa+lCl|Subject), + Theoph, start = Th.start, tolPwrss=1e-8)) + print(nm2, corr=FALSE) + + system.time(nm3 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ + (lKe|Subject) + (lKa|Subject) + (lCl|Subject), + Theoph, start = Th.start)) + print(nm3, corr=FALSE) + + ## dropping lKe from random effects: + system.time(nm4 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ (lKa+lCl|Subject), + Theoph, start = Th.start, tolPwrss=1e-8)) + print(nm4, corr=FALSE) + + system.time(nm5 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ + (lKa|Subject) + (lCl|Subject), + Theoph, + start = Th.start, tolPwrss=1e-8)) + print(nm5, corr=FALSE) + + if (require("PKPDmodels")) { + oral1cptSdlkalVlCl <- + PKmod("oral", "sd", list(ka ~ exp(lka), Cl ~ exp(lCl), V ~ exp(lV))) + if (FALSE) { + ## FIXME: Error in get(nm, envir = nlenv) : object 'k' not found + ## probably with environments/call stack etc.? + ## 'pnames' is c("lV","lka","k") -- not ("lV","lka","lCl") + ## nlmer -> nlformula -> MkRespMod + ## pnames are OK in nlformula, but in MkRespMod we try to recover + ## them from the column names of the gradient attribute of the + ## model evaluated in nlenv -- which are wrong. + system.time(nm2a <- nlmer(conc ~ oral1cptSdlkalVlCl(Dose, Time, lV, lka, lCl) ~ + (lV+lka+lCl|Subject), + Theoph, start = c(lV=-1, lka=-0.5, lCl=-3), tolPwrss=1e-8)) + print(nm2a, corr=FALSE) + } + } + } ## testLevel > 2 > > proc.time() user system elapsed 5.080 0.092 5.187 lme4/tests/prLogistic.Rout0000644000176000001440000000717212056243570015312 0ustar ripleyusers R Under development (unstable) (2012-11-20 r61133) -- "Unsuffered Consequences" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ## data set and formula extracted from ?prLogisticDelta example > ## (Thailand, clustered-data) in prLogistic package > load(system.file("testdata","prLogistic.RData",package="lme4")) > library(lme4) Loading required package: lattice Loading required package: Matrix Loading required package: Rcpp Loading required package: RcppEigen > ## FIXME: un-try() after PIRLS failure addressed > try(glmer(rgi~ sex + pped + (1|schoolid), + data = dataset, family=binomial)) Error in pwrssUpdate(pp, resp, tolPwrss, GQmat, compDev, fac, verbose) : PIRLS step failed > > > if (FALSE) { + library(ggplot2) + dataset$schoolid <- factor(dataset$schoolid) + length(levels(dataset$schoolid)) + ttab <- with(dataset,table(schoolid,sex,rgi,pped)) + ttab2 <- with(dataset,table(tapply(rgi,schoolid, + function(x) ifelse(all(x==0),0, + ifelse(all(x==1),1,0.5))))) + ggplot(dataset,aes(x=sex,y=rgi,colour=factor(pped)))+stat_sum(alpha=0.5) + + ## library(glmmML) + ## glmmML_fit <- glmmML(rgi~ sex + pped , cluster=schoolid, + ## data = dataset, family=binomial) + ## glmmML_est <- list(sigma=glmmML_fit$sigma,beta=coef(glmmML_fit)) + ## dput(glmmML_est) + glmmML_est <- structure(list(sigma = 1.25365353546143, + beta = structure(c(-2.19478801858317, + 0.548884468743364, -0.623835613907385), .Names = c("(Intercept)", + "sex", "pped"))), + .Names = c("sigma", "beta")) + + ## library(lme4.0) + ## lme4.0_fit <- glmer(rgi~ sex + pped + (1|schoolid), + ## data = dataset, family=binomial) + ## lme4.0_est <- list(sigma=unname(sqrt(unlist(VarCorr(lme4.0_fit)))), + ## beta=fixef(lme4.0_fit)) + ## dput(lme4.0_est) + ## detach("package:lme4.0") + lme4.0_est <- structure(list(sigma = 1.25369539060849, beta = structure(c(-2.19474529099587, + 0.548900267825802, -0.623934772981894), .Names = c("(Intercept)", + "sex", "pped"))), .Names = c("sigma", "beta")) + + devfun <- glmer(rgi~ sex + pped + (1|schoolid), + data = dataset, family=binomial, + devFunOnly=TRUE) + with(glmmML_est,devfun(c(sigma,beta))) + with(glmmML_est,devfun(c(sigma,beta))) ## FAILS + devfun <- glmer(rgi~ sex + pped + (1|schoolid), + data = dataset, family=binomial, + devFunOnly=TRUE) + with(lme4.0_est,devfun(c(sigma,beta))) ## 6326.456 + g3 <- with(glmmML_est,glmer(rgi~ sex + pped + (1|schoolid), + data = dataset, family=binomial, + start=list(theta=sigma,fixef=beta)), + verbose=10) + } > > proc.time() user system elapsed 5.704 2.884 8.463 lme4/tests/drop.R0000644000176000001440000000104212156422373013376 0ustar ripleyuserslibrary(lme4) fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) ## slightly weird model but plausible --- not that ## one would want to try drop1() on this model ... fm2 <- lmer(Reaction ~ 1+ (Days|Subject), sleepstudy) drop1(fm2) ## empty update(fm1, . ~ . - Days) anova(fm2) ## empty terms(fm1) terms(fm1,fixed.only=FALSE) extractAIC(fm1) drop1(fm1) drop1(fm1, test="Chisq") gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp, nAGQ=25L) drop1(gm1, test="Chisq") lme4/tests/fewlevels.R0000644000176000001440000000147312232467515014440 0ustar ripleyusers## example from Gabor Grothendieck; didn't ## https://stat.ethz.ch/pipermail/r-sig-mixed-models/2010q2/003726.html ## library(nlme) ## set.seed(1) ## f <- function(n, k) { ## set.seed(1) ## x <- 1:n ## fac <- gl(k, 1, n) ## fac.eff <- rnorm(k, 0, 4)[fac] ## e <- rnorm(n) ## y <- 1 + 2 * x + fac.eff + e ## lme(y ~ x, random = ~ 1 | fac) ## } ## n <- 10000 sim <- function(n=1e4,k=4, seed=1) { set.seed(seed) x <- 1:n fac <- gl(k, 1, n) fac.eff <- rnorm(k, 0, 4)[fac] e <- rnorm(n) y <- 1 + 2 * x + fac.eff + e data.frame(x,y,fac) } library(nlme) m.lme <- lme(y ~ x, random=~ 1|fac ,data=sim()) v.lme <- as.numeric(VarCorr(m.lme)[1,1]) library(lme4) m.lmer <- lmer(y ~ x + (1|fac),data=sim()) v.lmer <- VarCorr(m.lmer)[[1]][1,1] stopifnot(all.equal(v.lmer,19.54829,tol=1e-6)) lme4/tests/test_times.R0000644000176000001440000000072612051772014014614 0ustar ripleyuserslibrary(lme4) ## utility for timing individual tests testfiles <- setdiff(list.files(pattern="*\\.[Rr]$"),"test_times.R") tmpf <- function(x) { cat("*** ",x,"\n") system.time(local(source(x,echo=FALSE)))["elapsed"] } fn <- "test_timings.RData" if (!file.exists(fn)) { times <- sapply(testfiles,tmpf) save("times",file=fn) } if (FALSE) { load(fn) pdf("timings.pdf",width=5,height=8) dotchart(sort(times)) abline(v=c(2,10)) dev.off() } lme4/tests/priorWeights.R0000644000176000001440000000116712204271665015130 0ustar ripleyuserslibrary("lme4") set.seed(2) n <- 40 w <- runif(n) x <- runif(n) g <- factor(sample(1:10,n,replace=TRUE)) Z <- model.matrix(~g-1); y <- Z%*%rnorm(ncol(Z)) + x + rnorm(n)/w^.5 m <- lmer(y~x+(1|g),weights=w, REML = TRUE) fixef_lme4.0 <- c(-0.730654, 2.028954) stopifnot(all.equal(unname(fixef(m)), fixef_lme4.0, tol = 10^-3)) sigma_lme4.0 <- 1.736143 stopifnot(all.equal(sigma(m), sigma_lme4.0, tol = 10^-3)) Sigma_lme4.0 <- 2.356705 stopifnot(all.equal(as.vector(VarCorr(m)$g), Sigma_lme4.0, tol = 10^-3)) SE_lme4.0 <- c(0.9507008, 1.3765086) stopifnot(all.equal(as.vector(summary(m)$coefficients[,2]), SE_lme4.0, tol = 10^-3)) lme4/tests/napredict.R0000644000176000001440000000521012204271301014367 0ustar ripleyuserslibrary(lme4) library(testthat) cake2 <- rbind(cake,tail(cake,1)) cake2[nrow(cake2),"angle"] <- NA fm0 <- lmer(angle ~ recipe * temperature + (1|recipe:replicate), cake) fm1 <- update(fm0,data=cake2) expect_that(update(fm1,na.action=na.fail),throws_error("missing values in object")) fm1_omit <- update(fm1,na.action=na.omit) ## check equal: expect_true(all.equal(fixef(fm0),fixef(fm1))) expect_true(all.equal(VarCorr(fm0),VarCorr(fm1))) expect_true(all.equal(ranef(fm0),ranef(fm1))) ## works, but doesn't make much sense fm1_pass <- update(fm1,na.action=na.pass) expect_true(all(is.na(fitted(fm1_pass)))) fm1_exclude <- update(fm1,na.action=na.exclude) ## FIXME: fails on Windows (not on Linux!) expect_true(is.na(tail(predict(fm1_exclude),1))) ## test predict.lm d <- data.frame(x=1:10,y=c(rnorm(9),NA)) lm1 <- lm(y~x,data=d,na.action=na.exclude) predict(lm1) predict(lm1,newdata=data.frame(x=c(1:4,NA))) ## Triq examples ... m.lmer <- lmer (angle ~ temp + (1 | recipe) + (1 | replicate), data=cake) # Create new data frame with some NAs in fixed effect cake2 <- cake cake2$temp[1:5] <- NA p1_pass <- predict(m.lmer, newdata=cake2, REform=NA, na.action=na.pass) expect_true(length(p1_pass)==nrow(cake2)) expect_true(all(is.na(p1_pass[1:5]))) p1_omit <- predict(m.lmer, newdata=cake2, REform=NA, na.action=na.omit) p1_exclude <- predict(m.lmer, newdata=cake2, REform=NA, na.action=na.exclude) expect_true(length(p1_omit)==nrow(na.omit(cake2))) expect_true(all.equal(p1_exclude,p1_omit)) expect_that(predict(m.lmer, newdata=cake2, REform=NA, na.action=na.fail), throws_error("missing values in object")) ## now try it with REform==NULL p2_pass <- predict(m.lmer, newdata=cake2, REform=NULL, na.action=na.pass) expect_true(length(p2_pass)==nrow(cake2)) expect_true(all(is.na(p2_pass[1:5]))) p2_omit <- predict(m.lmer, newdata=cake2, REform=NULL, na.action=na.omit) p2_exclude <- predict(m.lmer, newdata=cake2, REform=NULL, na.action=na.exclude) expect_true(length(p2_omit)==nrow(na.omit(cake2))) expect_true(all.equal(p2_exclude,p2_omit)) expect_that(predict(m.lmer, newdata=cake2, REform=NULL, na.action=na.fail), throws_error("missing values in object")) ## experiment with NA values in random effects -- should get ## treated cake3 <- cake cake3$replicate[1:5] <- NA expect_that(predict(m.lmer, newdata=cake3, REform=NULL), throws_error("NAs are not allowed in prediction data")) p4 <- predict(m.lmer, newdata=cake3, REform=NULL, allow.new.levels=TRUE) p4B <- predict(m.lmer, newdata=cake3, REform=~1|recipe, allow.new.levels=TRUE) expect_true(all.equal(p4[1:5],p4B[1:5])) p4C <- predict(m.lmer, newdata=cake3, REform=NA) lme4/tests/prLogistic.R0000644000176000001440000000245312172000024014537 0ustar ripleyusers## data set and formula extracted from ?prLogisticDelta example ## (Thailand, clustered-data) in prLogistic package load(system.file("testdata","prLogistic.RData",package="lme4")) library(lme4) (testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) if (testLevel > 2) { lme4_est <- glmer(rgi ~ sex + pped + (1|schoolid), data = dataset, family=binomial) lme4_results <- list(sigma= sqrt(unname(unlist(VarCorr(lme4_est)))), beta = fixef(lme4_est)) ## stored results from other pkgs glmmML_est <- structure(list(sigma = 1.25365353546143, beta = structure(c(-2.19478801858317, 0.548884468743364, -0.623835613907385), .Names = c("(Intercept)", "sex", "pped"))), .Names = c("sigma", "beta")) lme4.0_est <- structure(list(sigma = 1.25369539060849, beta = structure(c(-2.19474529099587, 0.548900267825802, -0.623934772981894), .Names = c("(Intercept)", "sex", "pped"))), .Names = c("sigma", "beta")) stopifnot(all.equal(lme4_results,glmmML_est,tol=3e-3)) stopifnot(all.equal(lme4_results,lme4.0_est,tol=3e-3)) } lme4/tests/predict_basis.R0000644000176000001440000000303512232467522015251 0ustar ripleyusers## test for models containing data-defined bases ## ?makepredictcall ## ?model.frame ## ???? data(sleepstudy,package="lme4") library(splines) ## lm0 <- lm(Reaction~ns(Days,2),sleepstudy) ## attr(terms(lm0),"predvars") ## library(nlme) ## lme1 <- lme(Reaction~ns(Days,2),random=~1|Subject,sleepstudy) ## attr(terms(lme1),"predvars") ## no! ## attr(lme1$terms,"predvars") ## yes ## detach("package:nlme") library(lme4) library(testthat) fm1 <- lmer(Reaction ~ ns(Days,2) + (1|Subject), sleepstudy) fm2 <- lmer(Reaction ~ poly(Days,2) + (1|Subject), sleepstudy) fm3 <- lmer(Reaction ~ poly(Days,2,raw=TRUE) + (1|Subject), sleepstudy) newdat0 <- data.frame(Days=unique(sleepstudy$Days)) newdat <- data.frame(Days=5:12) tmpf <- function(fit) { with(sleepstudy,plot(Reaction~Days,xlim=c(0,12))) with(sleepstudy,points(Days,predict(fit),col=2)) with(newdat0,lines(Days,predict(fit,REform=NA,newdata=newdat0),col=4)) with(newdat,lines(Days,predict(fit,REform=NA,newdata=newdat),col=5)) } stopifnot(all.equal(predict(fm2,newdat,REform=NA), predict(fm3,newdat,REform=NA))) ## pictures tmpf(fm1) tmpf(fm2) tmpf(fm3) ## test for GLMMs set.seed(101) d <- data.frame(y=rbinom(10,size=1,prob=0.5), x=1:10, f=factor(rep(1:5,each=2))) gm1 <- glmer(y ~ poly(x,2) + (1|f), d, family=binomial) gm2 <- glmer(y ~ poly(x,2,raw=TRUE) + (1|f), d, family=binomial) newdat <- data.frame(x=c(1,4,6)) stopifnot(all.equal(predict(gm1,newdat,REform=NA), predict(gm2,newdat,REform=NA),tol=3e-6)) lme4/tests/glmer-1.Rout0000644000176000001440000002662012273465722014444 0ustar ripleyusers R Under development (unstable) (2012-11-20 r61133) -- "Unsuffered Consequences" Copyright (C) 2012 The R Foundation for Statistical Computing ISBN 3-900051-07-0 Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > ## generalized linear mixed model > stopifnot(suppressPackageStartupMessages(require(lme4))) > options(show.signif.stars = FALSE) > testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1 > > source(system.file("test-tools-1.R", package = "Matrix"), keep.source = FALSE) > ## > ##' Check that coefficient +- "2" * SD contains true value > ##' > ##' @title Check that confidence interval for coefficients contains true value > ##' @param fm fitted model, e.g., from lm(), lmer(), glmer(), .. > ##' @param true.coef numeric vector of true (fixed effect) coefficients > ##' @param conf.level confidence level for confidence interval > ##' @param sd.factor the "2", i.e. default 1.96 factor for the confidence interval > ##' @return TRUE or a string of "error" > ##' @author Martin Maechler > chkFixed <- function(fm, true.coef, conf.level = 0.95, + sd.factor = qnorm((1+conf.level)/2)) + { + stopifnot(is.matrix(cf <- coefficients(summary(fm))), ncol(cf) >= 2) + cc <- cf[,1] + sd <- cf[,2] + if(any(out1 <- true.coef < cc - sd.factor*sd)) + return(sprintf("true coefficient[j], j=%s, is smaller than lower confidence limit", + paste(which(out1), collapse=", "))) + if(any(out2 <- true.coef > cc + sd.factor*sd)) + return(sprintf("true coefficient[j], j=%s, is larger than upper confidence limit", + paste(which(out2), collapse=", "))) + ## else, return + TRUE + } > > > ## TODO: (1) move these to ./glmer-ex.R [DONE] > ## ---- (2) "rationalize" with ../man/cbpp.Rd > #m1e <- glmer1(cbind(incidence, size - incidence) ~ period + (1 | herd), > # family = binomial, data = cbpp, doFit = FALSE) > ## now > #bobyqa(m1e, control = list(iprint = 2L)) > > m1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), + family = binomial, data = cbpp) > ## response as a vector of probabilities and usage of argument "weights" > m1p <- glmer(incidence / size ~ period + (1 | herd), weights = size, + family = binomial, data = cbpp) > ## Confirm that these are equivalent: > stopifnot(all.equal(fixef(m1), fixef(m1p)), + all.equal(ranef(m1), ranef(m1p)), + TRUE) > ## for(m in c(m1, m1p)) { > ## cat("-------\\n\\nCall: ", > ## paste(format(getCall(m)), collapse="\\n"), "\\n") > ## print(logLik(m)); cat("AIC:", AIC(m), "\\n") ; cat("BIC:", BIC(m),"\\n") > ## } > stopifnot(all.equal(logLik(m1), logLik(m1p)), + all.equal(AIC(m1), AIC(m1p)), + all.equal(BIC(m1), BIC(m1p))) > > > m1b <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), + optimizer="bobyqa", + family = binomial, data = cbpp, verbose = 2L, + control = list(rhobeg=0.2, rhoend=2e-7), tolPwrss=1e-8) > > ## using nAGQ=9L provides a better evaluation of the deviance > m.9 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), + family = binomial, data = cbpp, nAGQ = 9) > > ## check with nAGQ = 25 > m2 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), + family = binomial, data = cbpp, nAGQ = 25) > > ## loosened tolerance on parameters > stopifnot(is((cm2 <- coef(m2)), "coef.mer"), + dim(cm2$herd) == c(15,4), + all.equal(fixef(m2), + ### lme4a [from an Ubuntu 11.10 amd64 system] + c(-1.39922533406847, -0.991407294757321, + -1.12782184600404, -1.57946627431248), + ##c(-1.3766013, -1.0058773, + ## -1.1430128, -1.5922817), + tol = 5.e-4, + check.attributes=FALSE), + all.equal(deviance(m2), 100.010030538022, tol=1e-9) + ## with bobyqa first (AGQ=0), then + ##all.equal(deviance(m2), 101.119749563, tol=1e-9) + ) > > ## 32-bit Ubuntu 10.04: > coef_m1_lme4.0 <- structure(c(-1.39853505102576, + -0.992334712470269, -1.12867541092127, + -1.58037389566025), + .Names = c("(Intercept)", "period2", "period3", + "period4")) > > ## library(glmmADMB) > ## mg <- glmmadmb(cbind(incidence, size - incidence) ~ period + (1 | herd), > ## family = "binomial", data = cbpp) > coef_m1_glmmadmb <- structure(c(-1.39853810064827, -0.99233330126975, -1.12867317840779, + -1.58031150854503), .Names = c("(Intercept)", "period2", "period3", + "period4")) > > ## library(glmmML) > ## mm <- glmmML(cbind(incidence, size - incidence) ~ period, > ## cluster=herd, > ## family = "binomial", data = cbpp) > coef_m1_glmmML <- structure(c(-1.39853234657711, -0.992336901732793, -1.12867036466201, + -1.58030977686564), .Names = c("(Intercept)", "period2", "period3", + "period4")) > > ## lme4[r 1636], 64-bit ubuntu 11.10: > ## c(-1.3788385, -1.0589543, > ## -1.1936382, -1.6306271), > > stopifnot(is((cm1 <- coef(m1b)), "coef.mer"), + dim(cm1$herd) == c(15,4), + all.equal(fixef(m1b),fixef(m1),tol=4e-5), + is.all.equal4(fixef(m1b), + coef_m1_glmmadmb, + coef_m1_lme4.0, + coef_m1_glmmML, + tol = 4e-5) + ) > > > ## Deviance for the new algorithm is lower, eventually we should change the previous test > ##stopifnot(deviance(m1) <= deviance(m1e)) > > showProc.time() # Time elapsed: 1.476 0.172 1.692 > > if (require('MASS', quietly = TRUE)) { + bacteria$wk2 <- bacteria$week > 2 + contrasts(bacteria$trt) <- + structure(contr.sdif(3), + dimnames = list(NULL, c("diag", "encourage"))) + print(fm5 <- glmer(y ~ trt + wk2 + (1|ID), + data=bacteria, family=binomial)) + showProc.time() # + + stopifnot( + all.equal(logLik(fm5), + ## was -96.127838 + structure(-96.13069, nobs = 220L, nall = 220L, + df = 5L, REML = FALSE, + class = "logLik"), + tol = 1e-5, check.attributes = FALSE) + , + all.equal(fixef(fm5), + ## was 2.834218798 -1.367099481 + c("(Intercept)"= 2.831609490, "trtdiag"= -1.366722631, + ## now 0.5842291915, -1.599148773 + "trtencourage"=0.5840147802, "wk2TRUE"=-1.598591346), tol = 1e-4) + ) + } Generalized linear mixed model fit by maximum likelihood ['glmerMod'] Family: binomial ( logit ) Formula: y ~ trt + wk2 + (1 | ID) Data: bacteria AIC BIC logLik deviance 202.2616 219.2297 -96.1308 192.2616 Random effects: Groups Name Variance Std.Dev. ID (Intercept) 1.543 1.242 Number of obs: 220, groups: ID, 50 Fixed effects: Estimate Std. Error z value Pr(>|z|) (Intercept) 2.8316 0.4466 6.341 2.28e-10 trtdiag -1.3667 0.6565 -2.082 0.037368 trtencourage 0.5840 0.6734 0.867 0.385803 wk2TRUE -1.5985 0.4612 -3.466 0.000529 Correlation of Fixed Effects: (Intr) trtdig trtncr trtdiag -0.044 trtencourag 0.064 -0.495 wk2TRUE -0.794 0.063 -0.056 Time elapsed: 0.596 0.084 0.685 > > ## Failure to specify a random effects term - used to give an obscure message > ## Ensure *NON*-translated message; works on Linux,... : > if(.Platform$OS.type == "unix") { + Sys.setlocale("LC_MESSAGES", "C") + tc <- tryCatch( + m2 <- glmer(incidence / size ~ period, weights = size, + family = binomial, data = cbpp) + , error = function(.) .) + stopifnot(inherits(tc, "error"), + identical(tc$message, + "No random effects terms specified in formula")) + } > > > ## glmer - Modeling overdispersion as "mixture" aka > ## ----- - *ONE* random effect *PER OBSERVATION" -- example inspired by Ben Bolker: > > ##' > ##' > ##'

> ##' @title > ##' @param ng number of groups > ##' @param nr number of "runs", i.e., observations per groups > ##' @param sd standard deviations of group and "Individual" random effects, > ##' (\sigma_f, \sigma_I) > ##' @param b true beta (fixed effects) > ##' @return a data frame (to be used in glmer()) with columns > ##' (x, f, obs, eta0, eta, mu, y), where y ~ Pois(lambda(x)), > ##' log(lambda(x_i)) = b_1 + b_2 * x + G_{f(i)} + I_i > ##' and G_k ~ N(0, \sigma_f); I_i ~ N(0, \sigma_I) > ##' @author Ben Bolker and Martin Maechler > rPoisGLMMi <- function(ng, nr, sd=c(f = 1, ind = 0.5), b=c(1,2)) + { + stopifnot(nr >= 1, ng >= 1, + is.numeric(sd), names(sd) %in% c("f","ind"), sd >= 0) + ntot <- nr*ng + b.reff <- rnorm(ng, sd= sd[["f"]]) + b.rind <- rnorm(ntot,sd= sd[["ind"]]) + x <- runif(ntot) + within(data.frame(x, + f = factor(rep(LETTERS[1:ng], each=nr)), + obs = 1:ntot, + eta0 = cbind(1, x) %*% b), + { + eta <- eta0 + b.reff[f] + b.rind[obs] + mu <- exp(eta) + y <- rpois(ntot, lambda=mu) + }) + } > > set.seed(1) > dd <- rPoisGLMMi(12, 20) > m0 <- glmer(y~x + (1|f), family="poisson", data=dd) > m1 <- glmer(y~x + (1|f) + (1|obs), family="poisson", data=dd) > stopifnot(isTRUE(chkFixed(m0, true.coef = c(1,2))), + isTRUE(chkFixed(m1, true.coef = c(1,2)))) > (a01 <- anova(m0, m1)) Data: dd Models: m0: y ~ x + (1 | f) m1: y ~ x + (1 | f) + (1 | obs) Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq) m0 3 2153.5 2164.0 -1073.77 2147.5 m1 4 1601.2 1615.1 -796.61 1593.2 554.33 1 < 2.2e-16 > > stopifnot(all.equal(a01$Chisq[2], 554.334056, tol=1e-5), + all.equal(a01$logLik, c(-1073.77193, -796.604902), tol=1e-6), + a01$ Df == 3:4, + a01$`Chi Df`[2] == 1) > > if (testLevel>1) { + nsim <- 10 + set.seed(2) + system.time( + simR <- lapply(1:nsim, function(i) { + cat(i,"", if(i %% 20 == 0)"\n") + dd <- rPoisGLMMi(10 + rpois(1, lambda=3), + 16 + rpois(1, lambda=5)) + m0 <- glmer(y~x + (1|f), family="poisson", data=dd) + m1 <- glmer(y~x + (1|f) + (1|obs), family="poisson", data=dd) + a01 <- anova(m0, m1) + stopifnot(a01$ Df == 3:4, + a01$`Chi Df`[2] == 1) + list(chk0 = chkFixed(m0, true.coef = c(1,2)), + chk1 = chkFixed(m1, true.coef = c(1,2)), + chisq= a01$Chisq[2], + lLik = a01$logLik) + })) + } > > ## m0 is the wrong model, so we don't expect much here: > table(unlist(lapply(simR, `[[`, "chk0"))) Error in lapply(simR, `[[`, "chk0") : object 'simR' not found Calls: table -> unlist -> lapply Execution halted lme4/tests/predict.R0000644000176000001440000001140112204271301014047 0ustar ripleyuserslibrary(lme4) library(testthat) do.plots <- FALSE gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) ## fitted values p0 <- predict(gm1) p0B <- predict(gm1,newdata=cbpp) expect_true(all.equal(p0,unname(p0B),tol=2e-5)) ## FIXME: why not closer? ## fitted values, unconditional (level-0) p1 <- predict(gm1,REform=NA) expect_true(length(unique(p1))==length(unique(cbpp$period))) if (do.plots) matplot(cbind(p0,p1),col=1:2,type="b") newdata <- with(cbpp,expand.grid(period=unique(period),herd=unique(herd))) ## new data, all RE p2 <- predict(gm1,newdata) ## new data, level-0 p3 <- predict(gm1,newdata,REform=NA) ## explicitly specify RE p4 <- predict(gm1,newdata,REform=~(1|herd)) expect_true(all.equal(p2,p4)) p5 <- predict(gm1,type="response") expect_true(all.equal(p5,plogis(p0))) if (do.plots) matplot(cbind(p2,p3),col=1:2,type="b") ## effects of new RE levels newdata2 <- rbind(newdata, data.frame(period=as.character(1:4),herd=rep("new",4))) expect_true(is(try(predict(gm1,newdata2),silent=TRUE),"try-error")) p6 <- predict(gm1,newdata2,allow.new.levels=TRUE) expect_true(all.equal(p2,p6[1:length(p2)])) ## original values should match ## last 4 values should match unconditional values expect_true(all(tail(p6,4)==predict(gm1,newdata=data.frame(period=factor(1:4)),REform=NA))) ## multi-group model fm1 <- lmer(diameter ~ (1|plate) + (1|sample), Penicillin) ## fitted values p0 <- predict(fm1) ## fitted values, unconditional (level-0) p1 <- predict(fm1,REform=NA) if (do.plots) matplot(cbind(p0,p1),col=1:2,type="b") newdata <- with(Penicillin,expand.grid(plate=unique(plate),sample=unique(sample))) ## new data, all RE p2 <- predict(fm1,newdata) ## new data, level-0 p3 <- predict(fm1,newdata,REform=NA) ## explicitly specify RE p4 <- predict(fm1,newdata,REform=~(1|plate)+(~1|sample)) p4B <- predict(fm1,newdata,REform=~(1|sample)+(~1|plate)) expect_true(all.equal(p2,p4,p4B)) p5 <- predict(fm1,newdata,REform=~(1|sample)) p6 <- predict(fm1,newdata,REform=~(1|plate)) if (do.plots) matplot(cbind(p2,p3,p5,p6),type="b",lty=1,pch=16) fm2 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) p0 <- predict(fm2) p1 <- predict(fm2,REform=NA) ## linear model, so results should be identical patterns but smaller -- ## not including intermediate days newdata <- with(sleepstudy,expand.grid(Days=range(Days),Subject=unique(Subject))) newdata$p2 <- predict(fm2,newdata) newdata$p3 <- predict(fm2,newdata,REform=NA) newdata$p4 <- predict(fm2,newdata,REform=~(0+Days|Subject)) newdata$p5 <- predict(fm2,newdata,REform=~(1|Subject)) ## reference values from an apparently-working run refval <- structure(list(Days = c(0, 9, 0, 9, 0, 9), Subject = structure(c(1L, 1L, 2L, 2L, 3L, 3L), .Label = c("308", "309", "310", "330", "331", "332", "333", "334", "335", "337", "349", "350", "351", "352", "369", "370", "371", "372"), class = "factor"), p2 = c(253.663652396798, 430.66001930835, 211.006415533628, 227.634788908917, 212.444742696829, 257.61053840953), p3 = c(251.405104848485, 345.610678484848, 251.405104848485, 345.610678484848, 251.405104848485, 345.610678484848 ), p4 = c(251.405104848485, 428.401471760037, 251.405104848485, 268.033478223774, 251.405104848485, 296.570900561186), p5 = c(253.663652396798, 347.869226033161, 211.006415533628, 305.211989169991, 212.444742696829, 306.650316333193)), .Names = c("Days", "Subject", "p2", "p3", "p4", "p5"), out.attrs = structure(list(dim = structure(c(2L, 18L), .Names = c("Days", "Subject")), dimnames = structure(list( Days = c("Days=0", "Days=9"), Subject = c("Subject=308", "Subject=309", "Subject=310", "Subject=330", "Subject=331", "Subject=332", "Subject=333", "Subject=334", "Subject=335", "Subject=337", "Subject=349", "Subject=350", "Subject=351", "Subject=352", "Subject=369", "Subject=370", "Subject=371", "Subject=372")), .Names = c("Days", "Subject"))), .Names = c("dim", "dimnames")), row.names = c(NA, 6L), class = "data.frame") expect_true(all.equal(head(newdata),refval)) library(lattice) tmpf <- function(data,...) { data$Reaction <- predict(fm2,data,...) if (do.plots) xyplot(Reaction~Days,group=Subject,data=data,type="l") } tmpf(sleepstudy) tmpf(sleepstudy,REform=NA) tmpf(sleepstudy,REform=~(0+Days|Subject)) tmpf(sleepstudy,REform=~(1|Subject)) ## from 'Colonel Triq': examples using *fewer* random effect levels ## than in original data set m <- lmer(angle ~ temp + recipe + (1 | replicate), data=cake) summary(m) # replicate 1 only appears in rows 1:18. rownames(cake[cake$replicate==1,]) predict(m, newdata=cake[-1:-17,], REform=~ (1 | replicate)) predict(m, newdata=cake[-1:-18,], REform=NA) predict(m, newdata=cake[-1:-18,], REform=~ (1 | replicate)) predict(m, newdata=cake[-1:-18,], REform=~ (1 | replicate), allow.new.levels=TRUE) lme4/tests/agridat_gotway.R0000644000176000001440000000356512213677421015453 0ustar ripleyuserslibrary(testthat) ## require(agridat) ## dat <- gotway.hessianfly load(system.file("testdata","gotway_hessianfly.rda",package="lme4")) # Block random. See Glimmix manual, output 1.18. # Note: (Different parameterization) ## require("lme4.0") ## fit2 <- glmer(cbind(y, n-y) ~ gen + (1|block), data=dat, family=binomial) ## params <- list(fixef=fixef(fit2),theta=getME(fit2,"theta")) ## detach("package:lme4.0") lme4.0fit <- structure(list(fixef = structure(c(1.50345713031203, -0.193853259383803, -0.540808391060274, -1.43419379979154, -0.203701042949808, -0.978322555343941, -0.604078624475678, -1.67742449813309, -1.39842466673692, -0.681709344788684, -1.46295367186169, -1.45908310198959, -3.55285756517073, -2.50731975980307, -2.08716296677356, -2.96974270029992), .Names = c("(Intercept)", "genG02", "genG03", "genG04", "genG05", "genG06", "genG07", "genG08", "genG09", "genG10", "genG11", "genG12", "genG13", "genG14", "genG15", "genG16")), theta = structure(0.0319087494293615, .Names = "block.(Intercept)")), .Names = c("fixef", "theta")) ## start doesn't work because we don't get there library(lme4) m1 <- glmer(cbind(y, n-y) ~ gen + (1|block), data=gotway.hessianfly, family=binomial) lme4fit <- list(fixef=fixef(m1),theta=getME(m1,"theta")) ## hack around slight naming differences lme4fit$theta <- unname(lme4fit$theta) lme4.0fit$theta <- unname(lme4.0fit$theta) expect_equal(lme4fit,lme4.0fit,tol=3e-4) ## Fun stuff: visualize and alternative model ## library(ggplot2) ## dat$prop <- dat$y/dat$n ## theme_set(theme_bw()) ## ggplot(dat,aes(x=gen,y=prop,colour=block))+geom_point(aes(size=n))+ ## geom_line(aes(group=block,colour=block))+ ## geom_smooth(family=binomial,aes(weight=n,colour=block,group=block),method="glm", ## alpha=0.1) ## dat$obs <- factor(seq(nrow(dat))) ## m2 <- glmer(cbind(y, n-y) ~ block+ (1|gen) + (1|obs), data=dat, family=binomial) lme4/tests/profile.R0000644000176000001440000000564312172000024014064 0ustar ripleyuserslibrary(lme4) (testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) fm01ML <- lmer(Yield ~ 1|Batch, Dyestuff, REML = FALSE) ## 0.8s (on a 5600 MIPS 64bit fast(year 2009) desktop "AMD Phenom(tm) II X4 925"): ## system.time( tpr <- profile(fm01ML) ) ## test all combinations of 'which', including plots wlist <- list(1:3,1:2,1,2:3,2,3,c(1,3)) invisible(lapply(wlist,function(w) xyplot(profile(fm01ML,which=w)))) (confint(tpr) -> CIpr) print(xyplot(tpr)) ## comparing against lme4a reference values -- but lme4 returns sigma ## rather than log(sigma) stopifnot(dim(CIpr) == c(3,2), all.equal(unname(CIpr[".sigma",]),exp(c(3.64362, 4.21446)), tol=1e-6), all.equal(unname(CIpr["(Intercept)",]),c(1486.451500,1568.548494))) if (testLevel > 2) { ## 2D profiles fm2ML <- lmer(diameter ~ 1 + (1|plate) + (1|sample), Penicillin, REML=0) system.time(pr2 <- profile(fm2ML)) (confint(pr2) -> CIpr2) lme4a_CIpr2 <- structure(c(0.633565787613112, 1.09578224011285, -0.721864513060904, 21.2666273835452, 1.1821039843372, 3.55631937954106, -0.462903300019305, 24.6778176174587), .Dim = c(4L, 2L), .Dimnames = list(c(".sig01", ".sig02", ".lsig", "(Intercept)"), c("2.5 %", "97.5 %"))) lme4a_CIpr2[".lsig",] <- exp(lme4a_CIpr2[".lsig",]) stopifnot(all.equal(unname(CIpr2),unname(lme4a_CIpr2),tol=1e-6)) print(xyplot(pr2, absVal=0, aspect=1.3, layout=c(4,1))) print(splom(pr2)) gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) ## GLMM profiles system.time(pr4 <- profile(gm1)) ## ~ 10 seconds ## FIXME: compDev=FALSE fails if (FALSE) { gm1B <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial, compDev=FALSE,verbose=3) } profile(gm1,which=3) xyplot(pr4,layout=c(5,1),as.table=TRUE) if (FALSE) { ## FIXME! fails because of NAs splom(pr4) } nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree, Orange, start = c(Asym = 200, xmid = 725, scal = 350)) if (FALSE) { ## not working yet pr5 <- profile(nm1,which=1,verbose=1,maxmult=1.2) xyplot(.zeta~.focal|.par,type=c("l","p"),data=lme4:::as.data.frame.thpr(pr5), scale=list(x=list(relation="free")), as.table=TRUE) } } ## testLevel > 2 ## NOT RUN: ~ 4 theta-variables, 19 seconds fm3ML <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, REML=FALSE) if (testLevel > 3) { system.time(pr3 <- profile(fm3ML)) xyplot(pr3) print(splom(pr3)) data("Contraception",package="mlmRev") fm2 <- glmer(use ~ urban+age+livch+(urban|district), Contraception, binomial) pr5 <- profile(fm2,verbose=10) xyplot(pr5) } ## testLevel > 3 lme4/tests/test_timings.RData0000644000176000001440000000131412051771621015734 0ustar ripleyusers‹uS]HQ½Úe¢ûWJ‹H˜k­BQ÷J-!ì‚®ýHEzwç®ÍÌî\×Õ ì!ú} %Z ‰BbŸD¢‡¥¤‡©°$t) +(-°ˆ¢;›Áæïœù¾s¾sçÆ¶Õ—´•ŠA±[\W‰GW±¸X'ªK0eâe+ 9·fs~|¾X¸ àÈÈÚPÍWg~ ßpq|pøýa€j_¹*xvˆï€ïžÍu4^hÛóOçcÍp²éþ©ÝûªYÿøö—ëY8ßÑx©!`¿Ý7P©hÓº”ƒû= àôhxèÉCgïüú ]pêÍ^åxÀá‚'íï8góWÚâú磮gâõÆ Uh×À’x¸ˆ¸$Æz ОÕb„jë³Uãƒøh§8„îL~û¦ÂoàãÙŸÓÕ9T‘±@(`Þš*Ù׊ìôÃùôKá³oöÐ=¿² !·ò2è‰SÊ£„ÕÅꈆM‹(Ë„7N» ³^‰)OP’´$¸RaÔ %¨Á¶¸Ì—Ù¼„º‰fqj8ài»,ÞIx4,ÁžNM',’_8†™,a×èá4—ˆRUÖÝpšjšPP0DzÙĽ6^ßNÒ²­B…+%çdSNÕ’½ºuÕHaMÆ ì˜·ÏÀ&#ŠšpheÄUƒêî7V6[n8»¥É¤Ed 5ÅNWûJ|ÍM±Hk«ò,~“j½œê´[^—ŠåiÚãØrX6ÏJÃúM¡"Q5áTD“ªFäiIªr/O,(DNÇ͈¥*?“ÀM•aN¶—×Rõn sYÞˉŃX“*ç]ŒöÈÆR˜%(sØÜ©M ÿvþØç_¹U­lme4/tests/testcrab.R0000644000176000001440000001043112156422373014243 0ustar ripleyuserslibrary("lme4") L <- load(system.file("testdata","crabs_randdata2.Rda",package="lme4")) ## randdata0: simulated data, in form suitable for plotting ## randdata: simulated data, in form suitable for analysis ## fr ## alive/dead formula ## fr2 ## proportion alive formula (use with weights=initial.snail.density) ## FIXME: there are still bigger differences than I'd like between the approaches ## (mostly in the random-effects correlation). It's not clear who's right; ## lme4 thinks its parameters are better, but ?? Could be explored further. if (FALSE) { ## library(ggplot2) ## commented to avoid triggering Suggests: requirement library(grid) zmargin <- theme(panel.margin=unit(0,"lines")) theme_set(theme_bw()) g1 <- ggplot(randdata0,aes(x=snail.size,y=surv,colour=snail.size,fill=snail.size))+ geom_hline(yintercept=1,colour="black")+ stat_sum(aes(size=factor(..n..)),alpha=0.6)+ facet_grid(.~ttt)+zmargin+ geom_boxplot(fill=NA,outlier.colour=NULL,outlier.shape=3)+ ## set outliers to same colour as points ## (hard to see which are outliers, but it doesn't really matter in this case) scale_size_discrete("# obs",range=c(2,5)) } t1 <- system.time(glmer1 <- glmer(fr2,weights=initial.snail.density, family ="binomial", data=randdata)) t1B <- system.time(glmer1B <- glmer(fr,family ="binomial", data=randdata)) res1 <- c(fixef(glmer1),c(VarCorr(glmer1)$plot)) res1B <- c(fixef(glmer1B),c(VarCorr(glmer1B)$plot)) p1 <- unlist(getME(glmer1,c("theta","beta"))) stopifnot(all.equal(res1,res1B)) dfun <- update(glmer1,devFunOnly=TRUE) stopifnot(all.equal(dfun(p1),c(-2*logLik(glmer1)))) ## ## library(lme4.0) ## version 0.999999.2 results ## t1_lme4.0 <- system.time(glmer1X <- ## glmer(fr2,weights=initial.snail.density, ## family ="binomial", data=randdata)) ## dput(c(fixef(glmer1X),c(VarCorr(glmer1X)$plot))) ## p1X <- c(getME(glmer1X,"theta"),getME(glmer1X,"beta")) p1X <- c(0.681301656652347, -1.14775239687404, 0.436143018123226, 2.77730476938968, 0.609023583738824, -1.60055813739844, 2.0324468778545, 0.624173873057839, -1.7908793509579, -2.44540201631615, -1.42365990002708, -2.26780929006268, 0.700928084600075, -1.26220238391029, 0.369024582097804, 3.44325347343035, 2.26400391093108) stopifnot(all.equal(unname(p1),p1X,tol=0.03)) dfun(p1X) dfun(p1) ## ~ 1.8 seconds elapsed time lme4.0_res <- structure(c(2.77730476938968, 0.609023583738824, -1.60055813739844, 2.0324468778545, 0.624173873057839, -1.7908793509579, -2.44540201631615, -1.42365990002708, -2.26780929006268, 0.700928084600075, -1.26220238391029, 0.369024582097804, 3.44325347343035, 2.26400391093108, 0.464171947357232, -0.532754465140956, -0.532754465140956, 0.801690946568518), .Names = c("(Intercept)", "crab.speciesS", "crab.speciesW", "crab.sizeS", "crab.sizeM", "snail.sizeS", "crab.speciesS:crab.sizeS", "crab.speciesS:crab.sizeM", "crab.speciesS:snail.sizeS", "crab.speciesW:snail.sizeS", "crab.sizeS:snail.sizeS", "crab.sizeM:snail.sizeS", "crab.speciesS:crab.sizeS:snail.sizeS", "crab.speciesS:crab.sizeM:snail.sizeS", "", "", "", "")) stopifnot(all.equal(res1,lme4.0_res,tol=0.015)) ## library("glmmADMB") ## prop/weights formulation: ~ 7 seconds ## t1_glmmadmb <- system.time(glmer1B <- glmmadmb(fr,family ="binomial", ## corStruct="full",data=randdata)) ## dput(c(fixef(glmer1B),c(VarCorr(glmer1B)$plot))) glmmADMB_res <- structure(c(2.7773101267224, 0.609026276823218, -1.60055704634712, 2.03244174458562, 0.624171008585953, -1.79088398816641, -2.44540300134182, -1.42366043619683, -2.26780858382505, 0.700927141726545, -1.26219964572264, 0.369029052442189, 3.44326297908383, 2.26403738918967, 0.46417, -0.53253, -0.53253, 0.80169), .Names = c("(Intercept)", "crab.speciesS", "crab.speciesW", "crab.sizeS", "crab.sizeM", "snail.sizeS", "crab.speciesS:crab.sizeS", "crab.speciesS:crab.sizeM", "crab.speciesS:snail.sizeS", "crab.speciesW:snail.sizeS", "crab.sizeS:snail.sizeS", "crab.sizeM:snail.sizeS", "crab.speciesS:crab.sizeS:snail.sizeS", "crab.speciesS:crab.sizeM:snail.sizeS", "", "", "", "")) stopifnot(all.equal(res1B,glmmADMB_res,tol=0.015)) lme4/tests/evalCall.R0000644000176000001440000000056112172000024014141 0ustar ripleyusers## see if we can still run lme4 functions when lme4 is not attached if ("package:lme4" %in% search()) detach("package:lme4") data(sleepstudy,package="lme4") data(cbpp,package="lme4") fm1 <- lme4::lmer(Reaction ~ Days + (Days|Subject), sleepstudy) gm1 <- lme4::glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) lme4/tests/bootMer.Rout0000644000176000001440000000655312204301544014573 0ustar ripleyusers R Under development (unstable) (2013-08-18 r63609) -- "Unsuffered Consequences" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. [Previously saved workspace restored] > library(lme4) Loading required package: lattice Loading required package: Matrix > library(testthat) > (testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) [1] 4 > mySumm <- function(.) { s <- sigma(.) + c(beta =getME(., "beta"), + sigma = s, sig01 = unname(s * getME(., "theta"))) } > fm1 <- lmer(Yield ~ 1|Batch, Dyestuff) > boo01 <- bootMer(fm1, mySumm, nsim = 10) > boo02 <- bootMer(fm1, mySumm, nsim = 10, use.u = TRUE) > > ## boo02 <- bootMer(fm1, mySumm, nsim = 500, use.u = TRUE) > if (require(boot)) { + boot.ci(boo02,index=2,type="perc") + } Loading required package: boot Attaching package: ‘boot’ The following object is masked from ‘package:lattice’: melanoma BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS Based on 10 bootstrap replicates CALL : boot.ci(boot.out = boo02, type = "perc", index = 2) Intervals : Level Percentile 95% (40.88, 64.75 ) Calculations and Intervals on Original Scale Warning : Percentile Intervals used Extreme Quantiles Some percentile intervals may be unstable Warning message: In norm.inter(t, alpha) : extreme order statistics used as endpoints > > fm2 <- lmer(angle ~ recipe * temperature + (1|recipe:replicate), cake) > boo03 <- bootMer(fm2, mySumm, nsim = 10) > boo04 <- bootMer(fm2, mySumm, nsim = 10, use.u = TRUE) > > if (testLevel > 1) { + gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), + data = cbpp, family = binomial) + boo05 <- bootMer(gm1, mySumm, nsim = 10) + boo06 <- bootMer(gm1, mySumm, nsim = 10, use.u = TRUE) + + cbpp$obs <- factor(seq(nrow(cbpp))) + gm2 <- glmer(cbind(incidence, size - incidence) ~ period + + (1 | herd) + (1|obs), + family = binomial, data = cbpp) + boo03 <- bootMer(gm2, mySumm, nsim = 10) + boo04 <- bootMer(gm2, mySumm, nsim = 10, use.u = TRUE) + } > load(system.file("testdata","culcita_dat.RData",package="lme4")) > cmod <- glmer(predation~ttt+(1|block),family=binomial,data=culcita_dat) > set.seed(101) > ## FIXME: sensitive to step-halving PIRLS tests > ## expect_warning(cc <- confint(cmod,method="boot",nsim=10,quiet=TRUE, > ## .progress="txt",PBargs=list(style=3)),"some bootstrap runs failed") > > ## FIXME: how do we make this safe for testing anywhere? > boo01P <- bootMer(fm1, mySumm, nsim = 10, parallel="multicore", ncpus=2) > ## FIXME: fails on my machine, but I don't know what I'm doing (BMB) > ## boo01P.snow <- bootMer(fm1, mySumm, nsim = 10, parallel="snow", ncpus=2) > > proc.time() user system elapsed 31.325 3.468 34.734 lme4/tests/testOptControl.R0000644000176000001440000000111212211706636015432 0ustar ripleyusers## https://github.com/lme4/lme4/issues/59 library(lme4) dat <- read.csv(system.file("testdata","dat20101314.csv",package="lme4")) NMcopy <- lme4:::Nelder_Mead cc <- capture.output(lmer(y ~ (1|Operator)+(1|Part)+(1|Part:Operator), data=dat, control= lmerControl("NMcopy", optCtrl= list(iprint=20)))) ## check that printing goes through step 140 twice and up to 240 once findStep <- function(str,n) sum(grepl(paste0("^\\(NM\\) ",n,": "),cc)) stopifnot(findStep(cc,140)==2 && findStep(cc,240)==1) lme4/tests/drop1contrasts.R0000644000176000001440000000123212156422373015421 0ustar ripleyusers## drop1 may not work right with contrasts: make up an example something like this ... ## options(contrasts=c("contr.sum","contr.poly")) ## drop1(fecpoiss_lm3,test="Chisq",scope=.~.) library(lme4) oldopts <- options(contrasts=c("contr.sum","contr.poly")) fm1 <- lmer(Reaction~Days+(Days|Subject),data=sleepstudy) drop1(fm1,test="Chisq") ## debug(lme4:::drop1.merMod) drop1(fm1,test="Chisq",scope=.~.) fm0 <- lm(Reaction~Days+Subject,data=sleepstudy) drop1(fm0,test="Chisq",scope=.~.) options(oldopts) ## restore original contrasts ff <- function() { lmer(Reaction~Days+(Days|Subject),data=sleepstudy) } drop1(ff()) ## OK because sleepstudy is accessible! lme4/tests/throw.R0000644000176000001440000000144712156422373013606 0ustar ripleyusers## original code was designed to detect segfaults/hangs from error handling library(lme4) set.seed(101) d <- expand.grid(block=LETTERS[1:26],rep=1:100) d$x <- runif(nrow(d)) reff_f <- rnorm(length(levels(d$block)),sd=1) ## need intercept large enough to avoid negative values d$eta0 <- 4+3*d$x ## version without random effects d$eta <- d$eta0+reff_f[d$block] ## inverse link d$mu <- 1/d$eta d$y <- rgamma(nrow(d),scale=d$mu/2,shape=2) ## update: these all work now (2013 May, but compDev is ignored gm1 <- glmer(y ~ 1|block, d, Gamma, nAGQ=25L) gm1 <- glmer(y ~ 1|block, d, Gamma, nAGQ=25L, compDev=FALSE) gm1 <- glmer(y ~ 1|block, d, Gamma, nAGQ=25L, compDev=FALSE, optimizer="Nelder_Mead") gm2 <- glmer(y ~ 1|block, d, Gamma, nAGQ=25L) gm3 <- glmer(y ~ 1|block, d, Gamma, nAGQ=25L) lme4/tests/nadrop.R0000644000176000001440000000174212172000024013703 0ustar ripleyuserslibrary(lme4) library(testthat) d <- data.frame(x=runif(100),f=factor(rep(1:10,10))) set.seed(101) u <- rnorm(10) d <- transform(d,y=rnorm(100,1+2*x+u[f],0.2)) d0 <- d d[c(3,5,7),"x"] <- NA ## 'omit' and 'exclude' are the only choices under which ## we will see NA values in the results fm0 <- lmer(y~x+(1|f),data=d0) ## no 'na.action' attribute because no NAs in this data set stopifnot(is.null(attr(model.frame(fm0),"na.action"))) fm1 <- update(fm0,data=d) ## no NAs in predict or residuals because na.omit stopifnot(!any(is.na(predict(fm1)))) stopifnot(!any(is.na(residuals(fm1)))) fm2 <- update(fm1,na.action="na.exclude") ## no NAs in predict or residuals because na.omit nNA <- sum(is.na(d$x)) stopifnot(sum(is.na(predict(fm2)))==nNA) stopifnot(sum(is.na(residuals(fm2)))==nNA) expect_error(fm3 <- lmer(y~x+(1|f),data=d,na.action="na.pass"), "infinite or missing values") refit(fm0) refit(fm1) refit(fm2) refit(fm0,runif(100)) refit(fm1,runif(100)) refit(fm2,runif(100)) lme4/tests/lmer2_ex.R0000644000176000001440000000563512156422373014163 0ustar ripleyusersstopifnot(suppressPackageStartupMessages(require(lme4))) ## Using simple generated data -- fully balanced here, unbalanced later set.seed(1) dat <- within(data.frame(lagoon = factor(rep(1:4, each = 25)), habitat = factor(rep(1:20, each = 5))), { ## a simple lagoon effect but no random effect y <- round(10*rnorm(100, m = 10*as.numeric(lagoon))) ## Here, *with* an RE, sigma_a = 100 RE <- rep(round(rnorm(nlevels(habitat), sd = 100)), each = 5) y2 <- y + RE }) ## FIXME: want lmer(* , sparseX = TRUE ) {as in lme4a} if (FALSE) { # need to adapt to new structure ##' ##' ##'
##' @title Comparing the different versions of lmer() for same data & model ##' @param form ##' @param data ##' @param verbose ##' @return chkLmers <- function(form, data, verbose = FALSE, tol = 200e-7) # had tol = 7e-7 working .. { # m <- lmer1(form, data = data) # ok, and more clear # m. <- lmer1(form, data = data, sparseX = TRUE, verbose = verbose) m2 <- lmer (form, data = data, verbose = verbose) # lmem-dense m2. <- lmer (form, data = data, sparseX = TRUE, verbose = verbose) ## Eq <- function(x,y) all.equal(x,y, tol = tol) stopifnot(## Compare sparse & dense of the new class results identical(slotNames(m2), slotNames(m2.)) , identical(slotNames(m2@fe), slotNames(m2.@fe)) , Eq(m2@resp, m2.@resp) , Eq(m2@re, m2.@re) , Eq(m2@fe@coef, m2.@fe@coef) , ## and now compare with the "old" (class 'mer') # Eq(unname(fixef(m)), m2@fe@beta) # , # Eq(unname(fixef(m.)), m2.@fe@beta) # , ## to do ## all.equal(ranef(m)), m2@re) ## all.equal(ranef(m.)), m2.@re) TRUE) invisible(list(#m=m, m.=m., m2 = m2, m2. = m2.)) } chk1 <- chkLmers(y ~ 0+lagoon + (1|habitat), data = dat, verbose = TRUE) chk2 <- chkLmers(y2 ~ 0+lagoon + (1|habitat), data = dat, verbose = TRUE) chk1$m2 ## show( lmer() ) -- sigma_a == 0 chk2$m2. ## show( lmer( ) ) -- n <- nrow(dat) for(i in 1:20) { iOut <- sort(sample(n, 1+rpois(1, 3), replace=FALSE)) cat(i,": w/o ", paste(iOut, collapse=", ")," ") chkLmers(y ~ 0+lagoon + (1|habitat), data = dat[- iOut,]) chkLmers(y2 ~ lagoon + (1|habitat), data = dat[- iOut,]) cat("\n") } ## One (rare) example where the default tolerance is not sufficient: dat. <- dat[- c(14, 34, 66, 67, 71, 88),] try( chkLmers(y ~ 0+lagoon + (1|habitat), data = dat.) ) ## Error: Eq(unname(fixef(m)), m2@fe@beta) is not TRUE ## ## but higher tolerance works: chkLmers(y ~ 0+lagoon + (1|habitat), data = dat., tol = 2e-4, verbose=TRUE) } proc.time() sessionInfo() lme4/tests/README.md0000644000176000001440000000457712156422373013606 0ustar ripleyusersTesting protocols ================ At present `lme4` uses two different testing protocols: * "vanilla" R testing -- every `.R` file gets run during `R CMD check` and compared with its corresponding `.Rout.save` file (if it exists) * `testthat` testing: tests in `inst/tests` can be run in the framework of the `testthat` package, specifically via `tests/test-all.R` There are some unique challenges in testing a package like `lme4` that does numerical computations: * because of floating-point precision issues, detailed numerical results will differ across platforms, compilers, etc.: this can be handled e.g. by setting an appropriate tolerance in `all.equal`, but we do have to decide on an appropriate tolerance. Also, this means that we should *not* typically print out full results of fits to the output file, because these will lead to `Rout`/`Rout.save` comparisons being flagged. * detailed numerical results will also change if we change the details (order etc.) of the internal algorithms (including changes to the underlying computational algorithms in `RcppEigen`); the same tolerance issues arise * in addition to well-targeted unit tests that test specific aspects of the interface, we really need a battery of tests that may be time-consuming, which will be a problem both for rapid development and for CRAN. *Solution*: * users can set an environment variable `LME4_TEST_LEVEL` which controls which tests will be run. Each `.R` file can contain the line(s) ``` testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1 ``` to get the information and specify the default testing level. The general idea would be that 1=default=quick (for CRAN compliance, tests running in a few seconds or less); 2=fairly quick (tests running in <10 seconds); 3=moderate (tests running in <30 seconds); 4=long/exhaustive. `LME4_TEST_LEVEL` could be set less than 1, or zero, or negative, if we wanted really quick tests. * at present this is implemented at the level of each `.R` file, and indeed even at a finer level, by putting `if (testLevel-condition) {}` blocks in the file. It might be nice to have something more global, but this granularity is convenient too. * the `Rout.save` results will differ according to test level: we can either make sure that results are saved at the CRAN default level, or use `.Rbuildignore` to make sure the `Rout.save` files stay off CRAN entirely. lme4/tests/HSAURtrees.Rout0000644000176000001440000000621412174544122015112 0ustar ripleyusers R Under development (unstable) (2013-05-22 r62774) -- "Unsuffered Consequences" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. Natural language support but running in an English locale R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library("lme4") Loading required package: lattice Loading required package: Matrix Loading required package: Rcpp Loading required package: RcppEigen > (testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) [1] 1 > > ## example from HSAUR2 package > if(FALSE) { + ## (commented out to avoid R CMD check warning about undeclared dependency + ## library("multcomp") + trees513 <- subset(trees513, !species %in% + c("fir", "ash/maple/elm/lime", "softwood (other)")) + trees513$species <- trees513$species[,drop = TRUE] + levels(trees513$species)[nlevels(trees513$species)] <- "hardwood" + save("trees513", file="trees513.RData") + } > > > load(system.file("testdata","trees513.RData",package="lme4")) > dfun <- glmer(damage ~ species - 1 + (1 | lattice / plot), + data = trees513, family = binomial, devFunOnly = TRUE) > ls.str(environment(dfun))# and you can investigate ... baseOffset : num [1:2657] 0 0 0 0 0 0 0 0 0 0 ... compDev : logi TRUE control : list() dpars : int [1:2] 1 2 fac : Factor w/ 180 levels "10_1:10","10_2:10",..: 6 6 6 6 6 6 6 6 6 6 ... GQmat : num [1, 1:3] 0 1 -0.919 lower : num [1:7] 0 0 -Inf -Inf -Inf ... lp0 : num [1:2657] 0.404 3.75 0.404 3.75 3.75 ... nAGQ : int 1 pp : Reference class 'merPredD' [package "lme4"] with 18 fields and 42 methods, of which 30 are possibly relevant: b, beta, CcNumer, copy#envRefClass, initialize, initializePtr, installPars, L, ldL2, ldRX2, linPred, P, ptr, RX, RXdiag, RXi, setBeta0, setDelb, setDelu, setTheta, solve, solveU, sqrL, u, unsc, updateDecomp, updateL, updateLamtUt, updateRes, updateXwts pwrssUpdate : function (pp, resp, tol, GQmat, compDev = TRUE, grpFac = NULL, verbose = 0) resp : Reference class 'glmResp' [package "lme4"] with 11 fields and 41 methods, of which 29 are possibly relevant: aic, allInfo, allInfo#lmResp, copy#envRefClass, devResid, fam, initialize, initialize#lmResp, initializePtr, Laplace, link, muEta, ptr, ptr#lmResp, resDev, setOffset, setResp, setTheta, setWeights, sqrtWrkWt, theta, updateMu, updateMu#lmResp, updateWts, variance, wrkResids, wrkResp, wrss, wtWrkResp tolPwrss : num 1e-07 verbose : int 0 > > if (testLevel > 2) { + mmod <- glmer(damage ~ species - 1 + (1 | lattice / plot), + data = trees513, family = binomial()) + summary(mmod) + } > > > proc.time() user system elapsed 3.468 1.376 10.486 lme4/tests/methods.R0000644000176000001440000000145212204271665014102 0ustar ripleyuserslibrary(lme4) library(testthat) fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) expect_equal(colnames(model.frame(fm1)),c("Reaction","Days","Subject")) expect_equal(colnames(model.frame(fm1,fixed.only=TRUE)),c("Reaction","Days")) expect_equal(formula(fm1),Reaction ~ Days + (Days | Subject)) expect_equal(formula(fm1,fixed.only=TRUE),Reaction ~ Days) ## ugly example: model frame with compound elements fm2 <- lmer(log(Reaction) ~ splines::ns(Days,3) + + I(1+Days^3) + (Days|Subject), sleepstudy) expect_equal(names(model.frame(fm2)), c("log(Reaction)", "splines::ns(Days, 3)", "I(1 + Days^3)", "Days", "Subject")) expect_equal(names(model.frame(fm2,fixed.only=TRUE)), c("log(Reaction)", "splines::ns(Days, 3)", "I(1 + Days^3)")) lme4/tests/README0000644000176000001440000000125212156422373013172 0ustar ripleyusersCatalog of currently-failing examples (commented out, testsx, etc.): glmmExt.R: "fail for MM" on Gaussian/inverse examples -- seems fine for me lmer.R: sstudy9 example. Should *not* work; is a meaningful error message possible? prLogistic.R: Thailand/clustered-data example from ?prLogisticDelta example in prLogistic package Presumably the problem is that 100/411 random-effect levels have only zeros -- but should this mess things up? glmmML and lme4.0 give nearly identical answers profile.R: fails on CBPP profiling from testsx: testcolonizer: definite case where complete separation occurs, GLM does not really give a fit testcrabs: ?? not sure ??lme4/tests/dynload.R0000644000176000001440000000367112162051127014066 0ustar ripleyusers## this is the simpler version of the code for testing/exercising ## https://github.com/lme4/lme4/issues/35 ## see also ../misc/issues/dynload.R for more complexity pkg <- so_name <- "lme4"; doUnload <- FALSE; doTest <- TRUE ## pkg <- so_name <- "RcppEigen"; doUnload <- TRUE; doTest <- TRUE ## need to deal with the fact that DLL name != package name for lme4.0 ... ### pkg <- "lme4.0"; so_name <- "lme4"; doUnload <- TRUE instPkgs <- as.data.frame(installed.packages(),stringsAsFactors=FALSE) Load <- function() { library(pkg,character.only=TRUE) } Unload <- function() { ld <- library.dynam() pnames <- sapply(ld,"[[","name") names(ld) <- pnames lp <- gsub("/libs/.*$","",ld[[so_name]][["path"]]) cat("unloading from",lp,"\n") library.dynam.unload(so_name, lp) } Detach <- function() { detach(paste0("package:",pkg),character.only=TRUE,unload=TRUE) if (doUnload) Unload() } tmpf <- function() { g <- getLoadedDLLs() lnames <- names(g)[is.na(instPkgs[names(g),"Priority"])] cat("loaded DLLs:",lnames,"\n") g <- g[na.omit(match(c(so_name,"nlme"),names(g)))] class(g) <- "DLLInfoList" g } test <- function() { if (doTest) { if (pkg %in% c("lme4","lme4.0")) { fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, devFunOnly=TRUE) } if (pkg=="RcppEigen") { data(trees, package="datasets") mm <- cbind(1, log(trees$Girth)) # model matrix y <- log(trees$Volume) # response ## bare-bones direct interface flm <- fastLmPure(mm, y) } } } if (FALSE) { ## FIXME: disabled test for now for (i in 1:6) { cat("Attempt #",i,"\n",sep="") cat("loading",pkg,"\n") Load() tmpf() test() cat("detaching",pkg,"\n") Detach() cat("loading nlme\n") library("nlme") tmpf() detach("package:nlme",unload=TRUE) cat("detaching nlme\n") } } lme4/tests/glmmExt.R0000644000176000001440000001040012204271301014030 0ustar ripleyuserslibrary("lme4") ## tests of a variety of GLMM families and links ## coding: family {g=Gamma, P=Poisson, G=Gaussian, B=binomial} ## link {l=log, i=inverse, c=cloglog, i=identity} ## model {1 = intercept-only, 2 = with continuous predictor} set.seed(101) d <- expand.grid(block=LETTERS[1:26], rep=1:100, KEEP.OUT.ATTRS = FALSE) d$x <- runif(nrow(d)) ## sd=1 reff_f <- rnorm(length(levels(d$block)),sd=1) ## need intercept large enough to avoid negative values d$eta0 <- 4+3*d$x ## fixed effects only d$eta <- d$eta0+reff_f[d$block] ## Gamma, inverse link d$mu <- 1/d$eta d$y <- rgamma(nrow(d),scale=d$mu/2,shape=2) str(d)## 2600 obs .. 'block' with 26 levels ## Gamma, log link dgl <- d dgl$mu <- exp(d$eta) dgl$y <- rgamma(nrow(d),scale=dgl$mu/2,shape=2) ## Poisson, log link dP <- d dP$mu <- exp(d$eta) ## log link dP$y <- rpois(nrow(d),dP$mu) ## Gaussian, log link ## need to use a non-identity link, otherwise glmer calls lmer dG <- d dG$mu <- exp(d$eta) dG$y <- rnorm(nrow(d),dG$mu,sd=2) ## Gaussian with inverse link dGi <- d dGi$mu <- 1/d$eta ## inverse link ## make sd small enough to avoid negative values dGi$y <- rnorm(nrow(d),dGi$mu,sd=0.01) ## binomial with cloglog link dBc <- d cc <- binomial(link="cloglog") dBc$mu <- cc$linkinv(d$eta - 5) # -5, otherwise y will be constant dBc$y <- factor(rbinom(nrow(d),dBc$mu,size=1)) ## binomial with identity link dBi <- d cc <- binomial(link="identity") dBi$mu <- cc$linkinv(d$eta/10) # scale so range goes from 0.2-0.8 dBi$y <- factor(rbinom(nrow(d),dBi$mu,size=1)) ############ ## Gamma/inverse ## GLMs gm0 <- glm(y ~ 1, data=d, family=Gamma) gm1 <- glm(y ~ block-1, data=d, family=Gamma) stopifnot(all.equal(sd(coef(gm1)),1.00753942148611)) gm2 <- glmer(y ~ 1 + (1|block), d, Gamma, nAGQ=0) gm3 <- glmer(y ~ x + (1|block), d, Gamma, nAGQ=0) gm2B <- glmer(y ~ 1 + (1|block), d, Gamma) gm3B <- glmer(y ~ x + (1|block), d, Gamma) ## Gamma/log ggl1 <- glmer(y ~ 1 + (1|block), data=dgl, family=Gamma(link="log")) ggl2 <- glmer(y ~ x + (1|block), data=dgl, family=Gamma(link="log")) ## ## library(lme4.0) ## ggl1 <- glmer(y ~ 1 + (1|block), data=dgl, family=Gamma(link="log"), verbose= 2) ## fails ## Poisson/log gP1 <- glmer(y ~ 1 + (1|block), data=dP, family=poisson) gP2 <- glmer(y ~ x + (1|block), data=dP, family=poisson) ## Gaussian/log gG1 <- glmer(y ~ 1 + (1|block), data=dG, family=gaussian(link="log")) gG2 <- glmer(y ~ x + (1|block), data=dG, family=gaussian(link="log")) ## works with lme4.0 but AIC/BIC/logLik are crazy, and scale ## parameter is not reported ## glmmML etc. doesn't allow models with scale parameters ## gG1B <- glmmadmb(y ~ 1 + (1|block), data=dG, ## family="gaussian",link="log",verbose=TRUE) ## what is the best guess at the estimate of the scale parameter? ## is it the same as sigma? ## gG1B$alpha ## if(Sys.info()["user"] != "maechler") { # <- seg.faults (MM) ## Gaussian/inverse gGi1 <- glmer(y ~ 1 + (1|block), data=dGi,family=gaussian(link="inverse")) gGi2 <- glmer(y ~ x + (1|block), data=dGi, family=gaussian(link="inverse")) ## Binomial/cloglog gBc1 <- glmer(y ~ 1 + (1|block), data=dBc, family=binomial(link="cloglog")) gBc2 <- glmer(y ~ x + (1|block), data=dBc, family=binomial(link="cloglog")) ## library("glmmADMB") ## glmmadmbfit <- glmmadmb(y ~ x + (1|block), data=dBc, ## family="binomial",link="cloglog") glmmadmbfit <- structure(list(fixef = structure(c(-0.717146132730349, 2.83642900561633), .Names = c("(Intercept)", "x")), VarCorr = structure(list( block = structure(0.79992, .Dim = c(1L, 1L), .Dimnames = list( "(Intercept)", "(Intercept)"))), .Names = "block", class = "VarCorr")), .Names = c("fixef", "VarCorr")) stopifnot(all.equal(fixef(gBc2),glmmadmbfit$fixef,tol=5e-3)) ## pretty loose tolerance ... stopifnot(all.equal(unname(unlist(VarCorr(gBc2))), c(glmmadmbfit$VarCorr$block),tol=2e-2)) gBi1 <- glmer(y ~ 1 + (1|block), data=dBi, family=binomial(link="identity")) gBi2 <- glmer(y ~ x + (1|block), data=dBi, family=binomial(link="identity")) ## FIXME: should test more of the *results* of these efforts, not ## just that they run without crashing ... lme4/tests/mkRout0000755000176000001440000000006412106720370013512 0ustar ripleyusersR CMD BATCH --vanilla $1.R; mv $1.Rout $1.Rout.save lme4/tests/glmerControlPass.R0000644000176000001440000000113312204271665015731 0ustar ripleyusers## test redirection from lmer to glmer (correct options passed, ## specifically glmerControl -> tolPwrss library("lme4") library("testthat") ## data("trees513", package = "multcomp") load(system.file("testdata","trees513.RData",package="lme4")) expect_is(mmod1 <- glmer(damage ~ species - 1 + (1 | lattice / plot), data = trees513B, family = binomial()),"glmerMod") expect_warning(mmod2 <- lmer(damage ~ species - 1 + (1 | lattice / plot), data = trees513B, family = binomial()),"calling lmer with .* is deprecated") mmod2@call <- mmod1@call ## hack calls to equality expect_equal(mmod1,mmod2) lme4/tests/glmmWeights.R0000644000176000001440000000444612204271665014734 0ustar ripleyuserslibrary(lme4) library(testthat) source(system.file("testdata/lme-tst-funs.R", package="lme4", mustWork=TRUE)) ##-> gSim(), a general simulation function ... ## hand-coded Pearson residuals {for sumFun() } mypresid <- function(x) { mu <- fitted(x) w <- weights(x) (getME(x,"y")-mu)*sqrt(w)/sqrt(x@resp$family$variance(mu)) } sumFun <- function(m) { ## sum of weighted residuals, 3 ways ## (always identical) wrss1 <- m@devcomp$cmp["wrss"] wrss2 <- sum(residuals(m,type="pearson")^2) wrss3 <- sum(m@resp$wtres^2) ## compare to hand-fitted Pearson resids ... wrss4 <- sum(mypresid(m)^2) c(wrss1,wrss2,wrss3,wrss4) } set.seed(101) ## GAMMA g0 <- glmer(y~x+(1|block),data=gSim(),family=Gamma) expect_equal(var(sumFun(g0)),0) ## BERNOULLI g1 <- glmer(y~x+(1|block),data=gSim(family=binomial(),nbinom=1), family=binomial) expect_equal(var(sumFun(g1)),0) ## POISSON d.P <- gSim(family=poisson()) g2 <- glmer(y ~ x + (1|block),data = d.P, family=poisson) expect_equal(var(sumFun(g2)),0) g2W <- glmer(y ~ x + (1|block), data=d.P, family=poisson, weights=rep(2,nrow(d.P))) expect_equal(var(sumFun(g2W)),0) ## correct ## non-Bernoulli BINOMIAL g3 <- glmer(y ~ x + (1|block),data=gSim(family=binomial(), nbinom=10), family=binomial) expect_equal(var(sumFun(g3)),0) d.b.2 <- gSim(nperblk = 2, family=binomial()) g.b.2 <- glmer(y ~ x + (1|block), data=d.b.2, family=binomial) expect_equal(var(sumFun(g.b.2)), 0) ## Many blocks of only 2 observations each - (but nicely balanced) ## Want this "as" https://github.com/lme4/lme4/issues/47 ## (but it "FAILS" survival already): ## ## n2 = n/2 : n2 <- 2048 n2 <- 100 # for building/testing set.seed(47) dB2 <- gSim(n2, nperblk = 2, x= rep(0:1, each= n2), family=binomial()) ## -- -- --- -------- gB2 <- glmer(y ~ x + (1|block), data=dB2, family=binomial) ## FAILS ----- ## library(survival) ## (gSurv.B2 <- clogit(y ~ x + strata(block), data=dB2)) ## summary(gSurv.B2) ## (SE.surf <- sqrt(diag(vcov(gSurv.B2)))) g3 <- glmer(y ~ x + (1|block),data=gSim(family=binomial(),nbinom=10), family=binomial) expect_equal(var(sumFun(g3)),0) ## check dispersion parameter ## (lowered tolerance to pass checks on my machine -- SCW) expect_equal(sigma(g0)^2,0.4888248,tol=1e-4) lme4/tests/glmmExt.Rout0000644000176000001440000001273612173504727014616 0ustar ripleyusers R Under development (unstable) (2013-07-10 r63264) -- "Unsuffered Consequences" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. 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Type 'q()' to quit R. > library("lme4") Loading required package: lattice Loading required package: Matrix Loading required package: Rcpp Loading required package: RcppEigen > > ## tests of a variety of GLMM families and links > ## coding: family {g=Gamma, P=Poisson, G=Gaussian, B=binomial} > ## link {l=log, i=inverse, c=cloglog, i=identity} > ## model {1 = intercept-only, 2 = with continuous predictor} > > set.seed(101) > d <- expand.grid(block=LETTERS[1:26], rep=1:100, KEEP.OUT.ATTRS = FALSE) > d$x <- runif(nrow(d)) ## sd=1 > reff_f <- rnorm(length(levels(d$block)),sd=1) > ## need intercept large enough to avoid negative values > d$eta0 <- 4+3*d$x ## fixed effects only > d$eta <- d$eta0+reff_f[d$block] > > ## Gamma, inverse link > d$mu <- 1/d$eta > d$y <- rgamma(nrow(d),scale=d$mu/2,shape=2) > > ## Gamma, log link > dgl <- d > dgl$mu <- exp(d$eta) > dgl$y <- rgamma(nrow(d),scale=dgl$mu/2,shape=2) > > ## Poisson, log link > dP <- d > dP$mu <- exp(d$eta) ## log link > dP$y <- rpois(nrow(d),dP$mu) > > ## Gaussian, log link > ## need to use a non-identity link, otherwise glmer calls lmer > dG <- d > dG$mu <- exp(d$eta) > dG$y <- rnorm(nrow(d),dG$mu,sd=2) > > ## Gaussian with inverse link > dGi <- d > dGi$mu <- 1/d$eta ## inverse link > ## make sd small enough to avoid negative values > dGi$y <- rnorm(nrow(d),dGi$mu,sd=0.01) > > ## binomial with cloglog link > dBc <- d > cc <- binomial(link="cloglog") > dBc$mu <- cc$linkinv(d$eta - 5) # -5, otherwise y will be constant > dBc$y <- factor(rbinom(nrow(d),dBc$mu,size=1)) > > ## binomial with identity link > dBi <- d > cc <- binomial(link="identity") > dBi$mu <- cc$linkinv(d$eta/10) # scale so range goes from 0.2-0.8 > dBi$y <- factor(rbinom(nrow(d),dBi$mu,size=1)) > > > ############ > ## Gamma/inverse > > ## GLMs > gm0 <- glm(y ~ 1, data=d, family=Gamma) > gm1 <- glm(y ~ block-1, data=d, family=Gamma) > stopifnot(all.equal(sd(coef(gm1)),1.00753942148611)) > > gm2 <- glmer(y ~ 1 + (1|block), d, Gamma, nAGQ=0) > gm3 <- glmer(y ~ x + (1|block), d, Gamma, nAGQ=0) > gm2B <- glmer(y ~ 1 + (1|block), d, Gamma) > gm3B <- glmer(y ~ x + (1|block), d, Gamma) > > ## > ## library(hglm) > ## h1 <- hglm2(y~x+(1|block), data=d, family=Gamma()) > ## lme4.0 fails on all of these ... > > ## Gamma/log > ggl1 <- glmer(y ~ 1 + (1|block), data=dgl, family=Gamma(link="log")) > ggl2 <- glmer(y ~ x + (1|block), data=dgl, family=Gamma(link="log")) > > ## > ## library(lme4.0) > ## ggl1 <- glmer(y ~ 1 + (1|block), data=dgl, family=Gamma(link="log"), verbose= 2) > ## fails > > ## Poisson/log > gP1 <- glmer(y ~ 1 + (1|block), data=dP, family=poisson) > gP2 <- glmer(y ~ x + (1|block), data=dP, family=poisson) > > ## Gaussian/log > gG1 <- glmer(y ~ 1 + (1|block), data=dG, family=gaussian(link="log")) > gG2 <- glmer(y ~ x + (1|block), data=dG, family=gaussian(link="log")) > > ## works with lme4.0 but AIC/BIC/logLik are crazy, and scale > ## parameter is not reported > ## glmmML etc. doesn't allow models with scale parameters > ## gG1B <- glmmadmb(y ~ 1 + (1|block), data=dG, > ## family="gaussian",link="log",verbose=TRUE) > ## what is the best guess at the estimate of the scale parameter? > ## is it the same as sigma? > ## gG1B$alpha > > ## if(Sys.info()["user"] != "maechler") { # <- seg.faults (MM) > > ## Gaussian/inverse > gGi1 <- glmer(y ~ 1 + (1|block), data=dGi,family=gaussian(link="inverse")) > gGi2 <- glmer(y ~ x + (1|block), data=dGi, family=gaussian(link="inverse")) > > > ## Binomial/cloglog > gBc1 <- glmer(y ~ 1 + (1|block), data=dBc, family=binomial(link="cloglog")) > > gBc2 <- glmer(y ~ x + (1|block), data=dBc, + family=binomial(link="cloglog")) > ## library("glmmADMB") > ## glmmadmbfit <- glmmadmb(y ~ x + (1|block), data=dBc, > ## family="binomial",link="cloglog") > glmmadmbfit <- structure(list(fixef = structure(c(-0.717146132730349, 2.83642900561633), + .Names = c("(Intercept)", "x")), VarCorr = structure(list( + block = structure(0.79992, .Dim = c(1L, 1L), + .Dimnames = list( + "(Intercept)", "(Intercept)"))), + .Names = "block", class = "VarCorr")), + .Names = c("fixef", "VarCorr")) > stopifnot(all.equal(fixef(gBc2),glmmadmbfit$fixef,tol=5e-3)) > ## pretty loose tolerance ... > stopifnot(all.equal(unname(unlist(VarCorr(gBc2))), + c(glmmadmbfit$VarCorr$block),tol=2e-2)) > > gBi1 <- glmer(y ~ 1 + (1|block), data=dBi, family=binomial(link="identity")) > gBi2 <- glmer(y ~ x + (1|block), data=dBi, family=binomial(link="identity")) > > ## FIXME: should test more of the *results* of these efforts, not > ## just that they run without crashing ... > > proc.time() user system elapsed 29.817 10.364 42.281 lme4/tests/testcolonizer.R0000644000176000001440000000161012156422373015337 0ustar ripleyusers## library(lme4.0) ## Emacs M- --> setwd() correctly ## m0.0 <- glm(colonizers~Treatment*homespecies*respspecies, data=randdat, family=poisson) ## with(randdat,tapply(colonizers,list(Treatment,homespecies,respspecies),sum)) ## summary(m1.0 <- glmer(form1, data=randdat, family=poisson)) ## summary(m2.0 <- glmer(form2, data=randdat, family=poisson)) ## detach("package:lme4.0", unload=TRUE) load(system.file("testdata","colonizer_rand.rda",package="lme4")) library("lme4") packageVersion("lme4") ## FIXME: currently fails. Works in lme4.0, BUT ... this is a very poorly ## posed problem (complete separation etc.) nAGQ=0 gives decent results. try(m1 <- glmer(form1,data=randdat, family=poisson, verbose=10L)) ## PIRLS step failed try(m1 <- glmer(form1,data=randdat, family=poisson, verbose=10L, nAGQ=0)) ## OK try(m2 <- glmer(form2,data=randdat, family=poisson, verbose=10L)) ## ditto lme4/tests/is.R0000644000176000001440000000134412156422373013052 0ustar ripleyuserslibrary(lme4) fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) stopifnot(isREML(fm1), isLMM(fm1), !isGLMM(fm1), !isNLMM(fm1)) fm1ML <- refitML(fm1) stopifnot(!isREML(fm1ML), isLMM(fm1ML), !isGLMM(fm1ML), !isNLMM(fm1ML)) gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) stopifnot(!isREML(gm1), !isLMM(gm1), isGLMM(gm1), !isNLMM(gm1)) nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree, Orange, start = c(Asym = 200, xmid = 725, scal = 350)) stopifnot(!isREML(nm1), !isLMM(nm1), !isGLMM(nm1), isNLMM(nm1)) lme4/tests/nbinom.R0000644000176000001440000001172612172000024013705 0ustar ripleyuserslibrary(lme4) (testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) ## for now, use hidden functions [MM: this is a sign, we should *export* them] getNBdisp <- lme4:::getNBdisp refitNB <- lme4:::refitNB simfun <- function(sd.u=1, NBtheta=0.5, nblock=25, fform=~x, beta=c(1,2), nrep=40,seed) { if (!missing(seed)) set.seed(seed) ntot <- nblock*nrep d1 <- data.frame(x=runif(ntot),f=rep(LETTERS[1:nblock],each=nrep)) u_f <- rnorm(nblock,sd=sd.u) X <- model.matrix(fform,data=d1) transform(d1,z=rnbinom(ntot, mu=exp(X %*% beta +u_f[f]),size=NBtheta)) } if (testLevel > 1) { set.seed(102) d.1 <- simfun() t1 <- system.time(g1 <- glmer.nb(z ~ x + (1|f), data=d.1, verbose=TRUE)) g1 ## ^^ FIXME: the data results show up as ..2 ; eval.parent() etc.? d1 <- getNBdisp(g1) (g1B <- refitNB(g1,theta=getNBdisp(g1))) (ddev <- deviance(g1)-deviance(g1B)) (rel.d <- (fixef(g1)-fixef(g1B))/fixef(g1)) stopifnot(abs(ddev) < 1e-6, abs(rel.d) < 0.0004) ## library(glmmADMB) ## t2 <- system.time(g2 <- glmmadmb(z~x+(1|f), ## data=d1,family="nbinom")) ## glmmADMB_vals <- list(fixef=fixef(g2), ## NLL=-logLik(g2), ## theta=g2$alpha) ## 0.4487 glmmADMB_vals <- list(fixef = c("(Intercept)"=0.92871, x=2.0507), NLL = structure(2944.62, class = "logLik", df= 4, nobs= 1000L), theta = 0.4487) ##' simplified logLik() so we can compare with "glmmADMB" (and other) results logLik.m <- function(x) { L <- logLik(x) attributes(L) <- attributes(L)[c("class","df","nobs")] L } stopifnot( all.equal( d1, glmmADMB_vals$theta, tol=0.0016) , all.equal(fixef(g1B), glmmADMB_vals$ fixef, tol=0.01)# not so close , if(FALSE) { ## df = 3 vs df = 4 --- fails! --- FIXME ?? all.equal(logLik.m(g1B), -glmmADMB_vals$ NLL, tol=0.001) } else all.equal(as.numeric(logLik.m(g1B)), as.numeric(-glmmADMB_vals$ NLL), tol= 4e-5) ) } if(FALSE) { ## simulation study -------------------- ## library(glmmADMB) ## avoid R CMD check warning simsumfun <- function(...) { d <- simfun(...) t1 <- system.time(g1 <- glmer.nb(z~x+(1|f),data=d)) t2 <- system.time(g2 <- glmmadmb(z~x+(1|f), data=d,family="nbinom")) c(t.glmer=unname(t1["elapsed"]),nevals.glmer=g1$nevals, theta.glmer=exp(g1$minimum), t.glmmadmb=unname(t2["elapsed"]),theta.glmmadmb=g2$alpha) } library(plyr) sim50 <- raply(50,simsumfun(),.progress="text") save("sim50",file="nbinomsim1.RData") library(reshape) m1 <- melt(data.frame(run=seq(nrow(sim50)),sim50),id.var="run") m1 <- data.frame(m1,colsplit(m1$variable,"\\.",c("v","method"))) m2 <- cast(subset(m1,v=="theta",select=c(run,value,method)), run~method) library(ggplot2) ggplot(subset(m1,v=="theta"),aes(x=method,y=value))+ geom_boxplot()+geom_point()+geom_hline(yintercept=0.5,colour="red") ggplot(subset(m1,v=="theta"),aes(x=method,y=value))+ stat_summary(fun.data=mean_cl_normal)+ geom_hline(yintercept=0.5,colour="red") ggplot(m2,aes(x=glmer-glmmadmb))+geom_histogram() ## glmer is slightly more biased (but maybe the MLE itself is biased???) }## end{simulation study}------------------------- ### epilepsy example: data(epil,package="MASS") epil2 <- transform(epil,Visit=(period-2.5)/5, Base=log(base/4),Age=log(age), subject=factor(subject)) ## t3 <- system.time(g3 <- glmmadmb(y~Base*trt+Age+Visit+(Visit|subject), ## data=epil2, family="nbinom")) ## glmmADMB_epil_vals <- list(fixef=fixef(g3), ## NLL=-logLik(g3), ## theta=g3$alpha) glmmADMB_epil_vals <- list(fixef = c("(Intercept)"= -1.33, "Base"=0.88392, "trtprogabide"=-0.92997, "Age"=0.47514, "Visit"=-0.27016, "Base:trtprogabide"=0.33724), NLL = structure(624.551, class = "logLik", df = 9, nobs = 236L), theta = 7.4702) if (testLevel > 3) { ## "too slow" for regular testing -- 49 (MM@lynne: 33, then 26) seconds: (t4 <- system.time(g4 <- glmer.nb(y~ Base*trt + Age + Visit + (Visit|subject), data=epil2, verbose=TRUE))) (Lg4 <- logLik(g4)) attributes(Lg4) <- attributes(Lg4)[c("class","df","nobs")] stopifnot( all.equal(getNBdisp(g4), glmmADMB_epil_vals$ theta, tol= 0.0022)# was 0.002 , all.equal(fixef (g4), glmmADMB_epil_vals$ fixef, tol= 0.004) , all.equal(logLik.m (g4), - glmmADMB_epil_vals$ NLL, tol= 0.0002) ) } cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' lme4/tests/bootMer.R0000644000176000001440000000311212232467515014043 0ustar ripleyuserslibrary(lme4) library(testthat) (testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) mySumm <- function(.) { s <- sigma(.) c(beta =getME(., "beta"), sigma = s, sig01 = unname(s * getME(., "theta"))) } fm1 <- lmer(Yield ~ 1|Batch, Dyestuff) boo01 <- bootMer(fm1, mySumm, nsim = 10) boo02 <- bootMer(fm1, mySumm, nsim = 10, use.u = TRUE) ## boo02 <- bootMer(fm1, mySumm, nsim = 500, use.u = TRUE) if (require(boot)) { boot.ci(boo02,index=2,type="perc") } fm2 <- lmer(angle ~ recipe * temperature + (1|recipe:replicate), cake) boo03 <- bootMer(fm2, mySumm, nsim = 10) boo04 <- bootMer(fm2, mySumm, nsim = 10, use.u = TRUE) if (testLevel > 1) { gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) boo05 <- bootMer(gm1, mySumm, nsim = 10) boo06 <- bootMer(gm1, mySumm, nsim = 10, use.u = TRUE) cbpp$obs <- factor(seq(nrow(cbpp))) gm2 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd) + (1|obs), family = binomial, data = cbpp) boo03 <- bootMer(gm2, mySumm, nsim = 10) boo04 <- bootMer(gm2, mySumm, nsim = 10, use.u = TRUE) } load(system.file("testdata","culcita_dat.RData",package="lme4")) cmod <- glmer(predation~ttt+(1|block),family=binomial,data=culcita_dat) set.seed(101) ## FIXME: sensitive to step-halving PIRLS tests ## expect_warning(cc <- confint(cmod,method="boot",nsim=10,quiet=TRUE, ## .progress="txt",PBargs=list(style=3)),"some bootstrap runs failed") lme4/tests/coefs.R0000644000176000001440000000061412156422373013535 0ustar ripleyusers## test coefficient extraction in the case where RE contain ## terms that are missing from the FE ... set.seed(101) d <- data.frame(resp=runif(100), var1=factor(sample(1:5,size=100,replace=TRUE)), var2=runif(100), var3=factor(sample(1:5,size=100,replace=TRUE))) library(lme4) mix1 <- lmer(resp ~ 0 + var1 + var1:var2 + (1|var3), data=d) coef(mix1) lme4/tests/lme4_nlme.R0000644000176000001440000000175512162051127014311 0ustar ripleyusers## testing whether lme4 and nlme play nicely. Only known issue ## is lmList-masking ... library("lme4") library("nlme") fm1_lmer <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) fm1_lme <- lme(Reaction ~ Days, random=~Days|Subject, sleepstudy) ## variance-covariance matrices: annoyingly different structures vc_lmer <- VarCorr(fm1_lmer) vc_lmerx <- c(diag(vc_lmer[[1]]),attr(vc_lmer[[1]],"correlation")[1,2]) vc_lme <- VarCorr(fm1_lme) suppressWarnings(storage.mode(vc_lme) <- "numeric") vc_lmex <- c(vc_lme[1:2,1],vc_lme[2,3]) stopifnot(all.equal(vc_lmex,vc_lmerx,tol=3e-5)) ## fixed effects (much easier) stopifnot(all.equal(fixef(fm1_lmer),fixef(fm1_lme))) stopifnot(all.equal(unname(unlist(unclass(ranef(fm1_lmer)))), unname(unlist(unclass(ranef(fm1_lme)))), tol=2e-5)) fm1L_lme <- nlme::lmList(distance ~ age | Subject, Orthodont) ## FIXME: lmList not working yet? ## fm1L_lmer <- lme4::lmList(distance ~ age | Subject, Orthodont) ## FIXME: test opposite order lme4/tests/confint.R0000644000176000001440000000177612174475414014114 0ustar ripleyuserslibrary("lme4") library("testthat") (testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) c0 <- confint(fm1,method="Wald") c0B <- confint(fm1,method="Wald",parm="Days") expect_equal(c0[2,],c0B[1,]) expect_equal(c(c0B),c(7.437592,13.496980),tol=1e-6) set.seed(101) if (testLevel > 1) { c1 <- confint(fm1,method="profile",parm=5:6) expect_error(confint(fm1,method="profile",parm="Days"), "must be specified as an integer") expect_equal(c0,c1,tol=2e-3) ## expect Wald and profile _reasonably_ close print(c1,digits=3) c2 <- confint(fm1,method="boot",nsim=50,parm=5:6) expect_error(confint(fm1,method="boot",nsim=50,parm="Days"), "must be specified as an integer") expect_equal(c1,c2,tol=2e-2) print(c2,digits=3) } if (testLevel>10) { c1B <- confint(fm1,method="profile") c2B <- confint(fm1,method="boot") expect_equal(unname(c1B),unname(c2B),tol=2e-2) } lme4/tests/modFormula.R0000644000176000001440000000300612204271301014524 0ustar ripleyuserslibrary(lme4) library(testthat) lmod <- lFormula(Reaction ~ Days + (Days|Subject), sleepstudy) devfun <- do.call(mkLmerDevfun, lmod) opt <- optimizeLmer(devfun) fm1 <- mkMerMod(environment(devfun), opt, lmod$reTrms, fr = lmod$fr) fm2 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) ## basic equivalence fm1C <- fm1 fm1C@call <- fm2@call expect_equal(fm2,fm1C) expect_equal(range(residuals(fm1)),c(-101.1789,132.5466),tol=1e-6) expect_is(model.frame(fm1),"data.frame") ## formulae expect_equal(formula(model.frame(fm1)),Reaction ~ Days + Subject) ## fixed only expect_equal(formula(fm1),Reaction~Days+(Days|Subject)) ## predictions expect_equal(predict(fm1,newdata=sleepstudy[1:10,],REform=NULL), predict(fm2,newdata=sleepstudy[1:10,],REform=NULL)) expect_equal(unname(predict(fm1,newdata=sleepstudy)), predict(fm1)) lmodOff <- lFormula(Reaction ~ Days + (Days|Subject) + offset(0.5*Days), sleepstudy) devfunOff <- do.call(mkLmerDevfun, lmodOff) opt <- optimizeLmer(devfunOff) fm1Off <- mkMerMod(environment(devfunOff), opt, lmodOff$reTrms, fr = lmodOff$fr) fm2Off <- lmer(Reaction ~ Days + (Days|Subject) + offset(0.5*Days), sleepstudy) expect_equal(predict(fm1Off,newdata=sleepstudy[1:10,],REform=NULL), predict(fm2Off,newdata=sleepstudy[1:10,],REform=NULL)) ## FIXME: need more torture tests with offset specified, in different environments ... ## FIXME: drop1(.) doesn't work with modular objects ... hard to see how it ## could, though ... ## drop1(fm1Off) drop1(fm2Off) lme4/tests/testOptControl.Rout0000644000176000001440000000234012204027767016171 0ustar ripleyusers R Under development (unstable) (2013-07-10 r63264) -- "Unsuffered Consequences" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: i686-pc-linux-gnu (32-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. 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Type 'q()' to quit R. > ## https://github.com/lme4/lme4/issues/59 > library(lme4) Loading required package: lattice Loading required package: Matrix Loading required package: Rcpp Loading required package: RcppEigen > dat <- read.csv(system.file("testdata","dat20101314.csv",package="lme4")) > library(lme4) > > NMcopy <- lme4:::Nelder_Mead > > fit <- lmer(y ~ (1|Operator)+(1|Part)+(1|Part:Operator), data=dat, + control=lmerControl("NMcopy", check.nlev.gtreq.5="ignore")) > > proc.time() user system elapsed 4.664 3.504 8.107 lme4/tests/glmer-1.R0000644000176000001440000002323412273465710013707 0ustar ripleyusers## generalized linear mixed model stopifnot(suppressPackageStartupMessages(require(lme4))) options(show.signif.stars = FALSE) (testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) source(system.file("test-tools-1.R", package = "Matrix"), keep.source = FALSE) ## ##' Check that coefficient +- "2" * SD contains true value ##' ##' @title Check that confidence interval for coefficients contains true value ##' @param fm fitted model, e.g., from lm(), lmer(), glmer(), .. ##' @param true.coef numeric vector of true (fixed effect) coefficients ##' @param conf.level confidence level for confidence interval ##' @param sd.factor the "2", i.e. default 1.96 factor for the confidence interval ##' @return TRUE or a string of "error" ##' @author Martin Maechler chkFixed <- function(fm, true.coef, conf.level = 0.95, sd.factor = qnorm((1+conf.level)/2)) { stopifnot(is.matrix(cf <- coefficients(summary(fm))), ncol(cf) >= 2) cc <- cf[,1] sd <- cf[,2] if(any(out1 <- true.coef < cc - sd.factor*sd)) return(sprintf("true coefficient[j], j=%s, is smaller than lower confidence limit", paste(which(out1), collapse=", "))) if(any(out2 <- true.coef > cc + sd.factor*sd)) return(sprintf("true coefficient[j], j=%s, is larger than upper confidence limit", paste(which(out2), collapse=", "))) ## else, return TRUE } ## TODO: (1) move these to ./glmer-ex.R [DONE] ## ---- (2) "rationalize" with ../man/cbpp.Rd #m1e <- glmer1(cbind(incidence, size - incidence) ~ period + (1 | herd), # family = binomial, data = cbpp, doFit = FALSE) ## now #bobyqa(m1e, control = list(iprint = 2L)) m1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp) ## response as a vector of probabilities and usage of argument "weights" m1p <- glmer(incidence / size ~ period + (1 | herd), weights = size, family = binomial, data = cbpp) ## Confirm that these are equivalent: stopifnot(all.equal(fixef(m1), fixef(m1p)), all.equal(ranef(m1), ranef(m1p)), TRUE) ## for(m in c(m1, m1p)) { ## cat("-------\\n\\nCall: ", ## paste(format(getCall(m)), collapse="\\n"), "\\n") ## print(logLik(m)); cat("AIC:", AIC(m), "\\n") ; cat("BIC:", BIC(m),"\\n") ## } stopifnot(all.equal(logLik(m1), logLik(m1p)), all.equal(AIC(m1), AIC(m1p)), all.equal(BIC(m1), BIC(m1p))) ## changed tolPwrss to 1e-7 to match other default m1b <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp, verbose = 2L, control = glmerControl(optimizer="bobyqa", tolPwrss=1e-7, optCtrl=list(rhobeg=0.2, rhoend=2e-7))) ## using nAGQ=9L provides a better evaluation of the deviance m.9 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp, nAGQ = 9) ## check with nAGQ = 25 m2 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), family = binomial, data = cbpp, nAGQ = 25) ## loosened tolerance on parameters stopifnot(is((cm2 <- coef(m2)), "coef.mer"), dim(cm2$herd) == c(15,4), all.equal(fixef(m2), ### lme4a [from an Ubuntu 11.10 amd64 system] c(-1.39922533406847, -0.991407294757321, -1.12782184600404, -1.57946627431248), ##c(-1.3766013, -1.0058773, ## -1.1430128, -1.5922817), tol = 5.e-4, check.attributes=FALSE), all.equal(deviance(m2), 100.010030538022, tol=1e-9) ## with bobyqa first (AGQ=0), then ##all.equal(deviance(m2), 101.119749563, tol=1e-9) ) ## 32-bit Ubuntu 10.04: coef_m1_lme4.0 <- structure(c(-1.39853505102576, -0.992334712470269, -1.12867541092127, -1.58037389566025), .Names = c("(Intercept)", "period2", "period3", "period4")) ## library(glmmADMB) ## mg <- glmmadmb(cbind(incidence, size - incidence) ~ period + (1 | herd), ## family = "binomial", data = cbpp) coef_m1_glmmadmb <- structure(c(-1.39853810064827, -0.99233330126975, -1.12867317840779, -1.58031150854503), .Names = c("(Intercept)", "period2", "period3", "period4")) ## library(glmmML) ## mm <- glmmML(cbind(incidence, size - incidence) ~ period, ## cluster=herd, ## family = "binomial", data = cbpp) coef_m1_glmmML <- structure(c(-1.39853234657711, -0.992336901732793, -1.12867036466201, -1.58030977686564), .Names = c("(Intercept)", "period2", "period3", "period4")) ## lme4[r 1636], 64-bit ubuntu 11.10: ## c(-1.3788385, -1.0589543, ## -1.1936382, -1.6306271), stopifnot(is((cm1 <- coef(m1b)), "coef.mer"), dim(cm1$herd) == c(15,4), all.equal(fixef(m1b),fixef(m1),tol=4e-5), is.all.equal4(fixef(m1b), coef_m1_glmmadmb, coef_m1_lme4.0, coef_m1_glmmML, tol = 5e-4) ) ## Deviance for the new algorithm is lower, eventually we should change the previous test ##stopifnot(deviance(m1) <= deviance(m1e)) showProc.time() # if (require('MASS', quietly = TRUE)) { bacteria$wk2 <- bacteria$week > 2 contrasts(bacteria$trt) <- structure(contr.sdif(3), dimnames = list(NULL, c("diag", "encourage"))) print(fm5 <- glmer(y ~ trt + wk2 + (1|ID), data=bacteria, family=binomial)) showProc.time() # stopifnot( all.equal(logLik(fm5), ## was -96.127838 structure(-96.13069, nobs = 220L, nall = 220L, df = 5L, REML = FALSE, class = "logLik"), tol = 5e-4, check.attributes = FALSE) , all.equal(fixef(fm5), ## was 2.834218798 -1.367099481 c("(Intercept)"= 2.831609490, "trtdiag"= -1.366722631, ## now 0.5842291915, -1.599148773 "trtencourage"=0.5840147802, "wk2TRUE"=-1.598591346), tol = 1e-4 ) ) } ## Failure to specify a random effects term - used to give an obscure message ## Ensure *NON*-translated message; works on Linux,... : if(.Platform$OS.type == "unix") { Sys.setlocale("LC_MESSAGES", "C") tc <- tryCatch( m2 <- glmer(incidence / size ~ period, weights = size, family = binomial, data = cbpp) , error = function(.) .) stopifnot(inherits(tc, "error"), identical(tc$message, "No random effects terms specified in formula")) } ## glmer - Modeling overdispersion as "mixture" aka ## ----- - *ONE* random effect *PER OBSERVATION" -- example inspired by Ben Bolker: ##' ##' ##'
##' @title ##' @param ng number of groups ##' @param nr number of "runs", i.e., observations per groups ##' @param sd standard deviations of group and "Individual" random effects, ##' (\sigma_f, \sigma_I) ##' @param b true beta (fixed effects) ##' @return a data frame (to be used in glmer()) with columns ##' (x, f, obs, eta0, eta, mu, y), where y ~ Pois(lambda(x)), ##' log(lambda(x_i)) = b_1 + b_2 * x + G_{f(i)} + I_i ##' and G_k ~ N(0, \sigma_f); I_i ~ N(0, \sigma_I) ##' @author Ben Bolker and Martin Maechler rPoisGLMMi <- function(ng, nr, sd=c(f = 1, ind = 0.5), b=c(1,2)) { stopifnot(nr >= 1, ng >= 1, is.numeric(sd), names(sd) %in% c("f","ind"), sd >= 0) ntot <- nr*ng b.reff <- rnorm(ng, sd= sd[["f"]]) b.rind <- rnorm(ntot,sd= sd[["ind"]]) x <- runif(ntot) within(data.frame(x, f = factor(rep(LETTERS[1:ng], each=nr)), obs = 1:ntot, eta0 = cbind(1, x) %*% b), { eta <- eta0 + b.reff[f] + b.rind[obs] mu <- exp(eta) y <- rpois(ntot, lambda=mu) }) } set.seed(1) dd <- rPoisGLMMi(12, 20) m0 <- glmer(y~x + (1|f), family="poisson", data=dd) m1 <- glmer(y~x + (1|f) + (1|obs), family="poisson", data=dd) stopifnot(isTRUE(chkFixed(m0, true.coef = c(1,2))), isTRUE(chkFixed(m1, true.coef = c(1,2)))) (a01 <- anova(m0, m1)) stopifnot(all.equal(a01$Chisq[2], 554.334056, tol=1e-5), all.equal(a01$logLik, c(-1073.77193, -796.604902), tol=1e-6), a01$ Df == 3:4, a01$`Chi Df`[2] == 1) if (testLevel > 1) { nsim <- 10 set.seed(2) system.time( simR <- lapply(1:nsim, function(i) { cat(i,"", if(i %% 20 == 0)"\n") dd <- rPoisGLMMi(10 + rpois(1, lambda=3), 16 + rpois(1, lambda=5)) m0 <- glmer(y~x + (1|f), family="poisson", data=dd) m1 <- glmer(y~x + (1|f) + (1|obs), family="poisson", data=dd) a01 <- anova(m0, m1) stopifnot(a01$ Df == 3:4, a01$`Chi Df`[2] == 1) list(chk0 = chkFixed(m0, true.coef = c(1,2)), chk1 = chkFixed(m1, true.coef = c(1,2)), chisq= a01$Chisq[2], lLik = a01$logLik) })) ## m0 is the wrong model, so we don't expect much here: table(unlist(lapply(simR, `[[`, "chk0"))) ## If the fixed effect estimates were unbiased and the standard errors correct, ## and N(0,sigma^2) instead of t_{nu} good enough for the fixed effects, ## the confidence interval should contain the true coef in ~95 out of 100: table(unlist(lapply(simR, `[[`, "chk1"))) ## The tests are all highly significantly in favor of m1 : summary(chi2s <- sapply(simR, `[[`, "chisq")) ## Min. 1st Qu. Median Mean 3rd Qu. Max. ## 158.9 439.0 611.4 698.2 864.3 2268.0 stopifnot(chi2s > qchisq(0.9999, df = 1)) } showProc.time() lme4/tests/optimizer.R0000644000176000001440000000502012211706636014453 0ustar ripleyuserslibrary(lme4) (testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) source(system.file("test-tools-1.R", package = "Matrix"), keep.source = FALSE) ## should be able to run any example with any bounds-constrained optimizer ... ## Nelder_Mead, bobyqa built in; optimx/nlminb, optimx/L-BFGS-B ## optimx/Rcgmin will require a bit more wrapping/interface work (requires gradient) fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) ## Nelder_Mead fm1B <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, control=lmerControl(optimizer="bobyqa")) stopifnot(all.equal(fixef(fm1),fixef(fm1B))) require(optimx) lmerCtrl.optx <- function(method, ...) lmerControl(optimizer="optimx", ..., optCtrl=list(method=method)) glmerCtrl.optx <- function(method, ...) glmerControl(optimizer="optimx", ..., optCtrl=list(method=method)) ## FAILS on Windows (on r-forge only, not win-builder)... 'function is infeasible at initial parameters' ## (can we test whether we are on r-forge??) if (.Platform$OS.type != "windows") { fm1C <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, control=lmerCtrl.optx(method="nlminb")) fm1D <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, control=lmerCtrl.optx(method="L-BFGS-B")) stopifnot(is.all.equal4(fixef(fm1),fixef(fm1B),fixef(fm1C),fixef(fm1D))) if (testLevel > 2) { fm1E <- update(fm1,control=lmerCtrl.optx(method=c("nlminb","L-BFGS-B"))) ## hack equivalence of call and optinfo fm1E@call <- fm1C@call fm1E@optinfo <- fm1C@optinfo ## FIXME: this *should* be identical, but we have small numeric differences all.equal(fm1C,fm1E,tol=1e-5) } } gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial, control=glmerControl(tolPwrss=1e-13)) gm1B <- update(gm1, control=glmerControl (tolPwrss=1e-13, optimizer="bobyqa")) gm1C <- update(gm1, control=glmerCtrl.optx(tolPwrss=1e-13, method="nlminb")) gm1D <- update(gm1, control=glmerCtrl.optx(tolPwrss=1e-13, method="L-BFGS-B")) stopifnot(is.all.equal4(fixef(gm1),fixef(gm1B),fixef(gm1C),fixef(gm1D),tol=1e-5)) gm1E <- update(gm1, control=glmerCtrl.optx(tolPwrss=1e-13, method=c("nlminb","L-BFGS-B"))) if (testLevel > 2) { ## hack equivalence of call and optinfo gm1E@call <- gm1C@call gm1E@optinfo <- gm1C@optinfo all.equal(gm1E,gm1C,tol=1e-5) ## FIXME: this *should* be identical, but we have small numeric differences } lme4/tests/nlmer.R0000644000176000001440000000436612172000024013542 0ustar ripleyuserslibrary(lme4) (testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) allEQ <- function(x,y, tolerance = 4e-4, ...) all.equal.numeric(x,y, tolerance=tolerance, ...) (nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ (Asym|Tree), Orange, start = c(Asym = 200, xmid = 725, scal = 350))) fixef(nm1) if (testLevel > 2) { ## 'Theoph' Data modeling Th.start <- c(lKe = -2.5, lKa = 0.5, lCl = -3) system.time(nm2 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ (lKe+lKa+lCl|Subject), Theoph, start = Th.start, tolPwrss=1e-8)) print(nm2, corr=FALSE) system.time(nm3 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ (lKe|Subject) + (lKa|Subject) + (lCl|Subject), Theoph, start = Th.start)) print(nm3, corr=FALSE) ## dropping lKe from random effects: system.time(nm4 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ (lKa+lCl|Subject), Theoph, start = Th.start, tolPwrss=1e-8)) print(nm4, corr=FALSE) system.time(nm5 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ (lKa|Subject) + (lCl|Subject), Theoph, start = Th.start, tolPwrss=1e-8)) print(nm5, corr=FALSE) if (require("PKPDmodels")) { oral1cptSdlkalVlCl <- PKmod("oral", "sd", list(ka ~ exp(lka), Cl ~ exp(lCl), V ~ exp(lV))) if (FALSE) { ## FIXME: Error in get(nm, envir = nlenv) : object 'k' not found ## probably with environments/call stack etc.? ## 'pnames' is c("lV","lka","k") -- not ("lV","lka","lCl") ## nlmer -> nlformula -> MkRespMod ## pnames are OK in nlformula, but in MkRespMod we try to recover ## them from the column names of the gradient attribute of the ## model evaluated in nlenv -- which are wrong. system.time(nm2a <- nlmer(conc ~ oral1cptSdlkalVlCl(Dose, Time, lV, lka, lCl) ~ (lV+lka+lCl|Subject), Theoph, start = c(lV=-1, lka=-0.5, lCl=-3), tolPwrss=1e-8)) print(nm2a, corr=FALSE) } } } ## testLevel > 2 lme4/tests/simulate.R0000644000176000001440000000516512204271665014267 0ustar ripleyuserslibrary(lme4) fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) s1 <- simulate(fm1,seed=101)[[1]] s2 <- simulate(fm1,seed=101,use.u=TRUE) ## binomial (2-column and prob/weights) gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) gm2 <- glmer(incidence/size ~ period + (1 | herd), weights=size, data = cbpp, family = binomial) s1 <- simulate(gm1,seed=101)[[1]] s2 <- simulate(gm2,seed=101)[[1]] stopifnot(all.equal(s1[,1]/rowSums(s1),s2)) s3 <- simulate(gm1,seed=101,use.u=TRUE) ## binomial (factor): Kubovy bug report 1 Aug 2013 d <- data.frame(y=factor(rep(letters[1:2],each=100)), f=factor(rep(1:10,10))) g1 <- glmer(y~(1|f),data=d,family=binomial) simulate(g1,nsim=10) ## test explicitly stated link function gm3 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial(link="logit")) s4 <- simulate(gm3,seed=101)[[1]] stopifnot(all.equal(s1,s4)) cbpp$obs <- factor(seq(nrow(cbpp))) gm4 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd) + (1|obs), data = cbpp, family = binomial) s5 <- simulate(gm4,seed=101)[[1]] s6 <- simulate(gm4,seed=101,use.u=TRUE)[[1]] ## Bernoulli ## works, but too slow if (FALSE) { data(guImmun,package="mlmRev") g1 <- glmer(immun~kid2p+mom25p+ord+ethn+momEd+husEd+momWork+rural+pcInd81+ (1|comm/mom),family="binomial",data=guImmun) s2 <- simulate(g1) } set.seed(101) d <- data.frame(f=rep(LETTERS[1:10],each=10)) d$x <- runif(nrow(d)) u <- rnorm(10) d$eta <- with(d,1+2*x+u[f]) d$y <- rbinom(nrow(d),plogis(d$eta),size=1) g1 <- glmer(y~x+(1|f),data=d,family="binomial") ## tolPwrss=1e-5: no longer necessary if (FALSE) { allcoef <- function(x) { c(deviance(x),getME(x,"theta"),getME(x,"beta")) } tfun <- function(t) { gg <- try(glmer(y~x+(1|f),data=d,family="binomial", control=glmerControl(tolPwrss=10^t))) if (inherits(gg,"try-error")) rep(NA,4) else allcoef(gg) } tvec <- seq(-4,-16,by=-0.25) tres <- cbind(tvec,t(sapply(tvec,tfun))) } s1 <- simulate(g1,seed=102)[[1]] d$y <- factor(c("N","Y")[d$y+1]) g1B <- glmer(y~x+(1|f),data=d,family="binomial") ## ,tolPwrss=1e-5) s1B <- simulate(g1B,seed=102)[[1]] stopifnot(all.equal(s1,as.numeric(s1B)-1)) ## another Bernoulli data(Contraception,package="mlmRev") fm1 <- glmer(use ~ urban+age+livch+(1|district), Contraception, binomial) s3 <- simulate(fm1) d$y <- rpois(nrow(d),exp(d$eta)) g2 <- glmer(y~x+(1|f),data=d,family="poisson") s4 <- simulate(g2) fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) s5 <- simulate(fm1) lme4/tests/REMLdev.R0000644000176000001440000000233012156422373013671 0ustar ripleyuserslibrary(lme4) fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) fm1ML <- refitML(fm1) deviance(fm1) deviance(fm1ML) deviance(fm1,REML=FALSE) ## FIXME: not working yet (NA) deviance(fm1,REML=TRUE) ## from lme4.0 oldvals <- c(REML=1743.6282722424, ML=1751.98581103058) ## leave out ML values for REML fits for now ... stopifnot( all.equal(deviance(fm1),deviance(fm1,REML=TRUE),deviance(fm1ML,REML=TRUE),oldvals["REML"]), all.equal(deviance(fm1ML),deviance(fm1ML,REML=FALSE),oldvals["ML"]), all.equal(deviance(fm1)/-2,c(logLik(fm1)),c(logLik(fm1ML,REML=TRUE)),c(logLik(fm1,REML=TRUE))), all.equal(deviance(fm1ML)/-2,c(logLik(fm1ML,REML=FALSE)), c(logLik(fm1ML,REML=FALSE)))) ## should be: ## stopifnot( ## all.equal(deviance(fm1),deviance(fm1,REML=TRUE),deviance(fm1ML,REML=TRUE),oldvals["REML"]), ## all.equal(deviance(fm1ML),deviance(fm1,REML=FALSE),deviance(fm1ML,REML=FALSE),oldvals["ML"]), ## all.equal(deviance(fm1)/2,c(logLik(fm1)),c(logLik(fm1ML,REML=TRUE)),c(logLik(fm1,REML=TRUE))), ## all.equal(deviance(fm1ML)/2,c(logLik(fm1,REML=FALSE)),c(logLik(fm1ML,REML=FALSE)), ## c(logLik(fm1ML,REML=FALSE)))) lme4/tests/elston.R0000644000176000001440000000616512172000024013730 0ustar ripleyusers## original code for reading/aggregating: ## tickdata <- read.table("Elston2001_tickdata.txt",header=TRUE, ## colClasses=c("factor","numeric","factor","numeric","factor","factor")) ## tickdata <- transform(tickdata,cHEIGHT=scale(HEIGHT,scale=FALSE)) ## for (i in names(tickdata)) { ## if (is.factor(tickdata[[i]])) { ## tickdata[[i]] <- factor(tickdata[[i]],levels=sort(as.numeric(levels(tickdata[[i]])))) ## } ## } ## summary(tickdata) ## grouseticks <- tickdata ## library(reshape) ## meantick <- rename(aggregate(TICKS~BROOD,data=tickdata,FUN=mean), ## c(TICKS="meanTICKS")) ## vartick <- rename(aggregate(TICKS~BROOD,data=tickdata,FUN=var), ## c(TICKS="varTICKS")) ## uniqtick <- unique(subset(tickdata,select=-c(INDEX,TICKS))) ## grouseticks_agg <- Reduce(merge,list(meantick,vartick,uniqtick)) ## save("grouseticks","grouseticks_agg",file="grouseticks.rda") library(lme4) (testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) data(grouseticks) do.plots <- FALSE form <- TICKS~YEAR+HEIGHT+(1|BROOD)+(1|INDEX)+(1|LOCATION) ## fit with lme4 ## library(lme4) ## t1 <- system.time(full_mod1 <- glmer(form, family="poisson",data=grouseticks)) ## c1 <- c(fixef(full_mod1),unlist(VarCorr(full_mod1)), logLik=logLik(full_mod1),time=t1["elapsed"]) ## allcoefs1 <- c(unlist(full_mod1@ST),fixef(full_mod1)) ## detach("package:lme4") ## lme4 summary results: t1 <- structure(c(1.288, 0.048, 1.36, 0, 0), class = "proc_time", .Names = c("user.self", "sys.self", "elapsed", "user.child", "sys.child")) c1 <- structure(c(11.3559080756861, 1.1804105508475, -0.978704335712111, -0.0237607330254979, 0.293232458048324, 0.562551624933584, 0.279548178949372, -424.771990224991, 1.36), .Names = c("(Intercept)", "YEAR96", "YEAR97", "HEIGHT", "INDEX", "BROOD", "LOCATION", "logLik", "time.elapsed" )) allcoefs1 <- structure(c(0.541509425632023, 0.750034415832756, 0.528723159081737, 11.3559080756861, 1.1804105508475, -0.978704335712111, -0.0237607330254979 ), .Names = c("", "", "", "(Intercept)", "YEAR96", "YEAR97", "HEIGHT")) if (testLevel > 1) { t2 <- system.time(full_mod2 <- glmer(form, family="poisson",data=grouseticks)) c2 <- c(fixef(full_mod2),unlist(VarCorr(full_mod2)), logLik=logLik(full_mod2),time=t2["elapsed"]) ## refit ## FIXME: eventually would like to get _exactly_ identical answers on refit() full_mod3 <- refit(full_mod2,grouseticks$TICKS) all.equal(full_mod2,full_mod3,tol=1e-5) } allcoefs <- function(x) c(getME(x,"theta"),getME(x,"beta")) ## deviance function ## FIXME: does compDev do _anything_ any more? mm <- glmer(form, family="poisson",data=grouseticks,devFunOnly=TRUE) mm2 <- glmer(form, family="poisson",data=grouseticks, devFunOnly=TRUE,control=glmerControl(compDev=TRUE)) stopifnot(all.equal(mm(allcoefs1),mm2(allcoefs1))) lme4/tests/polytomous.R0000644000176000001440000000255312172000024014653 0ustar ripleyuserslibrary(lme4) (testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) ## setup ## library(polytomous) ## data(think) ## think.polytomous.lmer1 <- polytomous(Lexeme ~ Agent + Patient + (1|Register), ## data=think, heuristic="poisson.reformulation") ## save("formula.poisson","data.poisson",file="polytomous_test.RData") load(system.file("testdata","polytomous_test.RData",package="lme4")) if (FALSE) { ## infinite loop glmer(formula.poisson,data=data.poisson,family=poisson,verbose=10) ## Cholmod not positive definite -> infinite loop glmer(formula.poisson,data=data.poisson,family=poisson, verbose=10,control=glmerControl(optimizer="bobyqa")) ## caught warning: maxfun < 10 * length(par)^2 is not recommended. -> infinite loop } ## works but sloooow .... if (FALSE) { try(g1 <- glmer(formula.poisson,data=data.poisson,family=poisson, control=glmerControl(compDev=FALSE),verbose=1)) ## runs for 2880 steps until: ## Error in pp$updateDecomp() : Downdated VtV is not positive definite } if (testLevel > 2) { glmer(formula.poisson,data=data.poisson,family=poisson, control=glmerControl(compDev=FALSE),optimizer="bobyqa") ## caught warning: maxfun < 10 * length(par)^2 is not recommended. ## but runs to completion } lme4/tests/boundary.R0000644000176000001440000000274412211706636014266 0ustar ripleyusers## In both of these cases boundary fit (i.e. estimate of zero RE ## variance) is *incorrect*. (Nelder_Mead, restart_edge=FALSE) is the ## only case where we get stuck; either optimizer=bobyqa or ## restart_edge=TRUE (default) works ## Stephane Laurent: dat <- read.csv(system.file("testdata","dat20101314.csv",package="lme4")) library(lme4) fit <- lmer(y ~ (1|Operator)+(1|Part)+(1|Part:Operator), data=dat) fit_b <- lmer(y ~ (1|Operator)+(1|Part)+(1|Part:Operator), data=dat, control=lmerControl(optimizer="bobyqa",restart_edge=FALSE)) fit_c <- lmer(y ~ (1|Operator)+(1|Part)+(1|Part:Operator), data=dat, control=lmerControl(restart_edge=FALSE)) ## tol=1e-5 seems OK in interactive use but not in R CMD check ... ?? stopifnot(all.equal(getME(fit,"theta"),getME(fit_b,"theta"),tol=2e-5)) stopifnot(all(getME(fit,"theta")>0)) ## Manuel Koller source(system.file("testdata","koller-data.R",package="lme4")) ldata <- getData(13) fm4 <- lmer(y ~ (1|Var2), ldata) fm4b <- lmer(y ~ (1|Var2), ldata, control=lmerControl(restart_edge=FALSE)) stopifnot(getME(fm4b,"theta")==0) fm4c <- lmer(y ~ (1|Var2), ldata, control=lmerControl(optimizer="bobyqa")) stopifnot(all.equal(getME(fm4,"theta"),getME(fm4c,"theta"),tol=1e-4)) stopifnot(all(getME(fm4,"theta")>0)) ## dd <- lmer(y ~ (1|Var2), ldata, devFunOnly=TRUE) ## tvec <- 10^seq(-7,0,by=0.1) ## dvec <- sapply(tvec,dd) ## d0 <- dd(0) ## plot(tvec,dvec,type="b") ## plot(tvec,abs(dvec-d0),log="xy",col=ifelse(dvec library("lme4") Loading required package: lattice Loading required package: Matrix > library("testthat") > > > context("testing '||' notation for independent ranefs") > > test_that("basic intercept + slope '||' works", { + expect_equivalent( + lFormula(Reaction ~ Days + (Days||Subject), sleepstudy)$reTrms, + lFormula(Reaction ~ Days + (1|Subject) + (0 + Days|Subject), sleepstudy)$reTrms, + ) + + expect_equivalent( + fitted(lmer(Reaction ~ Days + (Days||Subject), sleepstudy)), + fitted(lmer(Reaction ~ Days + (1|Subject) + (0 + Days|Subject), sleepstudy)), + ) + }) > > test_that("'||' works with nested, multiple, or interaction terms" , { + #works with nested + expect_equivalent(findbars(y ~ (x || id / id2)), + findbars(y ~ (1 | id / id2) + (0 + x | id / id2))) + + #works with multiple + expect_equivalent(findbars(y ~ (x1 + x2 || id / id2) + (x3 | id3) + (x4 || id4)), + findbars(y ~ (1 | id / id2) + (0 + x1 | id / id2) + + (0 + x2 | id / id2) + (x3 | id3) + (1 | id4) + + (0 + x4| id4))) + #interactions: + expect_equivalent(findbars(y ~ (x1*x2 || id)), + findbars(y ~ (1 | id) + (0+x1 | id) + (0 + x2 | id) + + (0 + x1:x2 | id))) + }) > > test_that("quoted terms work", { + f <- quote(crab.speciesS + crab.sizeS + + crab.speciesS:crab.sizeS + (snail.size | plot)) + expect_equivalent(findbars(f)[[1]], (~(snail.size|plot))[[2]][[2]] ) + }) > > test_that("leaves superfluous '||' alone", { + expect_equivalent(findbars(y ~ z + (0 + x || id)), + findbars(y ~ z + (0 + x | id))) + + }) > > test_that("plays nice with parens in fixed or random formulas", { + expect_equivalent(findbars(y ~ (z + x)^2 + (x || id)), + findbars(y ~ (z + x)^2 + (1 | id) + (0 + x | id))) + + expect_equivalent(findbars(y ~ ((x || id)) + (x2|id)), + findbars(y ~ (1 | id) + (0 + x | id) + (x2|id)))}) > > test_that("update works as expected", { + m <- lmer(Reaction ~ Days + (Days || Subject), sleepstudy) + expect_equivalent(fitted(update(m, .~.-(0 + Days | Subject))), + fitted(lmer(Reaction ~ Days + (1|Subject), sleepstudy))) + }) > > > proc.time() user system elapsed 4.444 4.236 9.227 lme4/tests/nlmer-conv.R0000644000176000001440000000166412172000024014503 0ustar ripleyusers### nlmer() convergence testing / monitoring / ... ## ------------------- (testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) ### The output of tests here are *not* 'diff'ed (<==> no *.Rout.save file) library(lme4) ## 'Theoph' Data modeling if (testLevel > 1) { Th.start <- c(lKe=-2.5, lKa=0.5, lCl=-3) (nm2 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ lKe+lKa+lCl|Subject, Theoph, start = Th.start)) (nm3 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~ (lKe|Subject)+(lKa|Subject)+(lCl|Subject), Theoph, start = Th.start)) ## dropping lKe from random effects: (nm4 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~lKa+lCl|Subject, Theoph, start = Th.start, tolPwrss=1e-8)) (nm5 <- nlmer(conc ~ SSfol(Dose, Time,lKe, lKa, lCl) ~(lKa|Subject)+(lCl|Subject), Theoph, start = Th.start)) } lme4/tests/profile_plots.R0000644000176000001440000000057612172000024015305 0ustar ripleyuserslibrary(lme4) fm1 <- lmer(Reaction~ Days + (Days|Subject), sleepstudy) ## system.time( tpr.fm1 <- profile(fm1, optimizer="Nelder_Mead") ) ## 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Check that quasi families throw an error assertError(lmer(cbind(incidence, size - incidence) ~ period + (1|herd), data = cbpp, family = quasibinomial)) assertError(lmer(incidence ~ period + (1|herd), data = cbpp, family = quasipoisson)) assertError(lmer(incidence ~ period + (1|herd), data = cbpp, family = quasi)) ## check bug found by Kevin Buhr set.seed(7) n <- 10 X <- data.frame(y=runif(n), x=rnorm(n), z=sample(c("A","B"), n, TRUE)) fm <- suppressWarnings(lmer(log(y) ~ x | z, data=X)) ## ignore grouping factors with ## gave error inside model.frame() stopifnot(all.equal(unname(fixef(fm)), -0.8345, tol=.01)) ## check working of Matrix methods on vcov(.) etc ---------------------- fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) V <- vcov(fm) V1 <- vcov(fm1) TOL <- 0 # to show the differences below TOL <- 1e-5 # for the check stopifnot( all.equal(diag(V), 0.176078, tol = TOL) # 64b: 2.4e-8 , all.equal(as.numeric(chol(V)), 0.4196165, tol = TOL) # 64b: 3.2e-8 , all.equal(diag(V1), c(46.574978, 2.389469), tol = TOL)# 64b: 9.8e-9 , dim(C1 <- chol(V1)) == c(2,2) , all.equal(as.numeric(C1), c(6.82458627, 0, -0.2126260, 1.5310973), tol=TOL)# 64b: 1.6e-9 , dim(chol(crossprod(getME(fm1, "Z")))) == 36 , TRUE) ## printing signif(chol(crossprod(getME(fm,"Z"))), 4)# -> simple 4 x 4 sparse showProc.time() # ## From: Stephane Laurent ## To: r-sig-mixed-models@.. ## "crash with the latest update of lme4" ## ## .. example for which lmer() crashes with the last update of lme4 ...{R-forge}, ## .. but not with version CRAN version (0.999999-0) lsDat <- data.frame( Operator = as.factor(rep(1:5, c(3,4,8,8,8))), Part = as.factor( c(2L, 3L, 5L, 1L, 1L, 2L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 5L, 1L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 1L, 2L, 2L, 3L, 3L, 4L, 5L, 5L)), y = c(0.34, -1.23, -2.46, -0.84, -1.57,-0.31, -0.18, -0.94, -0.81, 0.77, 0.4, -2.37, -2.78, 1.29, -0.95, -1.58, -2.06, -3.11,-3.2, -0.1, -0.49,-2.02, -0.75, 1.71, -0.85, -1.19, 0.13, 1.35, 1.92, 1.04, 1.08)) xtabs( ~ Operator + Part, data=lsDat) # --> 4 empty cells, quite a few with only one obs.: ## Part ## Operator 1 2 3 4 5 ## 1 0 1 1 0 1 ## 2 2 1 1 0 0 ## 3 2 2 2 1 1 ## 4 1 1 2 2 2 ## 5 1 2 2 1 2 ## FIXME: rank-Z tests false positive??? lf <- lFormula(y ~ (1|Part) + (1|Operator) + (1|Part:Operator), data = lsDat, control=lmerControl(check.nobs.vs.rankZ="ignore")) Zt <- lf$reTrms$Zt c(rankMatrix(Zt)) ## 21 c(rankMatrix(Zt,method="qr")) ## 31 c(rankMatrix(t(Zt),method="qr")) ## 30 nrow(lsDat) ## lf <- lFormula(y ~ (1|Part) + (1|Operator) + (1|Part:Operator), data = lsDat) fm3 <- lmer(y ~ (1|Part) + (1|Operator) + (1|Part:Operator), data = lsDat, control=lmerControl(check.nobs.vs.rankZ="ignore")) showProc.time() cat('Time elapsed: ', proc.time(),'\n') # for ``statistical reasons'' lme4/tests/dynload.R.orig0000644000176000001440000000456112157631713015035 0ustar ripleyusers## this is the simpler version of the code for testing/exercising ## https://github.com/lme4/lme4/issues/35 ## see also ../misc/issues/dynload.R for more complexity pkg <- so_name <- "lme4"; doUnload <- FALSE; doTest <- TRUE ## pkg <- so_name <- "RcppEigen"; doUnload <- TRUE; doTest <- TRUE ## need to deal with the fact that DLL name != package name for lme4.0 ... ### pkg <- "lme4.0"; so_name <- "lme4"; doUnload <- TRUE instPkgs <- as.data.frame(installed.packages(),stringsAsFactors=FALSE) Load <- function() { library(pkg,character.only=TRUE) } Unload <- function() { ld <- library.dynam() pnames <- sapply(ld,"[[","name") names(ld) <- pnames lp <- gsub("/libs/.*$","",ld[[so_name]][["path"]]) cat("unloading from",lp,"\n") library.dynam.unload(so_name, lp) } Detach <- function() { detach(paste0("package:",pkg),character.only=TRUE,unload=TRUE) if (doUnload) Unload() } tmpf <- function() { g <- getLoadedDLLs() lnames <- names(g)[is.na(instPkgs[names(g),"Priority"])] cat("loaded DLLs:",lnames,"\n") g <- g[na.omit(match(c(so_name,"nlme"),names(g)))] class(g) <- "DLLInfoList" g } test <- function() { if (doTest) { if (pkg %in% c("lme4","lme4.0")) { fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, devFunOnly=TRUE) } if (pkg=="RcppEigen") { data(trees, package="datasets") mm <- cbind(1, log(trees$Girth)) # model matrix y <- log(trees$Volume) # response ## bare-bones direct interface flm <- fastLmPure(mm, y) } } } if (FALSE) { <<<<<<< HEAD ## FIXME: disabled test for now for (i in 1:6) { cat("Attempt #",i,"\n",sep="") cat("loading",pkg,"\n") library(pkg,character.only=TRUE) tmpf() test() cat("detaching",pkg,"\n") Detach() cat("loading nlme\n") library("nlme") tmpf() detach("package:nlme",unload=TRUE) cat("detaching nlme\n") } ======= ## FIXME: disabled test for now for (i in 1:6) { cat("Attempt #",i,"\n",sep="") cat("loading",pkg,"\n") Load() tmpf() test() cat("detaching",pkg,"\n") Detach() cat("loading nlme\n") library("nlme") tmpf() detach("package:nlme",unload=TRUE) cat("detaching nlme\n") } >>>>>>> lmeControl } lme4/tests/lmList.R0000644000176000001440000000264112156740731013705 0ustar ripleyusersdata(Orthodont,package="nlme") Orthodont <- as.data.frame(Orthodont) library(lme4) fm1 <- lmList(Reaction ~ Days | Subject, sleepstudy) fm1 <- lmList(Reaction ~ Days | Subject, sleepstudy, pool=TRUE) coef(fm1) summary(fm1) confint(fm1) fm2 <- lmList(distance ~ age | Subject, Orthodont) coef(fm2) d <- data.frame( g = sample(c("A","B","C","D","E"), 250, replace=TRUE), y1 = runif(250, max=100), y2 = sample(c(0,1), 250, replace=TRUE) ) fm1 <- lmList(y1 ~ 1 | g, data=d) coef(fm1) confint(fm1) fm2 <- lmList(y2 ~ 1 | g, data=d, family=binomial) confint(fm2) fm3 <- lmList(cbind(incidence, size - incidence) ~ period|herd, family=binomial, data=cbpp) coef(fm3) ## this is a slightly odd example because the residual df from ## these fits are in fact zero ... so pooled.SD fails, as it should ## FIXME: methods(class="lmList") shows lots of methods inherited from nlme ## that will probably fail ... ## hide/fix these? ## library(reshape2) ## library(ggplot2) ## ggplot(melt(as.matrix(coef(fm3))), ## aes(value,Var2,colour=factor(Var1)))+ ## geom_point()+ ## geom_path(aes(group=factor(Var1))) if (FALSE) { for (i in c(unclass(methods(class="lmList")))) { method <- gsub("\\.lmList","",i) cat(method,"\n") ## do.call(,fm3) ## argh; do.call("coef",fm3) and coef(fm3) behave differently try(eval(parse(text=paste0(method,"(fm3)")))) } } lme4/tests/test-all.R0000644000176000001440000000033212232467515014162 0ustar ripleyusersif(require("testthat", quietly = TRUE)) { pkg <- "lme4" require(pkg, character.only=TRUE, quietly=TRUE) test_package(pkg) } else { print( "package 'testthat' not available, cannot run unit tests" ) } lme4/tests/respiratory.R0000644000176000001440000000137712156422373015030 0ustar ripleyusers## Data originally from Davis 1991 Stat. Med., as packaged in geepack ## and transformed (center, id -> factor, idctr created, levels labeled) library(lme4) load(system.file("testdata","respiratory.RData",package="lme4")) m_glmer_4.L <- glmer(outcome~center+treat+sex+age+baseline+(1|idctr), family=binomial,data=respiratory) m_glmer_4.GHQ5 <- glmer(outcome~center+treat+sex+age+baseline+(1|idctr), family=binomial,data=respiratory,nAGQ=5) m_glmer_4.GHQ8 <- glmer(outcome~center+treat+sex+age+baseline+(1|idctr), family=binomial,data=respiratory,nAGQ=8) m_glmer_4.GHQ16 <- glmer(outcome~center+treat+sex+age+baseline+(1|idctr), family=binomial,data=respiratory,nAGQ=16) lme4/tests/HSAURtrees.R0000644000176000001440000000417212204271665014366 0ustar ripleyuserslibrary("lme4") (testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) ## example from HSAUR2 package; data from 'multcomp'; see ../inst/testdata/trees513.R load(system.file("testdata","trees513.RData",package="lme4")) ## model formula: modForm <- damage ~ species - 1 + (1 | lattice / plot) dfun <- glmer(modForm, data = trees513B, family = binomial, devFunOnly = TRUE) ls.str(environment(dfun))# "for your information" .not.call <- function(x) x[names(x) != "call"] if (testLevel < 2) q("no") ## {{advantage to if(. >= 2) { ........} : autoprint of system.time() etc ## else (testLevel >= 2) : -------------------------------------------------- ## Generate oldres: ## ---------------- ## library(lme4.0) ## system.time(mmod0 <- glmer(damage ~ species - 1 + (1 | lattice / plot), ## data = trees513, family = binomial())) ## ## 4 seconds ## oldres <- c(fixef(mmod0),getME(mmod0,"theta")) ## detach("package:lme4.0") ## dput(oldres) oldres <- structure(c(5.23645064474105, 4.73568475545248, 2.65289926317093, 1.29043984816924, 1.59329381563025, 0.532663142106669, 1.16703186884403 ), .Names = c("speciesspruce", "speciespine", "speciesbeech", "speciesoak", "specieshardwood", "plot:lattice.(Intercept)", "lattice.(Intercept)")) system.time(mmodA <- glmer(modForm, data = trees513A, family = binomial())) ## 7 seconds newres <- c(fixef(mmodA), getME(mmodA,"theta")) stopifnot(all.equal(oldres, newres, tol=1.5e-3)) system.time(mmodB <- glmer(modForm, data = trees513B, family = binomial())) ## 10.4 seconds ## ## lmer( + family) -> diverts to glmer() with a warning [TODO: use assertWarning(.) eventually] system.time(lmodB <- lmer(modForm, data = trees513B, family = binomial())) stopifnot(all.equal(.not.call(summary(mmodB)), .not.call(summary(lmodB)))) newresB <- c(fixef(mmodB),getME(mmodB,"theta")) stopifnot(length(newresB) == length(oldres) + 1)# extra: species[ash/maple/elm/lime] ## (unfinished) lme4/tests/extras.R0000644000176000001440000000054612156422373013750 0ustar ripleyusers## library(lme4) ## This example takes too long ## if (isTRUE(try(data(star, package = 'mlmRev')) == 'star')) { ## fm1 <- lme4:::carryOver(math ~ gr+sx*eth+cltype+(yrs|id)+(1|tch)+(yrs|sch), ## star, yrs ~ tch/id, ## control = list(msV = 1, nit = 0, grad = 0)) ## print(fm1, corr = FALSE) ## } lme4/tests/refit.R0000644000176000001440000000611212232467522013546 0ustar ripleyuserslibrary(lme4) library(testthat) load(system.file("testdata","lme-tst-fits.rda",package="lme4")) (testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1) ## testing refit ## for each type of model, should be able to ## (1) refit with same data and get the same answer, ## at least structurally (small numerical differences ## are probably unavoidable) ## (2) refit with simulate()d data getinfo <- function(x) { c(fixef(x),logLik(x),unlist(ranef(x)),unlist(VarCorr(x))) } dropterms <- function(x) { attr(x@frame,"terms") <- NULL x } ## LMM fm1 <- fit_sleepstudy_2 fm1R <- refit(fm1,sleepstudy$Reaction) fm1S <- refit(fm1,simulate(fm1)[[1]]) stopifnot(all.equal(getinfo(fm1),getinfo(fm1R),tol=3e-5)) ## sapply(slotNames(fm1), ## function(x) isTRUE(all.equal(slot(fm1,x),slot(fm1R,x),tol=1.5e-5))) ## fm1@optinfo ## fm1R@optinfo getinfo(refitML(fm1)) ## binomial GLMM (two-column) gm1 <- fit_cbpp_1 gm1R <- refit(gm1,with(cbpp,cbind(incidence,size-incidence))) ## FIXME: testing all-zero responses ## this gives "pwrssUpdate did not converge in 30 iterations" ## not sure if it's pathological or not if (FALSE) { sim1Z <- simulate(gm1)[[1]] sim1Z[4,] <- c(0,0) refit(gm1,sim1Z) } ## FIXME: why is the difference this large? ## check components ... stopifnot(all.equal(getinfo(gm1),getinfo(gm1R),tol=1e-4)) ## FIXME: still failing on Windows ## gm1S <- refit(gm1,simulate(gm1)[[1]]) ## getinfo(gm1S) ## binomial GLMM (prob/weights) gm2 <- fit_cbpp_3 ## glmer(incidence/size ~ period + (1 | herd), cbpp, binomial, weights=size) gm2R <- refit(gm2,with(cbpp,incidence/size)) stopifnot(all.equal(getinfo(gm2),getinfo(gm2R),tol=6e-4)) ## from Alexandra Kuznetsova set.seed(101) Y <- matrix(rnorm(1000),ncol=2) d <- data.frame(y1=Y[,1], x=rnorm(100), f=rep(1:10,10)) fit1 <- lmer(y1 ~ x+(1|f),data=d) fit2 <- refit(fit1, newresp = Y[,2], rename.response=TRUE) ## check, but ignore terms attribute of model frame ... expect_warning(refit(fit1, newresp = Y[,2], junk=TRUE)) if (isTRUE(all.equal(fit1,fit2))) stop("fit1 and fit2 should not be equal") stopifnot(all.equal(dropterms(fit2), dropterms(u2 <- update(fit2)))) ## FIXME: check on Windows ## gm2S <- refit(gm2,simulate(gm2)[[1]]) ## getinfo(gm2S) if (testLevel > 1) { ## Bernoulli GLMM (specified as factor) if (require("mlmRev")) { data(Contraception,package="mlmRev") gm3 <- glmer(use ~ urban+age+livch+(1|district), Contraception, binomial) gm3R <- refit(gm3,Contraception$use) gm3S <- refit(gm3,simulate(gm3)[[1]]) stopifnot(all.equal(getinfo(gm3),getinfo(gm3R),tol=3e-4)) getinfo(gm3) getinfo(gm3R) getinfo(gm3S) data(Mmmec,package="mlmRev") gm4 <- glmer(deaths ~ uvb + (1|region), data=Mmmec, family=poisson, offset = log(expected)) gm4R <- refit(gm4,Mmmec$deaths) gm4S <- refit(gm4,simulate(gm4)[[1]]) getinfo(gm4) getinfo(gm4R) getinfo(gm4S) stopifnot(all.equal(getinfo(gm4),getinfo(gm4R),tol=6e-5)) } } lme4/tests/minval.R0000644000176000001440000000114512156422373013724 0ustar ripleyusers## example posted by Stéphane Laurent ## exercises bug where Nelder-Mead min objective function value was >0 set.seed(666) sims <- function(I, J, sigmab0, sigmaw0){ Mu <- rnorm(I, mean=0, sd=sigmab0) y <- c(sapply(Mu, function(mu) rnorm(J, mu, sigmaw0))) data.frame(y=y, group=gl(I,J)) } I <- 3 # number of groups J <- 8 # number of repeats per group sigmab0 <- 0.15 # between standard deviation sigmaw0 <- 0.15 # within standard deviation dat <- sims(I, J, sigmab0, sigmaw0) library(lme4) fm3 <- lmer(y ~ (1|group), data=dat) stopifnot(all.equal(unname(unlist(VarCorr(fm3))),0.029662844057)) lme4/tests/lmer-1.Rout.save0000644000176000001440000004452612232467515015234 0ustar ripleyusers R version 3.0.1 Patched (2013-08-16 r63595) -- "Good Sport" Copyright (C) 2013 The R Foundation for Statistical Computing Platform: x86_64-unknown-linux-gnu (64-bit) R is free software and comes with ABSOLUTELY NO WARRANTY. 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Type 'q()' to quit R. > ### suppressPackageStartupMessages(...) as we have an *.Rout.save to Rdiff against > stopifnot(suppressPackageStartupMessages(require(lme4))) > options(show.signif.stars = FALSE) > > source(system.file("test-tools.R", package = "Matrix"))# identical3() etc > all.EQ <- function(u,v, ...) all.equal.X(u, v, except = c("call", "frame"), ...) > S4_2list <- function(obj) { # no longer used + sn <- slotNames(obj) + structure(lapply(sn, slot, object = obj), .Names = sn) + } > ## Is now (2010-09-03) in Matrix' test-tools.R above > ## showProc.time <- local({ > ## pct <- proc.time() > ## function() { ## CPU elapsed __since last called__ > ## ot <- pct ; pct <<- proc.time() > ## cat('Time elapsed: ', (pct - ot)[1:3],'\n') > ## } > ## }) > > (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) Linear mixed model fit by REML ['lmerMod'] Formula: Reaction ~ Days + (Days | Subject) Data: sleepstudy REML criterion at convergence: 1743.628 Random effects: Groups Name Std.Dev. Corr Subject (Intercept) 24.741 Days 5.922 0.07 Residual 25.592 Number of obs: 180, groups: Subject, 18 Fixed Effects: (Intercept) Days 251.41 10.47 > (fm1a <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, REML = FALSE)) Linear mixed model fit by maximum likelihood ['lmerMod'] Formula: Reaction ~ Days + (Days | Subject) Data: sleepstudy AIC BIC logLik deviance 1763.9393 1783.0971 -875.9697 1751.9393 Random effects: Groups Name Std.Dev. Corr Subject (Intercept) 23.781 Days 5.717 0.08 Residual 25.592 Number of obs: 180, groups: Subject, 18 Fixed Effects: (Intercept) Days 251.41 10.47 > (fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) Linear mixed model fit by REML ['lmerMod'] Formula: Reaction ~ Days + (1 | Subject) + (0 + Days | Subject) Data: sleepstudy REML criterion at convergence: 1743.669 Random effects: Groups Name Std.Dev. Subject (Intercept) 25.052 Subject.1 Days 5.988 Residual 25.565 Number of obs: 180, groups: Subject, 18 Fixed Effects: (Intercept) Days 251.41 10.47 > anova(fm1, fm2) Data: sleepstudy Models: fm2: Reaction ~ Days + (1 | Subject) + (0 + Days | Subject) fm1: Reaction ~ Days + (Days | Subject) Df AIC BIC logLik deviance Chisq Chi Df Pr(>Chisq) fm2 5 1762.0 1778.0 -876.00 1752.0 fm1 6 1763.9 1783.1 -875.97 1751.9 0.0639 1 0.8004 > > ## Now works for glmer > fm1. <- suppressWarnings(glmer(Reaction ~ Days + (Days|Subject), sleepstudy)) > ## default family=gaussian/identity link -> automatically calls lmer() (but with a warning) > ## hack call -- comes out unimportantly different > fm1.@call[[1]] <- quote(lmer) > stopifnot(all.equal(fm1, fm1.)) > ## Test against previous version in lmer1 (using bobyqa for consistency) > #(fm1. <- lmer1(Reaction ~ Days + (Days|Subject), sleepstudy, opt = "bobyqa")) > #stopifnot(all.equal(fm1@devcomp$cmp['REML'], fm1.@devcomp$cmp['REML']), > # all.equal(fixef(fm1), fixef(fm1.)), > # all.equal(fm1@re@theta, fm1.@theta, tol = 1.e-7), > # all.equal(ranef(fm1), ranef(fm1.))) > > ## compDev = FALSE no longer applies to lmer > ## Test 'compDev = FALSE' (vs TRUE) > ## fm1. <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, > ## compDev = FALSE)#--> use R code (not C++) for deviance computation > ## stopifnot(all.equal(fm1@devcomp$cmp['REML'], fm1.@devcomp$cmp['REML']), > ## all.equal(fixef(fm1), fixef(fm1.)), > ## all.equal(fm1@re@theta, fm1.@re@theta, tol = 1.e-7), > ## all.equal(ranef(fm1), ranef(fm1.), tol = 1.e-7)) > > stopifnot(all.equal(fixef(fm1), fixef(fm2), tol = 1.e-13), + all.equal(unname(fixef(fm1)), + c(251.405104848485, 10.467285959595), tol = 1e-13), + all.equal(Matrix::cov2cor(vcov(fm1))["(Intercept)", "Days"], + -0.13755, tol=1e-4)) > > fm1ML <- refitML(fm1) > fm2ML <- refitML(fm2) > print(AIC(fm1ML)); print(AIC(fm2ML)) [1] 1763.939 [1] 1762.003 > print(BIC(fm1ML)); print(BIC(fm2ML)) [1] 1783.097 [1] 1777.968 > > (fm3 <- lmer(Yield ~ 1|Batch, Dyestuff2)) Linear mixed model fit by REML ['lmerMod'] Formula: Yield ~ 1 | Batch Data: Dyestuff2 REML criterion at convergence: 161.8283 Random effects: Groups Name Std.Dev. Batch (Intercept) 0.000 Residual 3.716 Number of obs: 30, groups: Batch, 6 Fixed Effects: (Intercept) 5.666 > stopifnot(all.equal(coef(summary(fm3)), + array(c(5.6656, 0.67838803150, 8.3515624346), + c(1,3), dimnames = list("(Intercept)", + c("Estimate", "Std. Error", "t value"))))) > showProc.time() # Time elapsed: 0.98 0.012 0.995 > > ### {from ../man/lmer.Rd } --- compare lmer & lmer1 --------------- > (fmX1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) Linear mixed model fit by REML ['lmerMod'] Formula: Reaction ~ Days + (Days | Subject) Data: sleepstudy REML criterion at convergence: 1743.628 Random effects: Groups Name Std.Dev. Corr Subject (Intercept) 24.741 Days 5.922 0.07 Residual 25.592 Number of obs: 180, groups: Subject, 18 Fixed Effects: (Intercept) Days 251.41 10.47 > (fm.1 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) Linear mixed model fit by REML ['lmerMod'] Formula: Reaction ~ Days + (1 | Subject) + (0 + Days | Subject) Data: sleepstudy REML criterion at convergence: 1743.669 Random effects: Groups Name Std.Dev. Subject (Intercept) 25.052 Subject.1 Days 5.988 Residual 25.565 Number of obs: 180, groups: Subject, 18 Fixed Effects: (Intercept) Days 251.41 10.47 > > #(fmX2 <- lmer2(Reaction ~ Days + (Days|Subject), sleepstudy)) > #(fm.2 <- lmer2(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) > ## check update(, ): > fm.3 <- update(fmX1, . ~ Days + (1|Subject) + (0+Days|Subject)) > stopifnot(all.equal(fm.1, fm.3)) > > fmX1s <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy, sparseX=TRUE) Warning messages: 1: In checkArgs("lmer", sparseX = TRUE) : sparseX = TRUE has no effect at present 2: In checkArgs("lmer", sparseX = TRUE) : extra argument(s) 'sparseX' disregarded > #fmX2s <- lmer2(Reaction ~ Days + (Days|Subject), sleepstudy, sparseX=TRUE) > > showProc.time() # Time elapsed: 0.428 0 0.427 > > for(nm in c("coef", "fixef", "ranef", "sigma", + "model.matrix", "model.frame" , "terms")) { + cat(sprintf("%15s : ", nm)) + FUN <- get(nm) + F.fmX1s <- FUN(fmX1s) + # F.fmX2s <- FUN(fmX2s) + # if(nm == "model.matrix") { + # F.fmX1s <- as(F.fmX1s, "denseMatrix") + # F.fmX2s <- as(F.fmX2s, "denseMatrix") + # FF <- function(.) {r <- FUN(.); row.names(r) <- NULL + # as(r, "generalMatrix") } + # } # else + FF <- FUN + stopifnot( + all.equal( FF(fmX1), F.fmX1s, tol = 1e-6) + # , + # all.equal( FF(fmX2), F.fmX2s, tol = 1e-5) + # , + # all.equal( FF(fm.1), F.fmX2s, tol = 9e-6) ## these are different models + # , + # all.equal(F.fmX2s, F.fmX1s, tol = 6e-6) + # , + # all.equal(FUN(fm.1), FUN(fm.2), tol = 6e-6) + , + TRUE) + cat("[Ok]\n") + } coef : [Ok] fixef : [Ok] ranef : [Ok] sigma : [Ok] model.matrix : [Ok] model.frame : [Ok] terms : [Ok] > > > ## transformed vars should work[even if non-sensical as here;failed in 0.995-1] > fm2l <- lmer(log(Reaction) ~ log(Days+1) + (log(Days+1)|Subject), + data = sleepstudy, REML = FALSE) > ## no need for an expand method now : xfm2 <- expand(fm2) > > stopifnot(dim(ranef(fm2l)[[1]]) == c(18, 2), + is((c3 <- coef(fm3)), "coef.mer"), + all(fixef(fm3) == c3$Batch),## <-- IFF \hat{\sigma^2} == 0 + TRUE) > > > > ## Simple example by Andrew Gelman (2006-01-10) ---- > n.groups <- 10 ; n.reps <- 2 > n <- length(group.id <- gl(n.groups, n.reps)) > ## simulate the varying parameters and the data: > set.seed(0) > a.group <- rnorm(n.groups, 1, 2) > y <- rnorm (n, a.group[group.id], 1) > ## fit and summarize the model > fit.1 <- lmer (y ~ 1 + (1 | group.id)) > coef (fit.1) $group.id (Intercept) 1 3.3760010 2 -0.2384543 3 3.1859572 4 3.1121389 5 1.4381126 6 -1.7593534 7 -0.2546156 8 0.6977977 9 1.2121810 10 4.9433563 attr(,"class") [1] "coef.mer" > ## check show( <"summary.mer"> ): > (sf1 <- summary(fit.1)) # --> now looks as for fit.1 Linear mixed model fit by REML ['lmerMod'] Formula: y ~ 1 + (1 | group.id) REML criterion at convergence: 73.4034 Random effects: Groups Name Variance Std.Dev. group.id (Intercept) 4.5579 2.1349 Residual 0.6678 0.8172 Number of obs: 20, groups: group.id, 10 Fixed effects: Estimate Std. Error t value (Intercept) 1.5713 0.6994 2.247 > > stopifnot(all.equal(fixef(fit.1), c("(Intercept)" = 1.571312129)), + all.equal(unname(ranef(fit.1, drop=TRUE)[["group.id"]]), + c(1.8046888, -1.8097665, 1.6146451, 1.5408268, -0.1331995, + -3.3306655, -1.8259277, -0.8735145, -0.3591311, 3.3720441), + tol = 1e-5) + ) > > > ## ranef and coef > rr <- ranef(fm1) > stopifnot(is.list(rr), length(rr) == 1, class(rr[[1]]) == "data.frame") > print(plot(rr)) $Subject > stopifnot(is(cc <- coef(fm1), "coef.mer"), + is.list(cc), length(cc) == 1, class(cc[[1]]) == "data.frame") > print(plot(cc)) $Subject > rr <- ranef(fm2) > stopifnot(is.list(rr), length(rr) == 1, class(rr[[1]]) == "data.frame") > print(plot(rr)) $Subject > stopifnot(is(cc <- coef(fm2), "coef.mer"), + is.list(cc), length(cc) == 1, class(cc[[1]]) == "data.frame") > print(plot(cc)) $Subject > > showProc.time() # Time elapsed: 0.768 0.004 0.777 > > ## Invalid factor specification -- used to seg.fault: > set.seed(1) > dat <- within(data.frame(lagoon = factor(rep(1:4,each = 25)), + habitat = factor(rep(1:20, each = 5))), + { + y <- round(10*rnorm(100, m = 10*as.numeric(lagoon))) + }) > > try(reg <- lmer(y ~ habitat + (1|habitat*lagoon), data = dat) # did seg.fault + ) # now gives error ^- should be ":" Error in FUN(X[[1L]], ...) : Invalid grouping factor specification, habitat * lagoon In addition: Warning message: In Ops.factor(habitat, lagoon) : * not meaningful for factors > r1 <- lmer(y ~ 0+habitat + (1|habitat:lagoon), data = dat) # ok, but senseless > r1b <- lmer(y ~ 0+habitat + (1|habitat), data = dat) # same model, clearly indeterminable > ## "TODO" : summary(r1) should ideally warn the user > stopifnot(all.equal(fixef(r1), fixef(r1b), tol= 1e-15), + all.equal(ranef(r1), ranef(r1b), tol= 1e-15, check.attributes=FALSE)) > > ## Use a more sensible model: > r2.0 <- lmer(y ~ 0+lagoon + (1|habitat:lagoon), data = dat) # ok > r2 <- lmer(y ~ 0+lagoon + (1|habitat), data = dat) # ok, and more clear > stopifnot(all.equal(fixef(r2), fixef(r2.0), tol= 1e-15), + all.equal(ranef(r2), ranef(r2.0), tol= 1e-15, check.attributes=FALSE)) > V2 <- vcov(r2) > assert.EQ.mat(V2, diag(x = 9.9833/3, nr = 4)) > stopifnot(all.equal(unname(fixef(r2)) - (1:4)*100, + c(1.72, 0.28, 1.76, 0.8), tol = 1e-13)) > > ## sparseX version should give same numbers: > r2. <- lmer(y ~ 0+lagoon + (1|habitat), data = dat, + sparseX = TRUE, verbose = TRUE) Warning messages: 1: In checkArgs("lmer", sparseX = TRUE) : sparseX = TRUE has no effect at present 2: In checkArgs("lmer", sparseX = TRUE) : extra argument(s) 'sparseX' disregarded > > ## the summary() components we do want to compare 'dense X' vs 'sparse X': > nmsSumm <- c("methTitle", "devcomp", "logLik", "ngrps", "coefficients", + "sigma", "REmat", "AICtab") > sr2 <- summary(r2) > sr2. <- summary(r2.) > sr2.$devcomp$dims['spFe'] <- 0L # to allow for comparisons below > stopifnot(all.equal(sr2[nmsSumm], sr2.[nmsSumm], tol= 1e-14) + , all.equal(ranef(r2), ranef(r2.), tol= 1e-14) + , Matrix:::isDiagonal(vcov(r2.)) # ok + , all.equal(Matrix::diag(vcov(r2.)), rep.int(V2[1,1], 4), tol= 1e-13) + # , all(vcov(r2.)@factors$correlation == diag(4)) # not sure why this fails + , TRUE) > r2. Linear mixed model fit by REML ['lmerMod'] Formula: y ~ 0 + lagoon + (1 | habitat) Data: dat REML criterion at convergence: 709.7447 Random effects: Groups Name Std.Dev. habitat (Intercept) 0.000 Residual 9.121 Number of obs: 100, groups: habitat, 20 Fixed Effects: lagoon1 lagoon2 lagoon3 lagoon4 101.7 200.3 301.8 400.8 > > > ### mcmcsamp() : > ## From: Andrew Gelman > ## Date: Wed, 18 Jan 2006 22:00:53 -0500 > > if (FALSE) { # mcmcsamp still needs work + ## NB: Need to restore coda to the Suggests: field of DESCRIPTION + ## file if this code block is reinstated. + ## has.coda <- require(coda) + ## if(!has.coda) + ## cat("'coda' package not available; some outputs will look suboptimal\n") + + ## Very simple example + y <- 1:10 + group <- gl(2,5) + (M1 <- lmer (y ~ 1 + (1 | group))) # works fine + (r1 <- mcmcsamp (M1)) # dito + r2 <- mcmcsamp (M1, saveb = TRUE) # gave error in 0.99-* and 0.995-[12] + (r10 <- mcmcsamp (M1, n = 10, saveb = TRUE)) + + ## another one, still simple + y <- (1:20)*pi + x <- (1:20)^2 + group <- gl(2,10) + M1 <- lmer (y ~ 1 | group) + mcmcsamp (M1, n = 2, saveb=TRUE) # fine + + M2 <- lmer (y ~ 1 + x + (1 + x | group)) # false convergence + ## should be identical (and is) + M2 <- lmer (y ~ x + ( x | group))# false convergence -> simulation doesn't work: + if(FALSE) ## try(..) fails here (in R CMD check) [[why ??]] + mcmcsamp (M2, saveb=TRUE) + ## Error: inconsistent degrees of freedom and dimension ... + + ## mcmc for glmer: + rG1k <- mcmcsamp(m1, n = 1000) + summary(rG1k) + rG2 <- mcmcsamp(m1, n = 3, verbose = TRUE) + } > > ## Spencer Graves' example (from a post to S-news, 2006-08-03) ---------------- > ## it should give an error, rather than silent non-sense: > tstDF <- data.frame(group = letters[1:5], y = 1:5) > assertError(## Now throws an error, as desired : + lmer(y ~ 1 + (1|group), data = tstDF) + ) > > showProc.time() # Time elapsed: 0.592 0.004 0.596 > > ## Wrong formula gave a seg.fault at times: > set.seed(2)# ! > D <- data.frame(y= rnorm(12,10), ff = gl(3,2,12), + x1=round(rnorm(12,3),1), x2=round(rnorm(12,7),1)) > ## NB: The first two are the same, having a length-3 R.E. with 3 x 3 vcov-matrix: > ## --> do need CPU > ## suppressWarnings() for warning about too-few random effects levels > m0 <- suppressWarnings(lmer(y ~ (x1 + x2)|ff, data = D)) > m1 <- suppressWarnings(lmer(y ~ x1 + x2|ff , data = D)) > m2 <- suppressWarnings(lmer(y ~ x1 + (x2|ff), data = D)) > m3 <- suppressWarnings(lmer(y ~ (x2|ff) + x1, data = D)) > stopifnot(all.equal(ranef(m0), ranef(m1)), + all.equal(ranef(m2), ranef(m3)), + inherits(tryCatch(lmer(y ~ x2|ff + x1, data = D), error = function(e)e), + "error")) Warning message: In Ops.factor(ff, x1) : + not meaningful for factors > > showProc.time() # Time elapsed: 1.512 0.004 1.526 > > ## Reordering of grouping factors should not change the internal structure > #Pm1 <- lmer1(strength ~ (1|batch) + (1|sample), Pastes, doFit = FALSE) > #Pm2 <- lmer1(strength ~ (1|sample) + (1|batch), Pastes, doFit = FALSE) > #P2.1 <- lmer (strength ~ (1|batch) + (1|sample), Pastes, devFunOnly = TRUE) > #P2.2 <- lmer (strength ~ (1|sample) + (1|batch), Pastes, devFunOnly = TRUE) > > ## The environments of Pm1 and Pm2 should be identical except for > ## "call" and "frame": > #stopifnot(## all.EQ(env(Pm1), env(Pm2)), > # all.EQ(S4_2list(P2.1), > # S4_2list(P2.2))) > > > ## example from Kevin Thorpe: synthesized equivalent > ## http://thread.gmane.org/gmane.comp.lang.r.lme4.devel/9835 > > ## NA issue: simpler example > d <- data.frame(y=1:60,f=factor(rep(1:6,each=10))) > d$y[2] <- NA > d$f[3:4] <- NA > lmer(y~(1|f),data=d) Linear mixed model fit by REML ['lmerMod'] Formula: y ~ (1 | f) Data: d REML criterion at convergence: 316.1615 Random effects: Groups Name Std.Dev. f (Intercept) 18.395 Residual 3.024 Number of obs: 57, groups: f, 6 Fixed Effects: (Intercept) 30.68 > glmer(y~(1|f),data=d,family=poisson) Generalized linear mixed model fit by maximum likelihood ['glmerMod'] Family: poisson ( log ) Formula: y ~ (1 | f) Data: d AIC BIC logLik deviance 354.3560 358.4421 -175.1780 350.3560 Random effects: Groups Name Std.Dev. f (Intercept) 0.7115 Number of obs: 57, groups: f, 6 Fixed Effects: (Intercept) 3.217 > > ## we originally thought that these examples should be > ## estimating non-zero variances, but they shouldn't ... > ## number of levels with each level of replication > levs <- c(800,300,150,100,50,50,50,20,20,5,2,2,2,2) > n <- seq_along(levs) > flevels <- seq(sum(levs)) > set.seed(101) > fakedat <- data.frame(DA = factor(rep(flevels,rep(n,levs))), + zbmi=rnorm(sum(n*levs))) > ## add NA values > fakedat[sample(nrow(fakedat),100),"zbmi"] <- NA > fakedat[sample(nrow(fakedat),100),"DA"] <- NA > > m5 <- lmer(zbmi ~ (1|DA) , data = fakedat, + control=lmerControl(check.nobs.vs.rankZ="ignore")) > m6 <- update(m5, data=na.omit(fakedat)) > stopifnot(VarCorr(m5)[["DA"]] == 0, + VarCorr(m6)[["DA"]] == 0) > > > > > proc.time() user system elapsed 8.904 0.088 9.022 lme4/tests/predsim.R0000644000176000001440000000311712232467515014104 0ustar ripleyusers## compare range, average, etc. of simulations to ## conditional and unconditional prediction library(lme4) do.plot <- FALSE fm1 <- lmer(Reaction~Days+(1|Subject),sleepstudy) set.seed(101) pp <- predict(fm1) rr <- range(usim2 <- simulate(fm1,1,use.u=TRUE)[[1]]) stopifnot(all.equal(rr,c(159.3896,439.1616),tol=1e-6)) if (do.plot) { plot(,ylim=rr) lines(sleepstudy$Reaction) points(simulate(fm1,1)[[1]],col=4) points(usim2,col=2) } set.seed(101) ## conditional prediction ss <- simulate(fm1,1000,use.u=TRUE) ss_sum <- t(apply(ss,1,quantile,c(0.025,0.5,0.975))) plot(pp) matlines(ss_sum,col=c(1,2,1),lty=c(2,1,2)) stopifnot(all.equal(unname(ss_sum[,2]),pp,tolerance=5e-3)) ## population-level prediction pp2 <- predict(fm1,REform=NA) ss2 <- simulate(fm1,1000,use.u=FALSE) ss_sum2 <- t(apply(ss2,1,quantile,c(0.025,0.5,0.975))) if (do.plot) { plot(pp2) matlines(ss_sum2,col=c(1,2,1),lty=c(2,1,2)) } stopifnot(all.equal(unname(ss_sum2[,2]),unname(pp2),tol=8e-3)) ## predict(...,newdata=...) on models with derived variables in the random effects ## e.g. (f:g, f/g) set.seed(101) d <- expand.grid(f=factor(letters[1:10]),g=factor(letters[1:10]), rep=1:10) d$y <- rnorm(nrow(d)) m1 <- lmer(y~(1|f:g),d) p1A <- predict(m1) p1B <- predict(m1,newdata=d) all.equal(p1A,unname(p1B)) m2 <- lmer(y~(1|f/g),d) p2A <- predict(m2) p2B <- predict(m2,newdata=d) all.equal(p2A,unname(p2B)) ## with numeric grouping variables dn <- transform(d,f=as.numeric(f),g=as.numeric(g)) m1N <- update(m1,data=dn) p1NA <- predict(m1N) p1NB <- predict(m1N,newdata=dn) all.equal(p1NA,unname(p1NB)) lme4/src/0000755000176000001440000000000012273467214011742 5ustar ripleyuserslme4/src/Makevars0000644000176000001440000000072412156422373013436 0ustar ripleyusers## -*- mode: makefile; -*- PKG_LIBS = `$(R_HOME)/bin/Rscript -e "Rcpp:::LdFlags()"` PKG_CXXFLAGS = -DEIGEN_DONT_VECTORIZE ## For a release, uncomment the following line to suppress package ## check warnings about __assert_fail. During development we retain ## the debugging checks even though they cause R to terminate, which ## is bad form. Better to learn of bugs in an impolite way than not ## to learn of them. PKG_CXXFLAGS = -DNDEBUG -DEIGEN_DONT_VECTORIZE lme4/src/mcmcsamp.cpp0000644000176000001440000002115612273467214014253 0ustar ripleyusers// // mcmcsamp.cpp: implementation of mcmcsamp and related classes using Eigen // // Copyright (C) 2011-2012 Douglas Bates, Martin Maechler and Ben Bolker // // This file is part of lme4. #include "mcmcsamp.h" namespace lme4 { using Rcpp::as; static inline double pwrss(lme4::merPredD *pred, lme4::lmResp *resp) { return pred->sqrL(1.) + resp->wrss(); } static inline double sigmaML(lme4::merPredD *pred, lme4::lmResp *resp) { return std::sqrt(pwrss(pred, resp)/double(resp->y().size())); } mcmcsamp::mcmcsamp(lme4::merPredD *pred, lme4::lmResp *resp, SEXP dev, SEXP fixef, SEXP sigma, SEXP ranef) : d_dev( as(dev)), d_fixef(as(fixef)), d_sigma(as(sigma)), d_ranef(as(ranef)) { Rcpp::RNGScope scope; // handles the getRNGstate/putRNGstate bool sig( d_sigma.size() > 0); bool rr( d_ranef.rows() > 0); int n( resp->y().size()); int nsamp(d_dev.size()); int nth( pred->theta().size()); int p( pred->beta0().size()); int q( pred->u0().size()); double npq( n + q); double lsigma(sig ? sigmaML(pred, resp) : 1.); double wrss; if (d_fixef.cols() != nsamp || d_fixef.rows() != p || (sig && d_sigma.size() != nsamp) || (ranef && (d_ranef.cols() != nsamp || d_ranef.rows() != p))) throw std::invalid_argument("dimension mismatch"); if (nth > 1) ::Rf_error("only handling the simple (nth == 1) cases now"); for (int k = 0; k < nsamp; ++k) { pred->updateDecomp(); pred->solve(); pred->MCMC_beta_u(lsigma); d_fixef.col(k) = pred->beta(1.); if (rr) d_ranef.col(k) = pred->b(1.); wrss = resp->updateMu(pred->linPred(1.)); if (sig) d_sigma[k] = lsigma = std::sqrt((pred->sqrL(1.) + wrss)/::Rf_rchisq(npq)); } } #if 0 /** * Generate a Markov-chain Monte Carlo sample from an mer object * * @param x pointer to an merMCMC object * @param fm pointer to an mer object * * @return x with samples filled in */ SEXP mer_MCMCsamp(SEXP , SEXP fm) { SEXP devsamp = GET_SLOT(x, lme4_devianceSym); int *dims = DIMS_SLOT(x), nsamp = LENGTH(devsamp); int n = dims[n_POS], np = dims[np_POS], p = dims[p_POS], q = dims[q_POS]; double *STsamp = REAL(GET_SLOT(x, lme4_STSym)), *d = DEV_SLOT(fm), *dev = REAL(devsamp), *sig = SLOT_REAL_NULL(x, lme4_sigmaSym), *fixsamp = FIXEF_SLOT(x), *resamp = RANEF_SLOT(x); GetRNGstate(); /* The first column of storage slots contains the fitted values */ for (int i = 1; i < nsamp; i++) { /* FIXME: This is probably wrong for a model with weights. */ if (sig) /* update and store sigma */ sig[i] = sqrt(d[pwrss_POS]/rchisq((double)(n + q))); /* update L, RX, beta, u, eta, mu, res and d */ MCMC_beta_u(fm, sig ? sig[i] : 1, fixsamp + i * p, resamp + (resamp ? i : 0) * q); dev[i] = d[ML_POS]; /* update theta_T, theta_S and A */ MCMC_T(fm, sig ? sig[i] : 1); MCMC_S(fm, sig ? sig[i] : 1); ST_getPars(fm, STsamp + i * np); /* record theta */ } PutRNGstate(); /* Restore pars from the first columns of the samples */ Memcpy(FIXEF_SLOT(fm), fixsamp, p); ST_setPars(fm, STsamp); update_ranef(fm); return x; } /** * Update the fixed effects and the orthogonal random effects in an MCMC sample * from an mer object. * * @param x an mer object * @param sigma current standard deviation of the per-observation * noise terms. * @param fvals pointer to memory in which to store the updated beta * @param rvals pointer to memory in which to store the updated b (may * be (double*)NULL) */ static void MCMC_beta_u(SEXP x, double sigma, double *fvals, double *rvals) { int *dims = DIMS_SLOT(x); int i1 = 1, p = dims[p_POS], q = dims[q_POS]; double *V = V_SLOT(x), *fixef = FIXEF_SLOT(x), *muEta = MUETA_SLOT(x), *u = U_SLOT(x), mone[] = {-1,0}, one[] = {1,0}; CHM_FR L = L_SLOT(x); double *del1 = Calloc(q, double), *del2 = Alloca(p, double); CHM_DN sol, rhs = N_AS_CHM_DN(del1, q, 1); R_CheckStack(); if (V || muEta) { error(_("Update not yet written")); } else { /* Linear mixed model */ update_L(x); update_RX(x); lmm_update_fixef_u(x); /* Update beta */ for (int j = 0; j < p; j++) del2[j] = sigma * norm_rand(); F77_CALL(dtrsv)("U", "N", "N", &p, RX_SLOT(x), &p, del2, &i1); for (int j = 0; j < p; j++) fixef[j] += del2[j]; /* Update u */ for (int j = 0; j < q; j++) del1[j] = sigma * norm_rand(); F77_CALL(dgemv)("N", &q, &p, mone, RZX_SLOT(x), &q, del2, &i1, one, del1, &i1); sol = M_cholmod_solve(CHOLMOD_Lt, L, rhs, &c); for (int j = 0; j < q; j++) u[j] += ((double*)(sol->x))[j]; M_cholmod_free_dense(&sol, &c); update_mu(x); /* and parts of the deviance slot */ } Memcpy(fvals, fixef, p); if (rvals) { update_ranef(x); Memcpy(rvals, RANEF_SLOT(x), q); } Free(del1); } /** * Update the theta_T parameters from the ST arrays in place. * * @param x an mer object * @param sigma current standard deviation of the per-observation * noise terms. */ /* FIXME: Probably should fold this function into MCMC_S */ static void MCMC_T(SEXP x, double sigma) { int *Gp = Gp_SLOT(x), nt = (DIMS_SLOT(x))[nt_POS]; double **st = Alloca(nt, double*); int *nc = Alloca(nt, int), *nlev = Alloca(nt, int); R_CheckStack(); if (ST_nc_nlev(GET_SLOT(x, lme4_STSym), Gp, st, nc, nlev) < 2) return; error("Code for non-trivial theta_T not yet written"); } /** * Update the theta_S parameters from the ST arrays in place. * * @param x an mer object * @param sigma current standard deviation of the per-observation * noise terms. */ static void MCMC_S(SEXP x, double sigma) { CHM_SP A = A_SLOT(x), Zt = Zt_SLOT(x); int *Gp = Gp_SLOT(x), *ai = (int*)(A->i), *ap = (int*)(A->p), *dims = DIMS_SLOT(x), *perm = PERM_VEC(x); int annz = ap[A->ncol], info, i1 = 1, n = dims[n_POS], nt = dims[nt_POS], ns, p = dims[p_POS], pos, q = dims[q_POS], znnz = ((int*)(Zt->p))[Zt->ncol]; double *R, *ax = (double*)(A->x), *b = RANEF_SLOT(x), *eta = ETA_SLOT(x), *offset = OFFSET_SLOT(x), *rr, *ss, one = 1, *u = U_SLOT(x), *y = Y_SLOT(x); int *nc = Alloca(nt, int), *nlev = Alloca(nt, int), *spt = Alloca(nt + 1, int); double **st = Alloca(nt, double*); R_CheckStack(); ST_nc_nlev(GET_SLOT(x, lme4_STSym), Gp, st, nc, nlev); ns = 0; /* ns is length(theta_S) */ spt[0] = 0; /* pointers into ss for terms */ for (int i = 0; i < nt; i++) { ns += nc[i]; spt[i + 1] = spt[i] + nc[i]; } if (annz == znnz) { /* Copy Z' to A unless A has new nonzeros */ Memcpy(ax, (double*)(Zt->x), znnz); } else error("Code not yet written for MCMC_S with NLMMs"); /* Create T'Zt in A */ Tt_Zt(A, Gp, nc, nlev, st, nt); /* Create P'u in ranef slot */ for (int i = 0; i < q; i++) b[perm[i]] = u[i]; /* Create X\beta + offset in eta slot */ for (int i = 0; i < n; i++) eta[i] = offset ? offset[i] : 0; F77_CALL(dgemv)("N", &n, &p, &one, X_SLOT(x), &n, FIXEF_SLOT(x), &i1, &one, eta, &i1); /* Allocate R, rr and ss */ R = Alloca(ns * ns, double); /* crossproduct matrix then factor */ rr = Alloca(ns, double); /* row of model matrix for theta_S */ ss = Alloca(ns, double); /* right hand side, then theta_S */ R_CheckStack(); AZERO(R, ns * ns); AZERO(ss, ns); /* Accumulate crossproduct from pseudo-data part of model matrix */ for (int i = 0; i < q; i++) { int sj = theta_S_ind(i, nt, Gp, nlev, spt); AZERO(rr, ns); rr[sj] = b[i]; F77_CALL(dsyr)("U", &ns, &one, rr, &i1, R, &ns); } /* Accumulate crossproduct and residual product of the model matrix. */ /* This is done one row at a time. Rows of the model matrix * correspond to columns of T'Zt */ for (int j = 0; j < n; j++) { /* jth column of T'Zt */ AZERO(rr, ns); for (int p = ap[j]; p < ap[j + 1]; p++) { int i = ai[p]; /* row in T'Zt */ int sj = theta_S_ind(i, nt, Gp, nlev, spt); rr[sj] += ax[p] * b[i]; ss[sj] += rr[sj] * (y[j] - eta[j]); } F77_CALL(dsyr)("U", &ns, &one, rr, &i1, R, &ns); } F77_CALL(dposv)("U", &ns, &i1, R, &ns, ss, &ns, &info); if (info) error(_("Model matrix for theta_S is not positive definite, %d."), info); for (int j = 0; j < ns; j++) rr[j] = sigma * norm_rand(); /* Sample from the conditional Gaussian distribution */ F77_CALL(dtrsv)("U", "N", "N", &ns, R, &ns, rr, &i1); for (int j = 0; j < ns; j++) ss[j] += rr[j]; /* Copy positive part of solution onto diagonals of ST */ pos = 0; for (int i = 0; i < nt; i++) { for (int j = 0; j < nc[i]; j++) { st[i][j * (nc[i] + 1)] = (ss[pos] > 0) ? ss[pos] : 0; pos++; } } update_A(x); } #endif } lme4/src/predModule.h0000644000176000001440000000701712273467214014220 0ustar ripleyusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; tab-width: 8 -*- // // predModule.h: predictor module using Eigen // // Copyright (C) 2011 Douglas Bates, Martin Maechler and Ben Bolker // // This file is part of lme4. #ifndef LME4_PREDMODULE_H #define LME4_PREDMODULE_H #include namespace lme4 { using Eigen::ArrayXd; using Eigen::LLT; using Eigen::MatrixXd; using Eigen::VectorXd; using Eigen::VectorXi; class merPredD { public: typedef Eigen::Map MMap; typedef Eigen::Map MVec; typedef Eigen::Map MiVec; typedef MatrixXd::Scalar Scalar; typedef MatrixXd::Index Index; typedef Eigen::SparseMatrix SpMatrixd; typedef Eigen::CholmodDecomposition ChmDecomp; typedef Eigen::MappedSparseMatrix MSpMatrixd; protected: MMap d_X, d_RZX, d_V, d_VtV; MSpMatrixd d_Zt, d_Ut, d_LamtUt, d_Lambdat; MVec d_theta, d_Vtr, d_Utr, d_Xwts, d_beta0, d_delb, d_delu, d_u0; MiVec d_Lind; Index d_N, d_p, d_q; Scalar d_CcNumer, d_ldL2, d_ldRX2; ChmDecomp d_L; LLT d_RX; public: merPredD(SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP, SEXP); VectorXi Pvec() const; MatrixXd RX() const; MatrixXd RXi() const; MatrixXd unsc() const; VectorXd RXdiag() const; VectorXd b(const Scalar& f) const; VectorXd beta(const Scalar& f) const; VectorXd linPred(const Scalar& f) const; VectorXd u(const Scalar& f) const; Rcpp::List condVar(const Rcpp::Environment&) const; Scalar CcNumer() const {return d_CcNumer;} Scalar ldL2() const {return d_ldL2;} Scalar ldRX2() const {return d_ldRX2;} Scalar solve(); Scalar solveU(); Scalar sqrL(const Scalar& f) const; const ChmDecomp& L() const {return d_L;} const MMap& V() const {return d_V;} const MMap& VtV() const {return d_VtV;} const MMap& RZX() const {return d_RZX;} const MSpMatrixd& Lambdat() const {return d_Lambdat;} const MSpMatrixd& LamtUt() const {return d_LamtUt;} const MSpMatrixd& Ut() const {return d_Ut;} const MSpMatrixd& Zt() const {return d_Zt;} const MVec& Utr() const {return d_Utr;} const MVec& Vtr() const {return d_Vtr;} const MVec& delb() const {return d_delb;} const MVec& delu() const {return d_delu;} const MVec& beta0() const {return d_beta0;} const MVec& theta() const {return d_theta;} const MVec& u0() const {return d_u0;} const MVec& Xwts() const {return d_Xwts;} int info() const {return d_L.info();} void installPars(const Scalar& f); void MCMC_beta_u(const Scalar& sigma); void setBeta0(const VectorXd&); void setDelb(const VectorXd&); void setDelu(const VectorXd&); void setTheta(const VectorXd&); void setU0(const VectorXd&); void updateDecomp(); void updateDecomp(const MatrixXd*); void updateL(); void updateLamtUt(); void updateRes(const VectorXd&); void updateXwts(const ArrayXd&); }; } #endif // LME4_PREDMODULE_H lme4/src/glmFamily.h0000644000176000001440000001464512273467214014046 0ustar ripleyusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; tab-width: 8 -*- // // glmFamily.h: glm family class using Eigen // // Copyright (C) 2012 Douglas Bates, Martin Maechler and Ben Bolker // // This file is part of lme4. #ifndef LME4_GLMFAMILY_H #define LME4_GLMFAMILY_H #include namespace glm { using Eigen::ArrayXd; class glmDist { protected: //@{ R functions from the family, as a fall-back Rcpp::Function d_devRes, d_variance, d_aic; //@} Rcpp::Environment d_rho; public: glmDist(Rcpp::List&); virtual ~glmDist() {} virtual const ArrayXd variance(const ArrayXd&) const; virtual const ArrayXd devResid(const ArrayXd&, const ArrayXd&, const ArrayXd&) const; virtual double aic(const ArrayXd&, const ArrayXd&, const ArrayXd&, const ArrayXd&, double) const; /**< in keeping with the botched up nomenclature in the R glm function, * the value of aic is the deviance */ virtual double theta() const; virtual void setTheta(const double&); }; class binomialDist : public glmDist { public: binomialDist(Rcpp::List& ll) : glmDist(ll) {} const ArrayXd variance(const ArrayXd&) const; const ArrayXd devResid(const ArrayXd&, const ArrayXd&, const ArrayXd&) const; double aic(const ArrayXd&, const ArrayXd&, const ArrayXd&, const ArrayXd&, double) const; }; class gammaDist : public glmDist { public: gammaDist(Rcpp::List& ll) : glmDist(ll) {} const ArrayXd variance(const ArrayXd&) const; const ArrayXd devResid(const ArrayXd&, const ArrayXd&, const ArrayXd&) const; double aic(const ArrayXd&, const ArrayXd&, const ArrayXd&, const ArrayXd&, double) const; }; class GaussianDist : public glmDist { public: GaussianDist(Rcpp::List& ll) : glmDist(ll) {} const ArrayXd variance(const ArrayXd&) const; const ArrayXd devResid(const ArrayXd&, const ArrayXd&, const ArrayXd&) const; double aic(const ArrayXd&, const ArrayXd&, const ArrayXd&, const ArrayXd&, double) const; }; class inverseGaussianDist : public glmDist { public: inverseGaussianDist(Rcpp::List& ll) : glmDist(ll) {} const ArrayXd variance(const ArrayXd&) const; const ArrayXd devResid(const ArrayXd&, const ArrayXd&, const ArrayXd&) const; double aic(const ArrayXd&, const ArrayXd&, const ArrayXd&, const ArrayXd&, double) const; }; class negativeBinomialDist : public glmDist { protected: double d_theta; public: negativeBinomialDist (Rcpp::List& ll); const ArrayXd variance(const ArrayXd&) const; const ArrayXd devResid(const ArrayXd&, const ArrayXd&, const ArrayXd&) const; double aic(const ArrayXd&, const ArrayXd&, const ArrayXd&, const ArrayXd&, double) const; double theta() const {return d_theta;} void setTheta(const double& ntheta) {d_theta = ntheta;} }; class PoissonDist : public glmDist { public: PoissonDist(Rcpp::List& ll) : glmDist(ll) {} const ArrayXd variance(const ArrayXd&) const; const ArrayXd devResid(const ArrayXd&, const ArrayXd&, const ArrayXd&) const; double aic(const ArrayXd&, const ArrayXd&, const ArrayXd&, const ArrayXd&, double) const; }; class glmLink { protected: //@{ R functions from the family, as a fall-back Rcpp::Function d_linkFun, d_linkInv, d_muEta; //@} Rcpp::Environment d_rho; public: glmLink(Rcpp::List&); virtual ~glmLink() {} virtual const ArrayXd linkFun(const ArrayXd&) const; virtual const ArrayXd linkInv(const ArrayXd&) const; virtual const ArrayXd muEta(const ArrayXd&) const; }; class cauchitLink : public glmLink { public: cauchitLink(Rcpp::List& ll) : glmLink(ll) {} const ArrayXd linkFun(const ArrayXd&) const; const ArrayXd linkInv(const ArrayXd&) const; const ArrayXd muEta(const ArrayXd&) const; }; class cloglogLink : public glmLink { public: cloglogLink(Rcpp::List& ll) : glmLink(ll) {} // const ArrayXd linkFun(const ArrayXd&) const; const ArrayXd linkInv(const ArrayXd&) const; const ArrayXd muEta(const ArrayXd&) const; }; class identityLink : public glmLink { public: identityLink(Rcpp::List& ll) : glmLink(ll) {} const ArrayXd linkFun(const ArrayXd&) const; const ArrayXd linkInv(const ArrayXd&) const; const ArrayXd muEta(const ArrayXd&) const; }; class inverseLink : public glmLink { public: inverseLink(Rcpp::List& ll) : glmLink(ll) {} const ArrayXd linkFun(const ArrayXd&) const; const ArrayXd linkInv(const ArrayXd&) const; const ArrayXd muEta(const ArrayXd&) const; }; class logLink : public glmLink { public: logLink(Rcpp::List& ll) : glmLink(ll) {} const ArrayXd linkFun(const ArrayXd&) const; const ArrayXd linkInv(const ArrayXd&) const; const ArrayXd muEta(const ArrayXd&) const; }; class logitLink : public glmLink { public: logitLink(Rcpp::List& ll) : glmLink(ll) {} const ArrayXd linkFun(const ArrayXd&) const; const ArrayXd linkInv(const ArrayXd&) const; const ArrayXd muEta(const ArrayXd&) const; }; class probitLink : public glmLink { public: probitLink(Rcpp::List& ll) : glmLink(ll) {} const ArrayXd linkFun(const ArrayXd&) const; const ArrayXd linkInv(const ArrayXd&) const; const ArrayXd muEta(const ArrayXd&) const; }; class glmFamily { protected: std::string d_family, d_linknam; /**< as in the R glmFamily object */ glmDist *d_dist; glmLink *d_link; public: glmFamily(Rcpp::List ll); ~glmFamily(); /**< explicit destructor to call delete on d_dist and d_link */ const std::string& fam() const {return d_family;} const std::string& lnk() const {return d_linknam;} //@{ Application of functions from the family using compiled code when available const ArrayXd devResid(const ArrayXd&, const ArrayXd&, const ArrayXd&) const; const ArrayXd linkFun(const ArrayXd& mu) const {return d_link->linkFun(mu);} const ArrayXd linkInv(const ArrayXd& eta) const {return d_link->linkInv(eta);} const ArrayXd muEta(const ArrayXd& eta) const {return d_link->muEta(eta);} const ArrayXd variance(const ArrayXd& mu) const {return d_dist->variance(mu);} double aic(const ArrayXd&, const ArrayXd&, const ArrayXd&, const ArrayXd&, double) const; double theta() const {return d_dist->theta();} void setTheta(const double& theta) {d_dist->setTheta(theta);} //@} }; } #endif /* LME4_GLMFAMILY_H */ lme4/src/optimizer.cpp0000644000176000001440000002405612273467214014477 0ustar ripleyusers// // Nelder_Mead.cpp: implementation of Nelder-Mead optimization algorithm // // Based on the files nldrmd.h, nldrmd.c, stop.c and nlopt-util.h from NLopt 2.2.4 // Steven G. Johnson, The NLopt nonlinear-optimization package, // http://ab-initio.mit.edu/nlopt // // Original implementation Copyright (C) 2007-2011 Massachusetts Institute of Technology // Modifications Copyright (C) 2011 Douglas Bates, Martin Maechler and Ben Bolker // // This file is part of lme4. #include "optimizer.h" namespace optimizer { using std::invalid_argument; using std::runtime_error; typedef VectorXd::Scalar Scalar; typedef VectorXd::Index Index; /** * Determine if two values are approximately equal relative to floating-point precision * * @param a first value to compare * @param b second value to compare * * @return true if a and b are approximately equal else false */ static bool close(const Scalar& a, const Scalar& b) { return (std::abs(a - b) <= 1e-13 * (std::abs(a) + std::abs(b))); } /** * * * @param lb lower bounds * @param ub upper bounds * @param xstep initial step sizes * @param x initial parameter vector * @param f value of function at initial parameter vector */ Nelder_Mead::Nelder_Mead(const VectorXd& lb, const VectorXd& ub, const VectorXd& xstep, const VectorXd& x, const nl_stop& stp) : d_lb( lb), d_ub( ub), d_xstep( xstep), d_x( x), d_n( x.size()), d_pts( d_n, d_n + 1), d_vals( d_n + 1), d_c( d_n), d_xcur( d_n), d_xeval( x), d_minf( std::numeric_limits::infinity()), d_stage( nm_restart), d_stop( stp), d_verb( 10) { d_stop.setForce_stop( 0); // BMB: this was undefined, bad news on Win64 if (!d_n || d_lb.size() != d_n || d_ub.size() != d_n || d_xstep.size() != d_n) throw invalid_argument("dimension mismatch"); if (((d_x - d_lb).array() < 0).any() || ((d_ub - d_x).array() < 0).any()) throw std::invalid_argument("initial x is not a feasible point"); d_stop.resetEvals(); init_pos = 0; for (int i = 0; i <= d_n; ++i) d_vals[i] = std::numeric_limits::min(); d_pts = d_x.replicate(1, d_n + 1); for (Index i = 0; i < d_n; ++i) { // generate and check the initial positions Index j(i + 1); d_pts(i, j) += d_xstep[i]; if (d_pts(i, j) > d_ub[i]) { d_pts(i, j) = (d_ub[i] - d_x[i] > std::abs(d_xstep[i]) * 0.1) ? d_ub[i] // ub is too close to pt, go in other direction : d_x[i] - std::abs(d_xstep[i]); } if (d_pts(i,j) < d_lb[i]) { if (d_x[i] - d_lb[i] > std::abs(d_xstep[i]) * 0.1) d_pts(i,j) = d_lb[i]; else { // lb is too close to pt, go in other direction d_pts(i,j) = d_x[i] + std::abs(d_xstep[i]); if (d_pts(i, j) > d_ub[i]) // go towards farther of lb, ub */ d_pts(i, j) = 0.5 * ((d_ub[i] - d_x[i] > d_x[i] - d_lb[i] ? d_ub[i] : d_lb[i]) + d_x[i]); } } if (close(d_pts(i,j), d_x[i])) throw std::invalid_argument("cannot generate feasible simplex"); } } /** * Install the function value at d_xeval * * @param f value of function at d_xeval * * @return status */ nm_status Nelder_Mead::newf(const Scalar& f) { d_stop.incrEvals(); if (d_verb > 0 && (d_stop.ev() % d_verb) == 0) Rcpp::Rcout << "(NM) " << d_stop.ev() << ": " << "f = " << value() << " at " << d_x.adjoint() << std::endl; if (d_stop.forced()) { if (d_verb==1) { Rcpp::Rcout << "(NM) stop_forced" << std::endl; } return nm_forced; } if (f < d_minf) { d_minf = f; d_x = d_xeval; // save the value generating current minimum if (d_minf < d_stop.minfMax()) { if (d_verb==1) { Rcpp::Rcout << "(NM) nm_minf_max: " << d_minf << ", " << d_stop.minfMax() << ", " << d_x << std::endl; } return nm_minf_max; } } if (d_stop.evals()) { if (d_verb==1) { Rcpp::Rcout << "(NM) nm_evals" << std::endl; } return nm_evals; } if (init_pos <= d_n) { if (d_verb==1) { Rcpp::Rcout << "(NM) init_pos <= d_n" << std::endl; } return init(f); } switch (d_stage) { case nm_restart: return restart(f); case nm_postreflect: return postreflect(f); case nm_postexpand: return postexpand(f); case nm_postcontract: return postcontract(f); } return nm_active; // -Wall } /** * Initialization of d_vals from the positions in d_pts; * * @param f function value * * @return status */ nm_status Nelder_Mead::init(const Scalar& f) { if (init_pos > d_n) throw std::runtime_error("init called after n evaluations"); d_vals[init_pos++] = f; if (init_pos > d_n) return restart(f); d_xeval = d_pts.col(init_pos); return nm_active; } /** * Recompute the high/low function values (d_fh and d_fl) and indices * (d_ih and d_il) plus the centroid of the n-1 simplex opposite the high * vertex. Check if the simplex has collapsed. If not, attempt a reflection. * * @param f function value * * @return status */ nm_status Nelder_Mead::restart(const Scalar& f) { d_fl = d_vals.minCoeff(&d_il); d_fh = d_vals.maxCoeff(&d_ih); d_c = (d_pts.rowwise().sum() - d_pts.col(d_ih)) / d_n; // compute centroid // check for x convergence by calculating the maximum absolute // deviation from the centroid for each coordinate in the simplex if (d_stop.x(VectorXd::Constant(d_n, 0.), (d_pts.colwise() - d_c).array().abs().rowwise().maxCoeff())) return nm_xcvg; if (!reflectpt(d_xcur, d_c, alpha, d_pts.col(d_ih))) return nm_xcvg; d_xeval = d_xcur; d_stage = nm_postreflect; return nm_active; } nm_status Nelder_Mead::postreflect(const Scalar& f) { // Rcpp::Rcout << "postreflect: "; if (f < d_fl) { // new best point, try to expand if (!reflectpt(d_xeval, d_c, gamm, d_pts.col(d_ih))) return nm_xcvg; // Rcpp::Rcout << "New best point" << std::endl; d_stage = nm_postexpand; f_old = f; return nm_active; } if (f < d_fh) { // accept new point // Rcpp::Rcout << "Accept new point" << std::endl; d_vals[d_ih] = f; d_pts.col(d_ih) = d_xeval; return restart(f); } // new worst point, contract // Rcpp::Rcout << "New worst point" << std::endl; if (!reflectpt(d_xcur, d_c, d_fh <= f ? -beta : beta, d_pts.col(d_ih))) return nm_xcvg; f_old = f; d_xeval = d_xcur; d_stage = nm_postcontract; return nm_active; } nm_status Nelder_Mead::postexpand(const Scalar& f) { if (f < d_vals[d_ih]) { // expanding improved // Rcpp::Rcout << "successful expand" << std::endl; d_pts.col(d_ih) = d_xeval; d_vals[d_ih] = f; } else { // Rcpp::Rcout << "unsuccessful expand" << std::endl; d_pts.col(d_ih) = d_xcur; d_vals[d_ih] = f_old; } return restart(f); } nm_status Nelder_Mead::postcontract(const Scalar& f) { if (f < f_old && f < d_fh) { // Rcpp::Rcout << "successful contraction:" << std::endl; d_pts.col(d_ih) = d_xeval; d_vals[d_ih] = f; return restart(f); } // Rcpp::Rcout << "unsuccessful contraction, shrink simplex" << std::endl; for (Index i = 0; i <= d_n; ++i) { if (i != d_il) { if (!reflectpt(d_xeval, d_pts.col(d_il), -delta, d_pts.col(i))) return nm_xcvg; d_pts.col(i) = d_xeval; } } init_pos = 0; d_xeval = d_pts.col(0); return nm_active; } /* Perform the reflection xnew = c + scale * (c - xold), returning 0 if xnew == c or xnew == xold (coincident points), 1 otherwise. The reflected point xnew is "pinned" to the lower and upper bounds (lb and ub), as suggested by J. A. Richardson and J. L. Kuester, "The complex method for constrained optimization," Commun. ACM 16(8), 487-489 (1973). This is probably a suboptimal way to handle bound constraints, but I don't know a better way. The main danger with this is that the simplex might collapse into a lower-dimensional hyperplane; this danger can be ameliorated by restarting (as in subplex), however. */ bool Nelder_Mead::reflectpt(VectorXd& xnew, const VectorXd& c, const Scalar& scale, const VectorXd& xold) { xnew = c + scale * (c - xold); bool equalc = true, equalold = true; for (Index i = 0; i < d_n; ++i) { Scalar newx = std::min(std::max(xnew[i], d_lb[i]), d_ub[i]); equalc = equalc && close(newx, d_c[i]); equalold = equalold && close(newx, xold[i]); xnew[i] = newx; } return !(equalc || equalold); } nl_stop::nl_stop(const VectorXd& xtol) : xtol_abs( xtol), maxeval( 300), minf_max( std::numeric_limits::min()), ftol_rel( 1e-15), xtol_rel( 1e-7) { } bool nl_stop::x(const VectorXd& x, const VectorXd& oldx) const { for (Index i = 0; i < x.size(); ++i) if (!relstop(oldx[i], x[i], xtol_rel, xtol_abs[i])) return false; return true; } bool nl_stop::dx(const VectorXd& x, const VectorXd& dx) const { for (Index i = 0; i < x.size(); ++i) if (!relstop(x[i] - dx[i], x[i], xtol_rel, xtol_abs[i])) return false; return true; } bool nl_stop::xs(const VectorXd& xs, const VectorXd& oldxs, const VectorXd& scale_min, const VectorXd& scale_max) const { for (Index i = 0; i < xs.size(); ++i) if (relstop(sc(oldxs[i], scale_min[i], scale_max[i]), sc(xs[i], scale_min[i], scale_max[i]), xtol_rel, xtol_abs[i])) return true; return false; } Golden::Golden(const Scalar& lower, const Scalar& upper) : d_lower(lower), d_upper(upper) { if (lower >= upper) throw invalid_argument("lower >= upper"); d_invratio = 2./(1. + std::sqrt(5.)); double range = upper - lower; d_x[0] = lower + range * (1. - d_invratio); d_x[1] = lower + range * d_invratio; d_init = true; d_ll = true; } void Golden::newf(const Scalar& fv) { Rcpp::Rcout << "f = " << fv << " at x = " << xeval() << std::endl; d_f[d_ll ? 0 : 1] = fv; if (d_init) { d_init = false; d_ll = false; return; } if (d_f[0] > d_f[1]) { // discard left portion of interval d_lower = d_x[0]; d_x[0] = d_x[1]; d_f[0] = d_f[1]; d_x[1] = d_lower + (d_upper - d_lower) * d_invratio; d_ll = false; } else { d_upper = d_x[1]; d_x[1] = d_x[0]; d_f[1] = d_f[0]; d_x[0] = d_lower + (d_upper - d_lower) * (1 - d_invratio); d_ll = true; } } } lme4/src/external.cpp0000644000176000001440000010651412273467214014277 0ustar ripleyusers// external.cpp: externally .Call'able functions in lme4 // // Copyright (C) 2011-2012 Douglas Bates, Martin Maechler and Ben Bolker // // This file is part of lme4. #include "predModule.h" #include "respModule.h" #include "optimizer.h" extern "C" { typedef Eigen::VectorXi iVec; typedef Eigen::Map MiVec; typedef Eigen::MatrixXd Mat; typedef Eigen::Map MMat; typedef Eigen::VectorXd Vec; typedef Eigen::Map MVec; typedef Eigen::ArrayXd Ar1; typedef Eigen::Map MAr1; typedef Eigen::ArrayXXd Ar2; typedef Eigen::Map MAr2; using Rcpp::CharacterVector; using Rcpp::Environment; using Rcpp::IntegerVector; using Rcpp::Language; using Rcpp::List; using Rcpp::Named; using Rcpp::NumericVector; using Rcpp::XPtr; using Rcpp::as; using Rcpp::wrap; using glm::glmFamily; using lme4::glmResp; using lme4::lmResp; using lme4::lmerResp; using lme4::merPredD; using lme4::nlsResp; using optimizer::Golden; using optimizer::Nelder_Mead; using optimizer::nm_status; using std::runtime_error; // utilities SEXP allPerm_int(SEXP v_) { BEGIN_RCPP; iVec v(as(v_)); // forces a copy int sz(v.size()); std::vector vec; std::sort(v.data(), v.data() + sz); do { vec.push_back(iVec(v)); } while (std::next_permutation(v.data(), v.data() + sz)); int nperm(vec.size()); List allPerm(nperm); for (int j = 0; j < nperm; ++j) allPerm[j] = wrap(vec[j]); return allPerm; END_RCPP; } SEXP Eigen_SSE() { BEGIN_RCPP; return wrap(Eigen::SimdInstructionSetsInUse()); END_RCPP; } // generalized linear model (and generalized linear mixed model) response SEXP glm_Create(SEXP fam, SEXP y, SEXP weights, SEXP offset, SEXP mu, SEXP sqrtXwt, SEXP sqrtrwt, SEXP wtres, SEXP eta, SEXP n) { BEGIN_RCPP; glmResp *ans = new glmResp(List(fam), y, weights, offset, mu, sqrtXwt, sqrtrwt, wtres, eta, n); return wrap(XPtr(ans, true)); END_RCPP; } SEXP glm_aic(SEXP ptr_) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr_)->aic()); END_RCPP; } SEXP glm_setN(SEXP ptr_, SEXP n) { BEGIN_RCPP; XPtr(ptr_)->setN(as(n)); END_RCPP; } SEXP glm_devResid(SEXP ptr_) { BEGIN_RCPP; return wrap(XPtr(ptr_)->devResid()); END_RCPP; } SEXP glm_family(SEXP ptr_) { BEGIN_RCPP; return wrap(XPtr(ptr_)->family()); END_RCPP; } SEXP glm_link(SEXP ptr_) { BEGIN_RCPP; return wrap(XPtr(ptr_)->link()); END_RCPP; } SEXP glm_muEta(SEXP ptr_) { BEGIN_RCPP; return wrap(XPtr(ptr_)->muEta()); END_RCPP; } SEXP glm_resDev(SEXP ptr_) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr_)->resDev()); END_RCPP; } SEXP glm_setTheta(SEXP ptr, SEXP newtheta) { BEGIN_RCPP; XPtr(ptr)->setTheta(::Rf_asReal(newtheta)); END_RCPP; } SEXP glm_sqrtWrkWt(SEXP ptr_) { BEGIN_RCPP; return wrap(XPtr(ptr_)->sqrtWrkWt()); END_RCPP; } SEXP glm_theta(SEXP ptr) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr)->theta()); END_RCPP; } SEXP glm_updateWts(SEXP ptr_) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr_)->updateWts()); END_RCPP; } SEXP glm_variance(SEXP ptr_) { BEGIN_RCPP; return wrap(XPtr(ptr_)->variance()); END_RCPP; } SEXP glm_wrkResids(SEXP ptr_) { BEGIN_RCPP; return wrap(XPtr(ptr_)->wrkResids()); END_RCPP; } SEXP glm_wrkResp(SEXP ptr_) { BEGIN_RCPP; return wrap(XPtr(ptr_)->wrkResp()); END_RCPP; } SEXP glm_wtWrkResp(SEXP ptr_) { BEGIN_RCPP; return wrap(XPtr(ptr_)->wtWrkResp()); END_RCPP; } SEXP glm_Laplace(SEXP ptr_, SEXP ldL2, SEXP ldRX2, SEXP sqrL) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr_)->Laplace(::Rf_asReal(ldL2), ::Rf_asReal(ldRX2), ::Rf_asReal(sqrL))); END_RCPP; } SEXP glm_updateMu(SEXP ptr_, SEXP gamma) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr_)->updateMu(as(gamma))); END_RCPP; } // glm family objects SEXP glmFamily_Create(SEXP fam_) { BEGIN_RCPP; glmFamily *ans = new glmFamily(List(fam_)); return wrap(XPtr(ans, true)); END_RCPP; } SEXP glmFamily_link(SEXP ptr, SEXP mu) { BEGIN_RCPP; return wrap(XPtr(ptr)->linkFun(as(mu))); END_RCPP; } SEXP glmFamily_linkInv(SEXP ptr, SEXP eta) { BEGIN_RCPP; return wrap(XPtr(ptr)->linkInv(as(eta))); END_RCPP; } SEXP glmFamily_devResid(SEXP ptr, SEXP y, SEXP mu, SEXP wt) { BEGIN_RCPP; return wrap(XPtr(ptr)->devResid(as(y), as(mu), as(wt))); END_RCPP; } SEXP glmFamily_aic(SEXP ptr, SEXP y, SEXP n, SEXP mu, SEXP wt, SEXP dev) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr)->aic(as(y), as(n), as(mu), as(wt), ::Rf_asReal(dev))); END_RCPP; } SEXP glmFamily_muEta(SEXP ptr, SEXP eta) { BEGIN_RCPP; return wrap(XPtr(ptr)->muEta(as(eta))); END_RCPP; } SEXP glmFamily_setTheta(SEXP ptr, SEXP ntheta) { BEGIN_RCPP; XPtr(ptr)->setTheta(::Rf_asReal(ntheta)); END_RCPP; } SEXP glmFamily_theta(SEXP ptr) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr)->theta()); END_RCPP; } SEXP glmFamily_variance(SEXP ptr, SEXP mu) { BEGIN_RCPP; return wrap(XPtr(ptr)->variance(as(mu))); END_RCPP; } static inline double pwrss(lmResp *rp, merPredD *pp, double fac) { return rp->wrss() + (fac ? pp->sqrL(fac) : pp->u0().squaredNorm()); } static double internal_glmerWrkIter(merPredD *pp, glmResp *rp, bool uOnly) { int debug=0; // !=0 to enable if (debug) Rcpp::Rcout << "(igWI, pre-updateXwts) Xwts: min: " << pp->Xwts().minCoeff() << " sqrtWrkWt: min: " << rp->sqrtWrkWt().minCoeff() << std::endl; pp->updateXwts(rp->sqrtWrkWt()); if (debug) Rcpp::Rcout << "(igWI) Xwts: min: " << pp->Xwts().minCoeff() << " max: " << pp->Xwts().maxCoeff() << std::endl; pp->updateDecomp(); // n.b. next line fails to compile ... 'no method' // Rcpp::Rcout << "\nd_L after updateDecomp: " << pp->L() << std::endl; // Rcpp::Rcout << "resDev after updateDecomp:" << rp->resDev() << std::endl; // Rcpp::Rcout << "sqrL:" << pp->sqrL(1.) << std::endl; // Rcpp::Rcout << "delb 3:\n" << pp->delb() << std::endl; // Rcpp::Rcout << "min delu at pt 3 of gwi: " << pp->delu().minCoeff() << std::endl; // Rcpp::Rcout << "max delu at pt 3 of gwi: " << pp->delu().maxCoeff() << std::endl; pp->updateRes(rp->wtWrkResp()); // Rcpp::Rcout << "\nresDev after updateRes:" << rp->resDev() << std::endl; // Rcpp::Rcout << "sqrL:" << pp->sqrL(1.) << std::endl; // Rcpp::Rcout << "delb 4:\n" << pp->delb() << std::endl; // Rcpp::Rcout << "min delu at pt 4 of gwi: " << pp->delu().minCoeff() << std::endl; // Rcpp::Rcout << "max delu at pt 4 of gwi: " << pp->delu().maxCoeff() << std::endl; if (uOnly) pp->solveU(); else pp->solve(); if (debug) { Rcpp::Rcout << "(igWI)" << " delu_min: " << pp->delu().minCoeff() << "; delu_max: " << pp->delu().maxCoeff() << "; delb_min: " << pp->delb().minCoeff() << "; delb_max: " << pp->delb().maxCoeff() << std::endl; // if (verb) } rp->updateMu(pp->linPred(1.)); if (debug) Rcpp::Rcout << "(igWI) mu: min: " << rp->mu().minCoeff() << " max: " << rp->mu().maxCoeff() << std::endl; return rp->resDev() + pp->sqrL(1.); } static void pwrssUpdate(glmResp *rp, merPredD *pp, bool uOnly, double tol, int verbose) { //Rcpp::Rcout << "\nFirst pwrssUpdate resDev: " << rp->resDev() << std::endl; double oldpdev=std::numeric_limits::max(); double pdev; int maxit = 30, maxstephalfit = 10; bool cvgd = false, verb = verbose > 2, moreverb = verbose > 10; // pdev = oldpdev; // define so debugging statements work on first step for (int i = 0; i < maxit; i++) { if (verb) { Rcpp::Rcout << "*** pwrssUpdate step " << i << std::endl; // Rcpp::Rcout << "\nmin delu at iteration " << i << ": " << pp->delu().minCoeff() << std::endl; // Rcpp::Rcout << "\nmax delu at iteration " << i << ": " << pp->delu().maxCoeff() << std::endl; // Rcpp::Rcout << "\nresDev before dels, iter: " << i << ", " << rp->resDev() << std::endl; // FIXME: would like to print this in row, not column, format // // Rcpp::Rcout << "before update:" << "pdev = " << pdev << std::endl; // if (verb) } Vec olddelu(pp->delu()), olddelb(pp->delb()); // Rcpp::Rcout << "\ndelb 2: " << pp->delb() << std::endl; // Rcpp::Rcout << "\nresDev before internal_glmerWrkIter, iter: " << i << ", " << rp->resDev() << std::endl; double pdev=internal_glmerWrkIter(pp, rp, uOnly); // Rcpp::Rcout << "\ndelb 3: " << pp->delb() << std::endl; // Rcpp::Rcout << "\nresDev after internal_glmerWrkIter, iter: " << i << ", " << rp->resDev() << std::endl; // Rcpp::Rcout << i << ": " << pdev << std::endl; // if (verb) // Rcpp::Rcout << "i = " << i << ", pdev = " << pdev << std::endl; // if (verb) if (std::abs((oldpdev - pdev) / pdev) < tol) {cvgd = true; break;} // if (pdev != pdev) Rcpp::Rcout << "nan detected" << std::endl; // if (isnan(pdev)) Rcpp::Rcout << "nan detected" << std::endl; // trying to detect nan; may be hard to do it completely portably, // and hard to detect in advance (i.e. what conditions lead to // nan from internal_glmerWrkIter ... ?) // http://stackoverflow.com/questions/570669/checking-if-a-double-or-float-is-nan-in-c // check use of isnan() in base R code, or other Rcpp code?? #define isNAN(a) (a!=a) if (isNAN(pdev) || (pdev > oldpdev)) { // PWRSS step led to _larger_ deviation, or nan; try step halving if (verb) Rcpp::Rcout << "\npwrssUpdate: Entering step halving loop" << std::endl; for (int k = 0; k < maxstephalfit && (isNAN(pdev) || pdev > oldpdev); k++) { pp->setDelu((olddelu + pp->delu())/2.); if (!uOnly) pp->setDelb((olddelb + pp->delb())/2.); // Rcpp::Rcout << "min delu at pt 2 of step halving iteration " << k << ": " << pp->delu().minCoeff() << std::endl; // Rcpp::Rcout << "max delu at pt 2 of step halving iteration " << k << ": " << pp->delu().maxCoeff() << std::endl; // pdev = internal_glmerWrkIter(pp, rp, uOnly); //pdev <- rp->resDev() + pp->sqrL(1.); // experiment!! SCW rp->updateMu(pp->linPred(1.)); pdev = rp->resDev() + pp->sqrL(1.); if (moreverb) { Rcpp::Rcout << "step-halving iteration " << k << ": pdev=" << pdev << "; delu_min: " << pp->delu().minCoeff() << "; delu_max: " << pp->delu().maxCoeff() << "; delb_min: " << pp->delb().minCoeff() << "; delb_max: " << pp->delb().maxCoeff() << std::endl; } // if (moreverb) } if (isNAN(pdev) || (pdev - oldpdev) > tol) // FIXME: fill in max halfsetp iters in error statement throw runtime_error("(maxstephalfit) PIRLS step-halvings failed to reduce deviance in pwrssUpdate"); } // step-halving //Rcpp::Rcout << "\ndelb 5: " << pp->delb() << std::endl; oldpdev = pdev; } // pwrss loop if (!cvgd) throw runtime_error("pwrssUpdate did not converge in 30 iterations"); } SEXP glmerLaplace(SEXP pp_, SEXP rp_, SEXP nAGQ_, SEXP tol_, SEXP verbose_) { BEGIN_RCPP; XPtr rp(rp_); XPtr pp(pp_); //Rcpp::Rcout << "\nglmerLaplace resDev: " << rp->resDev() << std::endl; //Rcpp::Rcout << "\ndelb 1: " << pp->delb() << std::endl; pwrssUpdate(rp, pp, ::Rf_asInteger(nAGQ_), ::Rf_asReal(tol_), ::Rf_asInteger(verbose_)); //Rcpp::Rcout << "\ndelb 2: " << pp->delb() << std::endl; //Rcpp::Rcout << "\nldL2: " << pp->ldL2() << std::endl; //Rcpp::Rcout << "\nldRX2: " << pp->ldRX2() << std::endl; //Rcpp::Rcout << "\nsqrL: " << pp->sqrL(1.) << std::endl; return ::Rf_ScalarReal(rp->Laplace(pp->ldL2(), pp->ldRX2(), pp->sqrL(1.))); END_RCPP; } static Ar1 devcCol(const MiVec& fac, const Ar1& u, const Ar1& devRes) { Ar1 ans(u.square()); for (int i = 0; i < devRes.size(); ++i) ans[fac[i] - 1] += devRes[i]; return ans; } static double sqrt2pi = std::sqrt(2. * PI); SEXP glmerAGQ(SEXP pp_, SEXP rp_, SEXP tol_, SEXP GQmat_, SEXP fac_, SEXP verbose_) { BEGIN_RCPP; XPtr rp(rp_); XPtr pp(pp_); const MiVec fac(as(fac_)); double tol(::Rf_asReal(tol_)); double verb(::Rf_asReal(verbose_)); if (fac.size() != rp->mu().size()) throw std::invalid_argument("size of fac must match dimension of response vector"); pwrssUpdate(rp, pp, true, tol, verb); // should be a no-op const Ar1 devc0(devcCol(fac, pp->u(1.), rp->devResid())); const unsigned int q(pp->u0().size()); if (pp->L().factor()->nzmax != q) throw std::invalid_argument("AGQ only defined for a single scalar random-effects term"); const Ar1 sd(MAr1((double*)pp->L().factor()->x, q).inverse()); const MMat GQmat(as(GQmat_)); Ar1 mult(q); mult.setZero(); for (int i = 0; i < GQmat.rows(); ++i) { double zknot(GQmat(i, 0)); if (zknot == 0) mult += Ar1::Constant(q, GQmat(i, 1)); else { pp->setU0(zknot * sd); // to be added to current delu rp->updateMu(pp->linPred(1.)); mult += (-0.5 * (devcCol(fac, pp->u(1.), rp->devResid()) - devc0) - GQmat(i, 2)).exp() * GQmat(i, 1)/sqrt2pi; } } pp->setU0(Vec::Zero(q)); // restore settings from pwrssUpdate; rp->updateMu(pp->linPred(1.)); return ::Rf_ScalarReal(devc0.sum() + pp->ldL2() - 2 * std::log(mult.prod())); END_RCPP; } void nstepFac(nlsResp *rp, merPredD *pp, int verb) { double prss0(pwrss(rp, pp, 0.)); for (double fac = 1.; fac > 0.001; fac /= 2.) { double prss1 = rp->updateMu(pp->linPred(fac)) + pp->sqrL(fac); if (verb > 3) ::Rprintf("prss0=%10g, diff=%10g, fac=%6.4f\n", prss0, prss0 - prss1, fac); if (prss1 < prss0) { pp->installPars(fac); return; } } throw runtime_error("step factor reduced below 0.001 without reducing pwrss"); } #define NMAXITER 300 static void prssUpdate(nlsResp *rp, merPredD *pp, int verb, bool uOnly, double tol) { bool cvgd(false); for (int it=0; it < NMAXITER; ++it) { rp->updateMu(pp->linPred(0.)); pp->updateXwts(rp->sqrtXwt()); pp->updateDecomp(); pp->updateRes(rp->wtres()); double ccrit((uOnly ? pp->solveU() : pp->solve())/pwrss(rp, pp, 0.)); if (verb > 3) ::Rprintf("ccrit=%10g, tol=%10g\n", ccrit, tol); if (ccrit < tol) { cvgd = true; break; } nstepFac(rp, pp, verb); } if (!cvgd) throw runtime_error("prss failed to converge in 300 iterations"); } SEXP nlmerLaplace(SEXP pp_, SEXP rp_, SEXP theta_, SEXP u0_, SEXP beta0_, SEXP verbose_, SEXP uOnly_, SEXP tol_) { BEGIN_RCPP; XPtr rp(rp_); XPtr pp(pp_); pp->setTheta(as(theta_)); pp->setU0(as(u0_)); pp->setBeta0(as(beta0_)); prssUpdate(rp, pp, ::Rf_asInteger(verbose_), ::Rf_asLogical(uOnly_), ::Rf_asReal(tol_)); return ::Rf_ScalarReal(rp->Laplace(pp->ldL2(), pp->ldRX2(), pp->sqrL(1.))); END_RCPP; } SEXP golden_Create(SEXP lower_, SEXP upper_) { BEGIN_RCPP; Golden *ans = new Golden(::Rf_asReal(lower_), ::Rf_asReal(upper_)); return wrap(XPtr(ans, true)); END_RCPP; } SEXP golden_newf(SEXP ptr_, SEXP f_) { BEGIN_RCPP; XPtr(ptr_)->newf(::Rf_asReal(f_)); END_RCPP; } SEXP golden_xeval(SEXP ptr_) { BEGIN_RCPP; return wrap(XPtr(ptr_)->xeval()); END_RCPP; } SEXP golden_value(SEXP ptr_) { BEGIN_RCPP; return wrap(XPtr(ptr_)->value()); END_RCPP; } SEXP golden_xpos(SEXP ptr_) { BEGIN_RCPP; return wrap(XPtr(ptr_)->xpos()); END_RCPP; } SEXP isNullExtPtr(SEXP Ptr) { void *ptr = R_ExternalPtrAddr(Ptr); // Rcpp::Rcout << "In isNullExtPtr, address is " << ptr << std::endl; return ::Rf_ScalarLogical(ptr == (void*)NULL); } // linear model response (also the base class for other response classes) SEXP lm_Create(SEXP y, SEXP weights, SEXP offset, SEXP mu, SEXP sqrtXwt, SEXP sqrtrwt, SEXP wtres) { BEGIN_RCPP; lmResp *ans = new lmResp(y, weights, offset, mu, sqrtXwt, sqrtrwt, wtres); return wrap(XPtr(ans, true)); END_RCPP; } SEXP lm_setOffset(SEXP ptr_, SEXP offset) { BEGIN_RCPP; XPtr(ptr_)->setOffset(as(offset)); END_RCPP; } SEXP lm_setResp(SEXP ptr_, SEXP resp) { BEGIN_RCPP; XPtr(ptr_)->setResp(as(resp)); END_RCPP; } SEXP lm_setWeights(SEXP ptr_, SEXP weights) { BEGIN_RCPP; XPtr(ptr_)->setWeights(as(weights)); END_RCPP; } SEXP lm_wrss(SEXP ptr_) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr_)->wrss()); END_RCPP; } SEXP lm_updateMu(SEXP ptr_, SEXP gamma) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr_)->updateMu(as(gamma))); END_RCPP; } // linear mixed-effects model response SEXP lmer_Create(SEXP y, SEXP weights, SEXP offset, SEXP mu, SEXP sqrtXwt, SEXP sqrtrwt, SEXP wtres) { BEGIN_RCPP; lmerResp *ans = new lmerResp(y, weights, offset, mu, sqrtXwt, sqrtrwt, wtres); return wrap(XPtr(ans, true)); END_RCPP; } SEXP lmer_setREML(SEXP ptr_, SEXP REML) { BEGIN_RCPP; int reml = ::Rf_asInteger(REML); XPtr(ptr_)->setReml(reml); return ::Rf_ScalarInteger(reml); END_RCPP; } SEXP lmer_Laplace(SEXP ptr_, SEXP ldL2, SEXP ldRX2, SEXP sqrL, SEXP sigma_sq) { BEGIN_RCPP; if (Rf_isNull(sigma_sq)) return ::Rf_ScalarReal(XPtr(ptr_)->Laplace(::Rf_asReal(ldL2), ::Rf_asReal(ldRX2), ::Rf_asReal(sqrL))); return ::Rf_ScalarReal(XPtr(ptr_)->Laplace(::Rf_asReal(ldL2), ::Rf_asReal(ldRX2), ::Rf_asReal(sqrL), ::Rf_asReal(sigma_sq))); END_RCPP; } static double lmer_dev(XPtr ppt, XPtr rpt, const Eigen::VectorXd& theta) { ppt->setTheta(theta); ppt->updateXwts(rpt->sqrtXwt()); ppt->updateDecomp(); rpt->updateMu(ppt->linPred(0.)); ppt->updateRes(rpt->wtres()); ppt->solve(); rpt->updateMu(ppt->linPred(1.)); return rpt->Laplace(ppt->ldL2(), ppt->ldRX2(), ppt->sqrL(1.)); } SEXP lmer_Deviance(SEXP pptr_, SEXP rptr_, SEXP theta_) { BEGIN_RCPP; XPtr rpt(rptr_); XPtr ppt(pptr_); return ::Rf_ScalarReal(lmer_dev(ppt, rpt, as(theta_))); END_RCPP; } SEXP lmer_opt1(SEXP pptr_, SEXP rptr_, SEXP lower_, SEXP upper_) { BEGIN_RCPP; XPtr rpt(rptr_); XPtr ppt(pptr_); Eigen::VectorXd th(1); optimizer::Golden gold(::Rf_asReal(lower_), ::Rf_asReal(upper_)); for (int i = 0; i < 30; ++i) { th[0] = gold.xeval(); gold.newf(lmer_dev(ppt, rpt, th)); } return List::create(Named("theta") = ::Rf_ScalarReal(gold.xpos()), Named("objective") = ::Rf_ScalarReal(gold.value())); END_RCPP; } // dense predictor module for mixed-effects models SEXP merPredDCreate(SEXP Xs, SEXP Lambdat, SEXP LamtUt, SEXP Lind, SEXP RZX, SEXP Ut, SEXP Utr, SEXP V, SEXP VtV, SEXP Vtr, SEXP Xwts, SEXP Zt, SEXP beta0, SEXP delb, SEXP delu, SEXP theta, SEXP u0) { BEGIN_RCPP; merPredD *ans = new merPredD(Xs, Lambdat, LamtUt, Lind, RZX, Ut, Utr, V, VtV, Vtr, Xwts, Zt, beta0, delb, delu, theta, u0); return wrap(XPtr(ans, true)); END_RCPP; } // setters SEXP merPredDsetTheta(SEXP ptr, SEXP theta) { BEGIN_RCPP; XPtr(ptr)->setTheta(as(theta)); return theta; END_RCPP; } SEXP merPredDsetBeta0(SEXP ptr, SEXP beta0) { BEGIN_RCPP; XPtr(ptr)->setBeta0(as(beta0)); END_RCPP; } SEXP merPredDsetDelu(SEXP ptr, SEXP delu) { BEGIN_RCPP; XPtr(ptr)->setDelu(as(delu)); END_RCPP; } SEXP merPredDsetDelb(SEXP ptr, SEXP delb) { BEGIN_RCPP; XPtr(ptr)->setDelb(as(delb)); END_RCPP; } // getters SEXP merPredDCcNumer(SEXP ptr) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr)->CcNumer()); END_RCPP; } SEXP merPredDL(SEXP ptr) { BEGIN_RCPP; return wrap(XPtr(ptr)->L()); END_RCPP; } SEXP merPredDPvec(SEXP ptr) { BEGIN_RCPP; return wrap(XPtr(ptr)->Pvec()); END_RCPP; } SEXP merPredDRX(SEXP ptr) { BEGIN_RCPP; return wrap(XPtr(ptr)->RX()); END_RCPP; } SEXP merPredDRXi(SEXP ptr) { BEGIN_RCPP; return wrap(XPtr(ptr)->RXi()); END_RCPP; } SEXP merPredDRXdiag(SEXP ptr) { BEGIN_RCPP; return wrap(XPtr(ptr)->RXdiag()); END_RCPP; } SEXP merPredDcondVar(SEXP ptr, SEXP rho) { BEGIN_RCPP; return wrap(XPtr(ptr)->condVar(Rcpp::Environment(rho))); END_RCPP; } SEXP merPredDldL2(SEXP ptr) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr)->ldL2()); END_RCPP; } SEXP merPredDldRX2(SEXP ptr) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr)->ldRX2()); END_RCPP; } SEXP merPredDunsc(SEXP ptr) { BEGIN_RCPP; return wrap(XPtr(ptr)->unsc()); END_RCPP; } // methods SEXP merPredDb(SEXP ptr, SEXP fac) { BEGIN_RCPP; return wrap(XPtr(ptr)->b(::Rf_asReal(fac))); END_RCPP; } SEXP merPredDbeta(SEXP ptr, SEXP fac) { BEGIN_RCPP; return wrap(XPtr(ptr)->beta(::Rf_asReal(fac))); END_RCPP; } SEXP merPredDinstallPars(SEXP ptr, SEXP fac) { BEGIN_RCPP; XPtr(ptr)->installPars(::Rf_asReal(fac)); END_RCPP; } SEXP merPredDlinPred(SEXP ptr, SEXP fac) { BEGIN_RCPP; return wrap(XPtr(ptr)->linPred(::Rf_asReal(fac))); END_RCPP; } SEXP merPredDsolve(SEXP ptr) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr)->solve()); END_RCPP; } SEXP merPredDsolveU(SEXP ptr) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr)->solveU()); END_RCPP; } SEXP merPredDsqrL(SEXP ptr, SEXP fac) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr)->sqrL(::Rf_asReal(fac))); END_RCPP; } SEXP merPredDu(SEXP ptr, SEXP fac) { BEGIN_RCPP; return wrap(XPtr(ptr)->u(::Rf_asReal(fac))); END_RCPP; } SEXP merPredDupdateDecomp(SEXP ptr, SEXP xPenalty_) { BEGIN_RCPP; if (Rf_isNull(xPenalty_)) XPtr(ptr)->updateDecomp(NULL); else { const Mat & xPenalty(as(xPenalty_)); XPtr(ptr)->updateDecomp(&xPenalty); } END_RCPP; } SEXP merPredDupdateL(SEXP ptr) { BEGIN_RCPP; XPtr(ptr)->updateL(); END_RCPP; } SEXP merPredDupdateLamtUt(SEXP ptr) { BEGIN_RCPP; XPtr(ptr)->updateLamtUt(); END_RCPP; } SEXP merPredDupdateRes(SEXP ptr, SEXP wtres) { BEGIN_RCPP; XPtr(ptr)->updateRes(as(wtres)); END_RCPP; } SEXP merPredDupdateXwts(SEXP ptr, SEXP wts) { BEGIN_RCPP; XPtr(ptr)->updateXwts(as(wts)); END_RCPP; } SEXP NelderMead_Create(SEXP lb_, SEXP ub_, SEXP xstep0_, SEXP x_, SEXP xtol_) { BEGIN_RCPP; MVec lb(as(lb_)), ub(as(ub_)), xstep0(as(xstep0_)), x(as(x_)), xtol(as(xtol_)); Nelder_Mead *ans = new Nelder_Mead(lb, ub, xstep0, x, optimizer::nl_stop(as(xtol_))); return wrap(XPtr(ans, true)); END_RCPP; } SEXP NelderMead_newf(SEXP ptr_, SEXP f_) { BEGIN_RCPP; switch (XPtr(ptr_)->newf(::Rf_asReal(f_))) { case optimizer::nm_evals: return ::Rf_ScalarInteger(-4); case optimizer::nm_forced: return ::Rf_ScalarInteger(-3); case optimizer::nm_nofeasible: return ::Rf_ScalarInteger(-2); case optimizer::nm_x0notfeasible: return ::Rf_ScalarInteger(-1); case optimizer::nm_active: return ::Rf_ScalarInteger(0); case optimizer::nm_minf_max: return ::Rf_ScalarInteger(1); case optimizer::nm_fcvg: return ::Rf_ScalarInteger(2); case optimizer::nm_xcvg: return ::Rf_ScalarInteger(3); } END_RCPP; } SEXP NelderMead_setForce_stop(SEXP ptr_, SEXP stp_) { BEGIN_RCPP; XPtr(ptr_)->setForce_stop(::Rf_asLogical(stp_)); END_RCPP; } SEXP NelderMead_setFtol_abs(SEXP ptr_, SEXP fta_) { BEGIN_RCPP; XPtr(ptr_)->setFtol_rel(::Rf_asReal(fta_)); END_RCPP; } SEXP NelderMead_setFtol_rel(SEXP ptr_, SEXP ftr_) { BEGIN_RCPP; XPtr(ptr_)->setFtol_rel(::Rf_asReal(ftr_)); END_RCPP; } SEXP NelderMead_setIprint(SEXP ptr_, SEXP ip_) { BEGIN_RCPP; XPtr(ptr_)->set_Iprint(::Rf_asInteger(ip_)); END_RCPP; } SEXP NelderMead_setMaxeval(SEXP ptr_, SEXP mm_) { BEGIN_RCPP; XPtr(ptr_)->set_Maxeval(::Rf_asInteger(mm_)); END_RCPP; } SEXP NelderMead_setMinf_max(SEXP ptr_, SEXP mm_) { BEGIN_RCPP; XPtr(ptr_)->setMinf_max(::Rf_asReal(mm_)); END_RCPP; } SEXP NelderMead_xeval(SEXP ptr_) { BEGIN_RCPP; return wrap(XPtr(ptr_)->xeval()); END_RCPP; } SEXP NelderMead_value(SEXP ptr_) { BEGIN_RCPP; return wrap(XPtr(ptr_)->value()); END_RCPP; } SEXP NelderMead_xpos(SEXP ptr_) { BEGIN_RCPP; return wrap(XPtr(ptr_)->xpos()); END_RCPP; } // return the number of function evaluations performed SEXP NelderMead_evals(SEXP ptr_) { BEGIN_RCPP; return wrap(int(XPtr(ptr_)->evals())); END_RCPP; } // nonlinear model response (also the base class for other response classes) SEXP nls_Create(SEXP y, SEXP weights, SEXP offset, SEXP mu, SEXP sqrtXwt, SEXP sqrtrwt, SEXP wtres, SEXP gamma, SEXP mod, SEXP env, SEXP pnms) { BEGIN_RCPP; nlsResp *ans = new nlsResp(y, weights, offset, mu, sqrtXwt, sqrtrwt, wtres, gamma, mod, env, pnms); return wrap(XPtr(ans, true)); END_RCPP; } SEXP nls_Laplace(SEXP ptr_, SEXP ldL2, SEXP ldRX2, SEXP sqrL) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr_)-> Laplace(::Rf_asReal(ldL2), ::Rf_asReal(ldRX2), ::Rf_asReal(sqrL))); END_RCPP; } SEXP nls_updateMu(SEXP ptr_, SEXP gamma) { BEGIN_RCPP; return ::Rf_ScalarReal(XPtr(ptr_)->updateMu(as(gamma))); END_RCPP; } SEXP showlocation(SEXP obj) { int ll = Rf_length(obj); if (Rf_isReal(obj)) { double *vv = REAL(obj); Rcpp::Rcout << "Numeric vector of length " << ll << " at location: " << vv << std::endl; if (ll > 0) { Rcpp::Rcout << "Values: " << vv[0]; for(int i = 1; i < std::min(ll, 5); ++i) Rcpp::Rcout << "," << vv[i]; if (ll > 8) Rcpp::Rcout << ",...,"; for (int i = std::max(5, ll - 3); i < ll; ++i) Rcpp::Rcout << "," << vv[i]; Rcpp::Rcout << std::endl; } } if (Rf_isInteger(obj)) { int *vv = INTEGER(obj); Rcpp::Rcout << "Numeric vector of length " << ll << " at location: " << vv << std::endl; if (ll > 0) { Rcpp::Rcout << "Values: " << vv[0]; for(int i = 1; i < std::min(ll, 5); ++i) Rcpp::Rcout << "," << vv[i]; if (ll > 8) Rcpp::Rcout << ",...,"; for (int i = std::max(5,ll - 3); i < ll; ++i) Rcpp::Rcout << "," << vv[i]; Rcpp::Rcout << std::endl; } } return R_NilValue; } } #include #define CALLDEF(name, n) {#name, (DL_FUNC) &name, n} static R_CallMethodDef CallEntries[] = { CALLDEF(Eigen_SSE, 0), CALLDEF(allPerm_int, 1), CALLDEF(glm_Create, 10), // generate external pointer CALLDEF(glm_setN, 2), // setters CALLDEF(glm_aic, 1), // getters CALLDEF(glm_devResid, 1), CALLDEF(glm_family, 1), CALLDEF(glm_link, 1), CALLDEF(glm_muEta, 1), CALLDEF(glm_resDev, 1), CALLDEF(glm_setTheta, 2), CALLDEF(glm_sqrtWrkWt, 1), CALLDEF(glm_theta, 1), CALLDEF(glm_variance, 1), CALLDEF(glm_wtWrkResp, 1), CALLDEF(glm_wrkResids, 1), CALLDEF(glm_wrkResp, 1), CALLDEF(glm_Laplace, 4), // methods CALLDEF(glm_updateMu, 2), CALLDEF(glm_updateWts, 1), CALLDEF(glmFamily_Create, 1), // generate external pointer CALLDEF(glmFamily_aic, 6), // methods CALLDEF(glmFamily_link, 2), CALLDEF(glmFamily_linkInv, 2), CALLDEF(glmFamily_devResid, 4), CALLDEF(glmFamily_muEta, 2), CALLDEF(glmFamily_setTheta, 2), CALLDEF(glmFamily_theta, 1), CALLDEF(glmFamily_variance, 2), CALLDEF(glmerAGQ, 6), CALLDEF(glmerLaplace, 5), CALLDEF(golden_Create, 2), CALLDEF(golden_newf, 2), CALLDEF(golden_value, 1), CALLDEF(golden_xeval, 1), CALLDEF(golden_xpos, 1), CALLDEF(isNullExtPtr, 1), CALLDEF(lm_Create, 7), // generate external pointer CALLDEF(lm_setOffset, 2), // setters CALLDEF(lm_setResp, 2), CALLDEF(lm_setWeights, 2), CALLDEF(lm_wrss, 1), // getter CALLDEF(lm_updateMu, 2), // method CALLDEF(lmer_Create, 7), // generate external pointer CALLDEF(lmer_setREML, 2), // setter CALLDEF(lmer_Deviance, 3), // methods CALLDEF(lmer_Laplace, 5), CALLDEF(lmer_opt1, 4), CALLDEF(merPredDCreate, 17), // generate external pointer CALLDEF(merPredDsetTheta, 2), // setters CALLDEF(merPredDsetBeta0, 2), CALLDEF(merPredDsetDelu, 2), // setters CALLDEF(merPredDsetDelb, 2), CALLDEF(merPredDCcNumer, 1), // getters CALLDEF(merPredDL, 1), CALLDEF(merPredDPvec, 1), CALLDEF(merPredDRX, 1), CALLDEF(merPredDRXdiag, 1), CALLDEF(merPredDRXi, 1), CALLDEF(merPredDldL2, 1), CALLDEF(merPredDldRX2, 1), CALLDEF(merPredDunsc, 1), CALLDEF(merPredDb, 2), // methods CALLDEF(merPredDbeta, 2), CALLDEF(merPredDcondVar, 2), CALLDEF(merPredDlinPred, 2), CALLDEF(merPredDinstallPars,2), CALLDEF(merPredDsolve, 1), CALLDEF(merPredDsolveU, 1), CALLDEF(merPredDsqrL, 2), CALLDEF(merPredDu, 2), CALLDEF(merPredDupdateDecomp,2), CALLDEF(merPredDupdateL, 1), CALLDEF(merPredDupdateLamtUt,1), CALLDEF(merPredDupdateRes, 2), CALLDEF(merPredDupdateXwts, 2), CALLDEF(NelderMead_Create, 5), CALLDEF(NelderMead_newf, 2), CALLDEF(NelderMead_setForce_stop, 2), CALLDEF(NelderMead_setFtol_abs, 2), CALLDEF(NelderMead_setFtol_rel, 2), CALLDEF(NelderMead_setIprint, 2), CALLDEF(NelderMead_setMaxeval, 2), CALLDEF(NelderMead_setMinf_max, 2), CALLDEF(NelderMead_value, 1), CALLDEF(NelderMead_xeval, 1), CALLDEF(NelderMead_xpos, 1), CALLDEF(nlmerLaplace, 8), CALLDEF(nls_Create, 11), // generate external pointer CALLDEF(nls_Laplace, 4), // methods CALLDEF(nls_updateMu, 2), CALLDEF(showlocation, 1), {NULL, NULL, 0} }; /** Initializer for lme4, called upon loading the package. * * Register routines that can be called directly from R. * Initialize CHOLMOD and require the LL' form of the factorization. * Install the symbols to be used by functions in the package. */ extern "C" void R_init_lme4(DllInfo *dll) { R_registerRoutines(dll, NULL, CallEntries, NULL, NULL); R_useDynamicSymbols(dll, (Rboolean)FALSE); } lme4/src/predModule.cpp0000644000176000001440000003344312273467214014555 0ustar ripleyusers// // predModule.cpp: implementation of predictor module using Eigen // // Copyright (C) 2011-2013 Douglas Bates, Martin Maechler, Ben Bolker and Steve Walker // // This file is part of lme4. #include "predModule.h" namespace lme4 { using Rcpp::as; using std::invalid_argument; using std::runtime_error; using Eigen::ArrayXd; typedef Eigen::Map MMat; typedef Eigen::Map MVec; typedef Eigen::Map MiVec; merPredD::merPredD(SEXP X, SEXP Lambdat, SEXP LamtUt, SEXP Lind, SEXP RZX, SEXP Ut, SEXP Utr, SEXP V, SEXP VtV, SEXP Vtr, SEXP Xwts, SEXP Zt, SEXP beta0, SEXP delb, SEXP delu, SEXP theta, SEXP u0) : d_X( as(X)), d_RZX( as(RZX)), d_V( as(V)), d_VtV( as(VtV)), d_Zt( as(Zt)), d_Ut( as(Ut)), d_LamtUt( as(LamtUt)), d_Lambdat( as(Lambdat)), d_theta( as(theta)), d_Vtr( as(Vtr)), d_Utr( as(Utr)), d_Xwts( as(Xwts)), d_beta0( as(beta0)), d_delb( as(delb)), d_delu( as(delu)), d_u0( as(u0)), d_Lind( as(Lind)), d_N( d_X.rows()), d_p( d_X.cols()), d_q( d_Zt.rows()), d_RX( d_p) { // Check consistency of dimensions if (d_N != d_Zt.cols()) throw invalid_argument("Z dimension mismatch"); if (d_Lind.size() != d_Lambdat.nonZeros()) throw invalid_argument("size of Lind does not match nonzeros in Lambda"); // checking of the range of Lind is now done in R code for reference class // initialize beta0, u0, delb, delu and VtV d_VtV.setZero().selfadjointView().rankUpdate(d_V.adjoint()); d_RX.compute(d_VtV); // ensure d_RX is initialized even in the 0-column X case setTheta(d_theta); // starting values into Lambda d_L.cholmod().final_ll = 1; // force an LL' decomposition updateLamtUt(); d_L.analyzePattern(d_LamtUt); // perform symbolic analysis if (d_L.info() != Eigen::Success) throw runtime_error("CholeskyDecomposition.analyzePattern failed"); } void merPredD::updateLamtUt() { // This complicated code bypasses problems caused by Eigen's // sparse/sparse matrix multiplication pruning zeros. The // Cholesky decomposition croaks if the structure of d_LamtUt changes. MVec(d_LamtUt.valuePtr(), d_LamtUt.nonZeros()).setZero(); for (Index j = 0; j < d_Ut.outerSize(); ++j) { for(MSpMatrixd::InnerIterator rhsIt(d_Ut, j); rhsIt; ++rhsIt) { Scalar y(rhsIt.value()); Index k(rhsIt.index()); MSpMatrixd::InnerIterator prdIt(d_LamtUt, j); for (MSpMatrixd::InnerIterator lhsIt(d_Lambdat, k); lhsIt; ++lhsIt) { Index i = lhsIt.index(); while (prdIt && prdIt.index() != i) ++prdIt; if (!prdIt) throw runtime_error("logic error in updateLamtUt"); prdIt.valueRef() += lhsIt.value() * y; } } } } VectorXd merPredD::b(const double& f) const {return d_Lambdat.adjoint() * u(f);} VectorXd merPredD::beta(const double& f) const {return d_beta0 + f * d_delb;} VectorXd merPredD::linPred(const double& f) const { //Rcpp::Rcout << "\nmin u:\n" << u(f).minCoeff() << std::endl; //Rcpp::Rcout << "\nmax u:\n" << u(f).maxCoeff() << std::endl; return d_X * beta(f) + d_Zt.adjoint() * b(f); } Rcpp::List merPredD::condVar(const Rcpp::Environment& rho) const { const Rcpp::List ll(as(rho["flist"])), trmlst(as(rho["terms"])); const int nf(ll.size()); const MiVec nl(as(rho["nlevs"])), nct(as(rho["nctot"])), off(as(rho["offsets"])); Rcpp::List ans(nf); ans.names() = clone(as(ll.names())); const SpMatrixd d_Lambda(d_Lambdat.adjoint()); for (int i = 0; i < nf; i++) { int ncti(nct[i]), nli(nl[i]); Rcpp::NumericVector ansi(ncti * ncti * nli); ansi.attr("dim") = Rcpp::IntegerVector::create(ncti, ncti, nli); ans[i] = ansi; const MiVec trms(as(trmlst(i))); if (trms.size() == 1) { // simple case int offset = off[trms[0] - 1]; for (int j = 0; j < nli; ++j) { MatrixXd Lv(d_Lambda.innerVectors(offset + j * ncti, ncti)); d_L.solveInPlace(Lv, CHOLMOD_A); MatrixXd rr(MatrixXd(ncti, ncti).setZero(). selfadjointView().rankUpdate(Lv.adjoint())); std::copy(rr.data(), rr.data() + rr.size(), &ansi[j * ncti * ncti]); } } else { throw std::runtime_error("multiple terms per factor not yet written"); } } return ans; } VectorXd merPredD::u(const double& f) const {return d_u0 + f * d_delu;} merPredD::Scalar merPredD::sqrL(const double& f) const {return u(f).squaredNorm();} void merPredD::updateL() { updateLamtUt(); // More complicated code to handle the case of zeros in // potentially nonzero positions. The factorize_p method is // for a SparseMatrix, not a MappedSparseMatrix. SpMatrixd m(d_LamtUt.rows(), d_LamtUt.cols()); m.resizeNonZeros(d_LamtUt.nonZeros()); std::copy(d_LamtUt.valuePtr(), d_LamtUt.valuePtr() + d_LamtUt.nonZeros(), m.valuePtr()); std::copy(d_LamtUt.innerIndexPtr(), d_LamtUt.innerIndexPtr() + d_LamtUt.nonZeros(), m.innerIndexPtr()); std::copy(d_LamtUt.outerIndexPtr(), d_LamtUt.outerIndexPtr() + d_LamtUt.cols() + 1, m.outerIndexPtr()); d_L.factorize_p(m, Eigen::ArrayXi(), 1.); d_ldL2 = ::M_chm_factor_ldetL2(d_L.factor()); } void merPredD::setTheta(const VectorXd& theta) { if (theta.size() != d_theta.size()) throw invalid_argument("theta size mismatch"); // update theta std::copy(theta.data(), theta.data() + theta.size(), d_theta.data()); // update Lambdat int *lipt = d_Lind.data(); double *LamX = d_Lambdat.valuePtr(), *thpt = d_theta.data(); for (int i = 0; i < d_Lind.size(); ++i) { LamX[i] = thpt[lipt[i] - 1]; } } merPredD::Scalar merPredD::solve() { d_delu = d_Utr - d_u0; d_L.solveInPlace(d_delu, CHOLMOD_P); d_L.solveInPlace(d_delu, CHOLMOD_L); // d_delu now contains cu d_CcNumer = d_delu.squaredNorm(); // numerator of convergence criterion d_delb = d_RX.matrixL().solve(d_Vtr - d_RZX.adjoint() * d_delu); d_CcNumer += d_delb.squaredNorm(); // increment CcNumer d_RX.matrixU().solveInPlace(d_delb); d_delu -= d_RZX * d_delb; d_L.solveInPlace(d_delu, CHOLMOD_Lt); d_L.solveInPlace(d_delu, CHOLMOD_Pt); return d_CcNumer; } merPredD::Scalar merPredD::solveU() { //Rcpp::Rcout << "\nd_u0:\n" << d_u0 << std::endl; d_delb.setZero(); // in calculation of linPred delb should be zero after solveU d_delu = d_Utr - d_u0; //d_delu = d_Utr - d_delu; // experiment!! SCW //Rcpp::Rcout << "\nd_delu before:\n" << d_delu << std::endl; //Rcpp::Rcout << "\nd_LamtUt:\n" << d_LamtUt << std::endl; //Rcpp::Rcout << "\nd_Utr:\n" << d_Utr << std::endl; d_L.solveInPlace(d_delu, CHOLMOD_P); d_L.solveInPlace(d_delu, CHOLMOD_L); // d_delu now contains cu d_CcNumer = d_delu.squaredNorm(); // numerator of convergence criterion d_L.solveInPlace(d_delu, CHOLMOD_Lt); d_L.solveInPlace(d_delu, CHOLMOD_Pt); //Rcpp::Rcout << "\nd_delu after:\n" << d_delu << std::endl; return d_CcNumer; } void merPredD::updateXwts(const ArrayXd& sqrtXwt) { if (d_Xwts.size() != sqrtXwt.size()) throw invalid_argument("updateXwts: dimension mismatch"); std::copy(sqrtXwt.data(), sqrtXwt.data() + sqrtXwt.size(), d_Xwts.data()); if (sqrtXwt.size() == d_V.rows()) { // W is diagonal d_V = d_Xwts.asDiagonal() * d_X; for (int j = 0; j < d_N; ++j) for (MSpMatrixd::InnerIterator Utj(d_Ut, j), Ztj(d_Zt, j); Utj && Ztj; ++Utj, ++Ztj) Utj.valueRef() = Ztj.value() * d_Xwts.data()[j]; } else { SpMatrixd W(d_V.rows(), sqrtXwt.size()); const double *pt = sqrtXwt.data(); W.reserve(sqrtXwt.size()); for (Index j = 0; j < W.cols(); ++j, ++pt) { W.startVec(j); W.insertBack(j % d_V.rows(), j) = *pt; } W.finalize(); d_V = W * d_X; SpMatrixd Ut(d_Zt * W.adjoint()); if (Ut.cols() != d_Ut.cols()) throw std::runtime_error("Size mismatch in updateXwts"); // More complex code to handle the pruning of zeros MVec(d_Ut.valuePtr(), d_Ut.nonZeros()).setZero(); for (int j = 0; j < d_Ut.outerSize(); ++j) { MSpMatrixd::InnerIterator lhsIt(d_Ut, j); for (SpMatrixd::InnerIterator rhsIt(Ut, j); rhsIt; ++rhsIt, ++lhsIt) { Index k(rhsIt.index()); while (lhsIt && lhsIt.index() != k) ++lhsIt; if (lhsIt.index() != k) throw std::runtime_error("Pattern mismatch in updateXwts"); lhsIt.valueRef() = rhsIt.value(); } } } d_VtV.setZero().selfadjointView().rankUpdate(d_V.adjoint()); updateL(); } void merPredD::updateDecomp() { updateDecomp(NULL); } // using a point so as to detect NULL void merPredD::updateDecomp(const MatrixXd* xPenalty) { // update L, RZX and RX updateL(); d_RZX = d_LamtUt * d_V; if (d_p > 0) { d_L.solveInPlace(d_RZX, CHOLMOD_P); d_L.solveInPlace(d_RZX, CHOLMOD_L); MatrixXd VtVdown(d_VtV); if (xPenalty == NULL) d_RX.compute(VtVdown.selfadjointView().rankUpdate(d_RZX.adjoint(), -1)); else { d_RX.compute(VtVdown.selfadjointView().rankUpdate(d_RZX.adjoint(), -1).rankUpdate(*xPenalty, 1)); } if (d_RX.info() != Eigen::Success) ::Rf_error("Downdated VtV is not positive definite"); d_ldRX2 = 2. * d_RX.matrixLLT().diagonal().array().abs().log().sum(); } } void merPredD::updateRes(const VectorXd& wtres) { if (d_V.rows() != wtres.size()) throw invalid_argument("updateRes: dimension mismatch"); d_Vtr = d_V.adjoint() * wtres; d_Utr = d_LamtUt * wtres; } void merPredD::installPars(const Scalar& f) { d_u0 = u(f); d_beta0 = beta(f); d_delb.setZero(); d_delu.setZero(); } void merPredD::setBeta0(const VectorXd& nBeta) { if (nBeta.size() != d_p) throw invalid_argument("setBeta0: dimension mismatch"); std::copy(nBeta.data(), nBeta.data() + d_p, d_beta0.data()); } void merPredD::setDelb(const VectorXd& newDelb) { if (newDelb.size() != d_p) throw invalid_argument("setDelb: dimension mismatch"); std::copy(newDelb.data(), newDelb.data() + d_p, d_delb.data()); } void merPredD::setDelu(const VectorXd& newDelu) { if (newDelu.size() != d_q) throw invalid_argument("setDelu: dimension mismatch"); std::copy(newDelu.data(), newDelu.data() + d_q, d_delu.data()); } void merPredD::setU0(const VectorXd& newU0) { if (newU0.size() != d_q) throw invalid_argument("setU0: dimension mismatch"); std::copy(newU0.data(), newU0.data() + d_q, d_u0.data()); } template struct Norm_Rand : std::unary_function { const T operator()(const T& x) const {return ::norm_rand();} }; inline static VectorXd Random_Normal(int size, double sigma) { return ArrayXd(size).unaryExpr(Norm_Rand()) * sigma; } void merPredD::MCMC_beta_u(const Scalar& sigma) { VectorXd del2(d_RX.matrixU().solve(Random_Normal(d_p, sigma))); d_delb += del2; VectorXd del1(Random_Normal(d_q, sigma) - d_RZX * del2); d_L.solveInPlace(del1, CHOLMOD_Lt); d_delu += del1; } VectorXi merPredD::Pvec() const { int* ppt((int*)d_L.factor()->Perm); VectorXi ans(d_q); std::copy(ppt, ppt + d_q, ans.data()); return ans; } MatrixXd merPredD::RX() const { return d_RX.matrixU(); } MatrixXd merPredD::RXi() const { return d_RX.matrixU().solve(MatrixXd::Identity(d_p,d_p)); } MatrixXd merPredD::unsc() const { return MatrixXd(MatrixXd(d_p, d_p).setZero(). selfadjointView(). rankUpdate(RXi())); } VectorXd merPredD::RXdiag() const { return d_RX.matrixLLT().diagonal(); } } lme4/src/respModule.h0000644000176000001440000001072012273467214014232 0ustar ripleyusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; tab-width: 8 -*- // // respModule.h: response modules using Eigen // // Copyright (C) 2011-2012 Douglas Bates, Martin Maechler and Ben Bolker // // This file is part of lme4. #ifndef LME4_RESPMODULE_H #define LME4_RESPMODULE_H #include "glmFamily.h" namespace lme4 { typedef Eigen::Map MVec; using Rcpp::CharacterVector; using Rcpp::Environment; using Rcpp::Language; using Rcpp::NumericVector; using glm::glmFamily; class lmResp { protected: double d_wrss; /**< current weighted sum of squared residuals */ MVec d_y, /**< response vector */ d_weights, /**< prior weights - always present even if unity */ d_offset, /**< offset in the model */ d_mu, /**< mean response from current linear predictor */ d_sqrtXwt, /**< Square roots of the "X weights". For * lmResp and lmerResp these are the same as * the sqrtrwt. For glmResp and nlsResp they * incorporate the gradient of the eta to mu * mapping.*/ d_sqrtrwt, /**< Square roots of the residual weights */ d_wtres; /**< Current weighted residuals */ public: lmResp(SEXP,SEXP,SEXP,SEXP,SEXP,SEXP,SEXP); const MVec& sqrtXwt() const {return d_sqrtXwt;} /**< return a const reference to d_sqrtXwt */ const MVec& mu() const {return d_mu;} /**< return a const reference to d_mu */ const MVec& offset() const {return d_offset;} /**< return a const reference to d_offset */ const MVec& sqrtrwt() const {return d_sqrtrwt;} /**< return a const reference to d_sqrtrwt */ const MVec& weights() const {return d_weights;} /**< return a const reference to d_weights */ const MVec& wtres() const {return d_wtres;} /**< return a const reference to d_wtres */ const MVec& y() const {return d_y;} /**< return a const reference to d_y */ double wrss() const {return d_wrss;} /**< return the weighted sum of squared residuals */ double updateMu(const Eigen::VectorXd&); double updateWts() {return updateWrss();} /**< update the weights. For a * glmResp this done separately from * updating the mean, because of the * iterative reweighting. */ double updateWrss(); /**< update the weighted residuals and d_wrss */ void setOffset(const Eigen::VectorXd&); /**< set a new value of the offset */ void setResp(const Eigen::VectorXd&); /**< set a new value of the response, y */ void setWeights(const Eigen::VectorXd&); /**< set a new value of the prior weights */ }; class lmerResp : public lmResp { private: int d_reml; /**< 0 for evaluating the deviance, p * for evaluating the REML criterion. */ public: lmerResp(SEXP,SEXP,SEXP,SEXP,SEXP,SEXP,SEXP); double Laplace(double,double,double) const; double Laplace(double,double,double,double) const; int REML() const {return d_reml;} void setReml(int); }; class glmResp : public lmResp { protected: glmFamily d_fam; MVec d_eta, d_n; public: glmResp(Rcpp::List,SEXP,SEXP,SEXP,SEXP,SEXP,SEXP,SEXP,SEXP,SEXP); Eigen::ArrayXd devResid() const; Eigen::ArrayXd muEta() const; Eigen::ArrayXd sqrtWrkWt() const; Eigen::ArrayXd variance() const; Eigen::ArrayXd wrkResids() const; Eigen::ArrayXd wrkResp() const; Eigen::ArrayXd wtWrkResp() const; const MVec& eta() const {return d_eta;} const MVec& n() const {return d_n;} const std::string& family() const {return d_fam.fam();} const std::string& link() const {return d_fam.lnk();} double aic() const; double Laplace(double,double,double) const; double resDev() const; double theta() const {return d_fam.theta();} //< negative binomial distribution only double updateMu(const Eigen::VectorXd&); double updateWts(); void setN(const Eigen::VectorXd&); void setTheta(const double& ntheta) {d_fam.setTheta(ntheta);} // negative binomial distribution only }; class nlsResp : public lmResp { protected: MVec d_gamma; Environment d_nlenv; Language d_nlmod; CharacterVector d_pnames; public: nlsResp(SEXP,SEXP,SEXP,SEXP,SEXP,SEXP,SEXP,SEXP,SEXP,SEXP,SEXP); double Laplace(double, double, double) const; double updateMu(const Eigen::VectorXd&); }; } #endif lme4/src/optimizer.h0000644000176000001440000001272012273467214014137 0ustar ripleyusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; tab-width: 8 -*- // // Nelder_Mead.h: NLopt's Nelder-Mead optimizer, modified to use Eigen // // Copyright (C) 2011 Douglas Bates, Martin Maechler and Ben Bolker // // This file is part of lme4. #ifndef LME4_NELDER_MEAD_H #define LME4_NELDER_MEAD_H #include namespace optimizer { using Eigen::MatrixXd; using Eigen::VectorXd; using Eigen::VectorXi; typedef VectorXd::Scalar Scalar; typedef VectorXd::Index Index; class nl_stop { private: // utilities bool relstop(const Scalar& vold, const Scalar& vnew, const Scalar& reltol, const Scalar& abstol) const; Scalar sc(const Scalar& x, const Scalar& smin, const Scalar& smax) const { return smin + x * (smax - smin); } protected: const VectorXd xtol_abs; unsigned n, nevals, maxeval; Scalar minf_max, ftol_rel, ftol_abs, xtol_rel; bool force_stop; public: nl_stop(const VectorXd&); // constructor void incrEvals() {nevals++;} void resetEvals() {nevals = 0;} // setters void setFtol_rel(const Scalar& ftr) {ftol_rel = ftr;} void setFtol_abs(const Scalar& fta) {ftol_abs = fta;} void setForce_stop(const bool& stp) {force_stop = stp;} void setMinf_max(const Scalar& mm) {minf_max = mm;} void set_Maxeval(const unsigned int& mm) {maxeval = mm;} int get_Maxeval() const {return maxeval;} bool f(const Scalar& f, const Scalar& oldf) const { // convergence checking return (f <= minf_max || ftol(f, oldf)); } bool ftol(const Scalar& f, const Scalar& oldf) const { return relstop(oldf, f, ftol_rel, ftol_abs); } bool x(const VectorXd& x, const VectorXd& oldx) const; bool dx(const VectorXd& x, const VectorXd& dx) const; bool xs(const VectorXd& xs, const VectorXd& oldxs, const VectorXd& scale_min, const VectorXd& scale_max) const; bool evals() const {return maxeval > 0 && nevals > maxeval;} bool forced() const {return force_stop;} int ev() const {return nevals;} Scalar minfMax() const {return minf_max;} }; inline bool nl_stop::relstop(const Scalar& vold, const Scalar& vnew, const Scalar& reltol, const Scalar& abstol) const { if (std::abs(vold) == std::numeric_limits::infinity()) return false; return std::abs(vnew - vold) < abstol || std::abs(vnew - vold) < reltol * (std::abs(vnew) + std::abs(vold)) * 0.5 || (reltol > 0 && vnew == vold); } enum nm_status {nm_active, nm_x0notfeasible, nm_nofeasible, nm_forced, nm_minf_max, nm_evals, nm_fcvg, nm_xcvg}; enum nm_stage {nm_restart, nm_postreflect, nm_postexpand, nm_postcontract}; /* heuristic "strategy" constants: */ static const double alpha = 1, beta = 0.5, gamm = 2, delta = 0.5; class Nelder_Mead { private: Scalar f_old; Index init_pos; nm_status init(const Scalar&); nm_status restart(const Scalar&); bool reflectpt(VectorXd&, const VectorXd&, const Scalar&, const VectorXd&); nm_status postreflect(const Scalar&); nm_status postexpand(const Scalar&); nm_status postcontract(const Scalar&); protected: const VectorXd d_lb; /*<< lower bounds */ const VectorXd d_ub; /*<< upper bounds */ const VectorXd d_xstep; /*<< initial step sizes */ VectorXd d_x; /*<< initial value and optimum */ Index d_ih; /**< index in d_vals of largest value */ Index d_il; /**< index in d_vals of smallest value */ Index d_n; /**< size of parameter vector */ MatrixXd d_pts; /*<< points */ VectorXd d_vals; /*<< function values */ VectorXd d_c; /*<< centroid */ VectorXd d_xcur; /*<< current x */ VectorXd d_xeval; /*<< x at which next evaluation is requested */ Scalar d_fl, d_fh, d_minf; nm_status d_stat; nm_stage d_stage; nl_stop d_stop; Index d_verb; /**< verbosity, if > 0 results are displayed every d_verb evaluations */ public: Nelder_Mead(const VectorXd&, const VectorXd&, const VectorXd&, const VectorXd&, const nl_stop&); const MatrixXd& pts() const {return d_pts;} const VectorXd& lb() const {return d_lb;} const VectorXd& ub() const {return d_ub;} const VectorXd& vals() const {return d_vals;} const VectorXd& xstep() const {return d_xstep;} const VectorXd& xeval() const {return d_xeval;} const VectorXd& xpos() const {return d_x;} Index ih() const {return d_ih;} Index il() const {return d_il;} Index evals() const {return d_stop.ev();} Scalar value() const {return d_minf;} nm_status newf(const Scalar&); void setForce_stop(const bool& stp) {d_stop.setForce_stop(stp);} void setFtol_abs(const Scalar& fta) {d_stop.setFtol_abs(fta);} void setFtol_rel(const Scalar& ftr) {d_stop.setFtol_rel(ftr);} void set_Maxeval(const unsigned int& mm) {d_stop.set_Maxeval(mm);} void set_Iprint(const int& ip) {d_verb = ip;} void setMinf_max(const Scalar& mm) {d_stop.setMinf_max(mm);} }; class Golden { protected: Scalar d_invratio, d_lower, d_upper; Eigen::Vector2d d_x, d_f; bool d_init, d_ll; public: Golden(const Scalar&, const Scalar&); void newf(const Scalar&); Scalar xeval() const {return d_x[d_ll ? 0 : 1];} Scalar value() const {return d_f[0];} Scalar xpos() const {return d_x[0];} }; } #endif // LME4_NELDER_MEAD_H lme4/src/glmFamily.cpp0000644000176000001440000004104512273467214014373 0ustar ripleyusers// // glmFamily.cpp: implementation of glmFamily and related classes using Eigen // // Copyright (C) 2011-2012 Douglas Bates, Martin Maechler and Ben Bolker // // This file is part of lme4. #include "glmFamily.h" #include #include #include using namespace Rcpp; namespace glm { /** Cumulative probability function of the complement of the Gumbel distribution * * (i.e. pgumbel(q,0.,1.,0) == 1 - pgumbel2(-q,0.,1.,0)) * * @param q the quantile at which to evaluate the cumulative probability * @param loc location parameter * @param scale scale parameter * @param lower_tail when zero evaluate the complement of the cdf * * @return Cumulative probability value or its complement, according to the value of lower_tail */ static inline double pgumbel2(const double& q, const double& loc, const double& scale, int lower_tail) { double qq = (q - loc) / scale; qq = -std::exp(qq); return lower_tail ? -expm1(qq) : std::exp(qq); } /** * density of the complement of the Gumbel distribution * * @param x numeric argument * @param loc location parameter * @param scale scale parameter * @param give_log should the logarithm of the density be returned * * @return density or its logarithm, according to the value of give_log */ static inline double dgumbel2(const double& x, const double& loc, const double& scale, int give_log) { double xx = (x - loc) / scale; xx = xx - std::exp(xx) - std::log(scale); return give_log ? xx : std::exp(xx); } //@{ Templated scalar functors used in links, inverse links, etc. template struct logN0 : public std::unary_function { const T operator()(const T& x) const {return x ? std::log(x) : T();} }; template struct safemult : public std::binary_function { const T operator()(const T& x, const T& y) const {return x ? (x*y) : T();} }; static inline ArrayXd Y_log_Y(const ArrayXd& y, const ArrayXd& mu) { // return y * (y/mu).unaryExpr(logN0()); return y * (y/mu).unaryExpr(logN0()); } static inline double Y_log_Y(const double y, const double mu) { double v=(y/mu); // BMB: could do this better if I understood templates return y * ( v ? std::log(v) : v ); } template struct Round : public std::unary_function { const T operator()(const T& x) const {return nearbyint(x);} }; template struct x1mx : public std::unary_function { const T operator() (const T& x) const { return T(std::max(std::numeric_limits::epsilon(), x * (1 - x))); } }; template struct Lgamma : public std::unary_function { const T operator() (const T& x) const { return lgamma(x); } }; template struct cauchitinv : public std::unary_function { const T operator() (const T& x) const { return T(std::min(1.-std::numeric_limits::epsilon(), ::Rf_pcauchy(double(x), 0., 1., 1, 0))); } }; template struct cauchit : public std::unary_function { const T operator() (const T& x) const { return T(::Rf_qcauchy(double(x), 0., 1., 1, 0)); } }; template struct cauchitmueta : public std::unary_function { const T operator() (const T& x) const { return T(::Rf_dcauchy(double(x), 0., 1., 0)); } }; // TODO: (re)consider clamping this (and the other inverse-link functions) // * warn on active clamp? // * clamp from both sides? // * intercept problems elsewhere? // * allow toggling of clamp activity by user? // (applies to logitmueta too) template struct logitinv : public std::unary_function { const T operator() (const T& x) const { return T(std::min(1.-std::numeric_limits::epsilon(), Rf_plogis(double(x), 0., 1., 1, 0))); } }; template struct logit : public std::unary_function { const T operator() (const T& x) const { return T(::Rf_qlogis(double(x), 0., 1., 1, 0)); } }; template struct logitmueta : public std::unary_function { const T operator() (const T& x) const { return T(std::max(std::numeric_limits::epsilon(), Rf_dlogis(double(x), 0., 1., 0))); } }; template struct probitinv : public std::unary_function { const T operator() (const T& x) const { return T(std::min(1.-std::numeric_limits::epsilon(), ::Rf_pnorm5(double(x), 0., 1., 1, 0))); } }; template struct probit : public std::unary_function { const T operator() (const T& x) const { return T(::Rf_qnorm5(double(x), 0., 1., 1, 0)); } }; template struct probitmueta : public std::unary_function { const T operator() (const T& x) const { return T(::Rf_dnorm4(double(x), 0., 1., 0)); } }; template struct clogloginv : public std::unary_function { const T operator() (const T& x) const { return T(std::min(1.-std::numeric_limits::epsilon(), pgumbel2(double(x), 0., 1., 1))); } }; template struct cloglogmueta : public std::unary_function { const T operator() (const T& x) const { return T(dgumbel2(double(x), 0., 1., 0)); } }; //@} template struct boundexp : public std::unary_function { const T operator() (const T& x) const { return T(std::max(std::numeric_limits::epsilon(), exp(double(x)))); } }; //@{ double binomialDist::aic (const ArrayXd& y, const ArrayXd& n, const ArrayXd& mu, const ArrayXd& wt, double dev) const { ArrayXd m((n > 1).any() ? n : wt); ArrayXd yy((m * y).unaryExpr(Round())); m = m.unaryExpr(Round()); double ans(0.); for (int i=0; i < mu.size(); ++i) ans += (m[i] <= 0. ? 0. : wt[i]/m[i]) * ::Rf_dbinom(yy[i], m[i], mu[i], true); return (-2. * ans); } const ArrayXd binomialDist::devResid(const ArrayXd& y, const ArrayXd& mu, const ArrayXd& wt) const { int debug=0; if (debug) { for (int i=0; i < mu.size(); ++i) { double r = 2. * wt[i] * (Y_log_Y(y[i], mu[i]) + Y_log_Y(1. - y[i], 1. - mu[i])); if (r!=r) { // attempt to detect `nan` (needs cross-platform testing, but should compile // everywhere whether or not it actually works) Rcpp::Rcout << "(bD) " << "nan @ pos " << i << ": y= " << y[i] << "; mu=" << mu[i] << "; wt=" << wt[i] << "; 1-y=" << 1. - y[i] << "; 1-mu=" << 1. - mu[i] << "; ylogy=" << Y_log_Y(y[i], mu[i]) << "; cylogy=" << Y_log_Y(1.-y[i], 1.-mu[i]) << std::endl; } } } return 2. * wt * (Y_log_Y(y, mu) + Y_log_Y(1. - y, 1. - mu)); } const ArrayXd binomialDist::variance(const ArrayXd& mu) const {return mu.unaryExpr(x1mx());} //@} //@{ double gammaDist::aic (const ArrayXd& y, const ArrayXd& n, const ArrayXd& mu, const ArrayXd& wt, double dev) const { double nn(wt.sum()); double disp(dev/nn); double ans(0), invdisp(1./disp); for (int i = 0; i < mu.size(); ++i) ans += wt[i] * ::Rf_dgamma(y[i], invdisp, mu[i] * disp, true); return -2. * ans + 2.; } const ArrayXd gammaDist::devResid(const ArrayXd& y, const ArrayXd& mu, const ArrayXd& wt) const { return -2. * wt * ((y/mu).unaryExpr(logN0()) - (y - mu)/mu); } const ArrayXd gammaDist::variance(const ArrayXd& mu) const {return mu.square();} //@} //@{ double GaussianDist::aic (const ArrayXd& y, const ArrayXd& n, const ArrayXd& mu, const ArrayXd& wt, double dev) const { double nn(mu.size()); return nn * (std::log(2. * M_PI * dev/nn) + 1.) + 2. - wt.log().sum(); } const ArrayXd GaussianDist::devResid(const ArrayXd& y, const ArrayXd& mu, const ArrayXd& wt) const { return wt * (y - mu).square(); } const ArrayXd GaussianDist::variance(const ArrayXd& mu) const {return ArrayXd::Ones(mu.size());} //@} //@{ double inverseGaussianDist::aic (const ArrayXd& y, const ArrayXd& n, const ArrayXd& mu, const ArrayXd& wt, double dev) const { double wtsum(wt.sum()); return wtsum * (std::log(dev/wtsum * 2. * M_PI) + 1.) + 3. * (y.log() * wt).sum() + 2.; } const ArrayXd inverseGaussianDist::devResid(const ArrayXd& y, const ArrayXd& mu, const ArrayXd& wt) const { return wt * ((y - mu).square())/(y * mu.square()); } const ArrayXd inverseGaussianDist::variance(const ArrayXd& mu) const {return mu.cube();} //@} //@{ double negativeBinomialDist::aic (const ArrayXd& y, const ArrayXd& n, const ArrayXd& mu, const ArrayXd& wt, double dev) const { return 2. * (wt * (y + d_theta) * (mu + d_theta).log() - y * mu.log() + (y + 1).unaryExpr(Lgamma()) - d_theta * std::log(d_theta) + lgamma(d_theta) - (d_theta + y).unaryExpr(Lgamma())).sum(); } const ArrayXd negativeBinomialDist::devResid(const ArrayXd &y, const ArrayXd &mu, const ArrayXd &wt) const { return 2. * wt * (Y_log_Y(y, mu) - (y + d_theta) * ((y + d_theta)/(mu + d_theta)).log()); } const ArrayXd negativeBinomialDist::variance(const ArrayXd &mu) const { return mu + mu.square()/d_theta; } //@} //@{ double PoissonDist::aic (const ArrayXd& y, const ArrayXd& n, const ArrayXd& mu, const ArrayXd& wt, double dev) const { double ans(0.); for (int i = 0; i < mu.size(); ++i) ans += ::Rf_dpois(y[i], mu[i], true) * wt[i]; return (-2. * ans); } const ArrayXd PoissonDist::devResid(const ArrayXd& y, const ArrayXd& mu, const ArrayXd& wt) const { return 2. * wt * (y * (y/mu).unaryExpr(logN0()) - (y - mu)); } const ArrayXd PoissonDist::variance(const ArrayXd& mu) const {return mu;} //@} //@{ const ArrayXd cauchitLink::linkFun(const ArrayXd& mu) const {return mu.unaryExpr(cauchit());} const ArrayXd cauchitLink::linkInv(const ArrayXd& eta) const {return eta.unaryExpr(cauchitinv());} const ArrayXd cauchitLink::muEta( const ArrayXd& eta) const {return eta.unaryExpr(cauchitmueta());} //@} //@{ const ArrayXd logLink::linkFun(const ArrayXd& mu) const {return mu.log();} const ArrayXd logLink::linkInv(const ArrayXd& eta) const {return eta.unaryExpr(boundexp());} const ArrayXd logLink::muEta( const ArrayXd& eta) const {return eta.unaryExpr(boundexp());} //@} //@{ const ArrayXd logitLink::linkFun(const ArrayXd& mu) const {return mu.unaryExpr(logit());} const ArrayXd logitLink::linkInv(const ArrayXd& eta) const {return eta.unaryExpr(logitinv());} const ArrayXd logitLink::muEta( const ArrayXd& eta) const {return eta.unaryExpr(logitmueta());} //@} //@{ const ArrayXd probitLink::linkFun(const ArrayXd& mu) const {return mu.unaryExpr(probit());} const ArrayXd probitLink::linkInv(const ArrayXd& eta) const {return eta.unaryExpr(probitinv());} const ArrayXd probitLink::muEta( const ArrayXd& eta) const {return eta.unaryExpr(probitmueta());} //@} //@{ const ArrayXd identityLink::linkFun(const ArrayXd& mu) const {return mu;} const ArrayXd identityLink::linkInv(const ArrayXd& eta) const {return eta;} const ArrayXd identityLink::muEta( const ArrayXd& eta) const {return ArrayXd::Ones(eta.size());} //@} //@{ const ArrayXd inverseLink::linkFun(const ArrayXd& mu) const {return mu.inverse();} const ArrayXd inverseLink::linkInv(const ArrayXd& eta) const {return eta.inverse();} const ArrayXd inverseLink::muEta( const ArrayXd& eta) const {return -(eta.inverse().square());} //@} //@{ // const ArrayXd cloglogLink::linkFun(const ArrayXd& mu) const {return mu.unaryExpr(cloglog());} const ArrayXd cloglogLink::linkInv(const ArrayXd& eta) const {return eta.unaryExpr(clogloginv());} const ArrayXd cloglogLink::muEta( const ArrayXd& eta) const {return eta.unaryExpr(cloglogmueta());} //@} glmDist::glmDist(Rcpp::List& ll) : d_devRes (as(ll["dev.resids"])), d_variance(as(ll["variance"])), d_aic( as(ll["aic"])), d_rho( d_aic.environment()) { } glmLink::glmLink(Rcpp::List& ll) : d_linkFun(as(ll["linkfun"])), d_linkInv(as(ll["linkinv"])), d_muEta( as(ll["mu.eta"])), d_rho( d_linkFun.environment()) { } glmFamily::glmFamily(Rcpp::List ll) : d_family( as(as(ll["family"]))), d_linknam(as(as(ll["link"]))), d_dist( new glmDist(ll)), d_link( new glmLink(ll)) { if (!ll.inherits("family")) throw std::runtime_error("glmFamily requires a list of (S3) class \"family\""); if (d_linknam == "cauchit") {delete d_link; d_link = new cauchitLink(ll);} if (d_linknam == "cloglog") {delete d_link; d_link = new cloglogLink(ll);} if (d_linknam == "identity") {delete d_link; d_link = new identityLink(ll);} if (d_linknam == "inverse") {delete d_link; d_link = new inverseLink(ll);} if (d_linknam == "log") {delete d_link; d_link = new logLink(ll);} if (d_linknam == "logit") {delete d_link; d_link = new logitLink(ll);} if (d_linknam == "probit") {delete d_link; d_link = new probitLink(ll);} if (d_family == "binomial") {delete d_dist; d_dist = new binomialDist(ll);} if (d_family == "Gamma") {delete d_dist; d_dist = new gammaDist(ll);} if (d_family == "gaussian") {delete d_dist; d_dist = new GaussianDist(ll);} if (d_family == "inverse.gaussian") {delete d_dist; d_dist = new inverseGaussianDist(ll);} if (d_family.substr(0, 18) == "Negative Binomial(") {delete d_dist; d_dist = new negativeBinomialDist(ll);} if (d_family == "poisson") {delete d_dist; d_dist = new PoissonDist(ll);} } glmFamily::~glmFamily() { delete d_dist; delete d_link; } const ArrayXd glmFamily::devResid(const ArrayXd& y, const ArrayXd& mu, const ArrayXd& wt) const { return d_dist->devResid(y, mu, wt); } double glmFamily::aic(const ArrayXd& y, const ArrayXd& n, const ArrayXd& mu, const ArrayXd& wt, double dev) const { return d_dist->aic(y, n, mu, wt, dev); } const ArrayXd glmLink::linkFun(const ArrayXd& mu) const { return as(::Rf_eval(::Rf_lang2(as(d_linkFun), as(Rcpp::NumericVector(mu.data(), mu.data() + mu.size())) ), d_rho)); } const ArrayXd glmLink::linkInv(const ArrayXd& eta) const { return as(::Rf_eval(::Rf_lang2(as(d_linkInv), as(Rcpp::NumericVector(eta.data(), eta.data() + eta.size())) ), d_rho)); } const ArrayXd glmLink::muEta(const ArrayXd &eta) const { return as(::Rf_eval(::Rf_lang2(as(d_muEta), as(Rcpp::NumericVector(eta.data(), eta.data() + eta.size())) ), d_rho)); } const ArrayXd glmDist::variance(const ArrayXd &mu) const { return as(::Rf_eval(::Rf_lang2(as(d_variance), as(Rcpp::NumericVector(mu.data(), mu.data() + mu.size())) ), d_rho)); } const ArrayXd glmDist::devResid(const ArrayXd &y, const ArrayXd &mu, const ArrayXd &wt) const { int n = mu.size(); return as(::Rf_eval(::Rf_lang4(as(d_devRes), as(NumericVector(y.data(), y.data() + n)), as(NumericVector(mu.data(), mu.data() + n)), as(NumericVector(wt.data(), wt.data() + n)) ), d_rho)); } double glmDist::aic(const ArrayXd& y, const ArrayXd& n, const ArrayXd& mu, const ArrayXd& wt, double dev) const { int nn = mu.size(); double ans = ::Rf_asReal(::Rf_eval(::Rf_lang6(as(d_aic), as(NumericVector(y.data(), y.data() + nn)), as(NumericVector(n.data(), n.data() + nn)), as(NumericVector(mu.data(), mu.data() + nn)), as(NumericVector(wt.data(), wt.data() + nn)), PROTECT(::Rf_ScalarReal(dev))), d_rho)); UNPROTECT(1); return ans; } negativeBinomialDist::negativeBinomialDist(Rcpp::List& ll) : glmDist(ll), d_theta(::Rf_asReal(as(d_rho[".Theta"]))) {} double glmDist::theta() const { throw std::invalid_argument("theta accessor applies only to negative binomial"); } void glmDist::setTheta(const double& theta) { throw std::invalid_argument("setTheta applies only to negative binomial"); } } lme4/src/Makevars.win0000644000176000001440000000100712156422373014225 0ustar ripleyusers## -*- mode: makefile; -*- ## This assumes that we can call Rscript to ask Rcpp about its locations ## Use the R_HOME indirection to support installations of multiple R version PKG_LIBS = $(shell "${R_HOME}/bin${R_ARCH_BIN}/Rscript.exe" -e "Rcpp:::LdFlags()") PKG_CPPFLAGS = -I. -DNDEBUG -DEIGEN_DONT_VECTORIZE ## For development define the package CPPFLAGS as #PKG_CPPFLAGS= -I. -DEIGEN_DONT_VECTORIZE ## to activate assertions in Eigen. For the purposes of R CMD check the ## assertions should be suppressed. lme4/src/mcmcsamp.h0000644000176000001440000000205112273467214013711 0ustar ripleyusers// -*- mode: C++; c-indent-level: 4; c-basic-offset: 4; tab-width: 8 -*- // // mcmcsamp.h: Markov-chain Monte Carlo sample class using Eigen // // Copyright (C) 2012 Douglas Bates, Martin Maechler and Ben Bolker // // This file is part of lme4. #ifndef LME4_MCMCSAMP_H #define LME4_MCMCSAMP_H #include "predModule.h" #include "respModule.h" namespace lme4 { class mcmcsamp { public: typedef Eigen::ArrayXd Ar1; typedef Eigen::Map MAr1; typedef Eigen::VectorXd Vec; typedef Eigen::Map MVec; typedef Eigen::ArrayXXd Ar2; typedef Eigen::Map MAr2; typedef Eigen::MatrixXd Mat; typedef Eigen::Map MMat; protected: // lme4::merPredD *d_pred; // lme4::lmResp *d_resp; MVec d_dev; MMat d_fixef; MVec d_sigma; MMat d_ranef; public: // all the work is done in the constructor mcmcsamp(lme4::merPredD *pred, lme4::lmResp *resp, SEXP dev, SEXP fixef, SEXP sigma, SEXP ranef); }; } #endif /* LME4_GLMFAMILY_H */ lme4/src/respModule.cpp0000644000176000001440000002133412273467214014570 0ustar ripleyusers// respModule.cpp: response modules using Eigen // // Copyright (C) 2011-2012 Douglas Bates, Martin Maechler and Ben Bolker // // This file is part of lme4. #include "respModule.h" #include namespace lme4 { using Eigen::ArrayXd; using Eigen::VectorXd; using Rcpp::List; using Rcpp::NumericMatrix; using Rcpp::as; using std::copy; using std::invalid_argument; typedef Eigen::Map MVec; lmResp::lmResp(SEXP y, SEXP weights, SEXP offset, SEXP mu, SEXP sqrtXwt, SEXP sqrtrwt, SEXP wtres) : d_y( as(y)), d_weights(as(weights)), d_offset( as(offset)), d_mu( as(mu)), d_sqrtXwt(as(sqrtXwt)), d_sqrtrwt(as(sqrtrwt)), d_wtres( as(wtres)) { updateWrss(); } /** * Update the (conditional) mean response and weighted residuals * * @param gamma New value of the linear predictor * @return updated sum of squared, weighted residuals */ double lmResp::updateMu(const VectorXd& gamma) { if (gamma.size() != d_offset.size()) throw invalid_argument("updateMu: Size mismatch"); d_mu = d_offset + gamma; return updateWrss(); } /** * Update the wtres vector and return its sum of squares * wtres <- sqrtrwt * (y - mu) * return(wrss <- sum(wtres^2)) * * @return Updated weighted residual sum of squares */ double lmResp::updateWrss() { // Rcpp::Rcout << "\nwrss 1:\n" << d_wrss << std::endl; // double testwrss(0); // manual computation // for (int p = 0; p < d_y.size(); ++p) { // testwrss =+ pow(d_sqrtrwt[p] * (d_y[p] - d_mu[p]), 2); // } // Rcpp::Rcout << "\ntestwrss: " << testwrss << std::endl; d_wtres = d_sqrtrwt.cwiseProduct(d_y - d_mu); // Rcpp::Rcout << "\nwrss 2:\n" << d_wrss << std::endl; d_wrss = d_wtres.squaredNorm(); // Rcpp::Rcout << "\nwrss: " << d_wrss << std::endl; // Rcpp::Rcout << "\nwrss 3:\n" << d_wrss << std::endl; return d_wrss; } /** * Set a new value of the offset. * * The values are copied into the d_offset member because that member is mapped. * @param oo New value of the offset */ void lmResp::setOffset(const VectorXd& oo) { if (oo.size() != d_offset.size()) throw invalid_argument("setOffset: Size mismatch"); d_offset = oo; // this copies the values } void lmResp::setResp(const VectorXd& yy) { if (yy.size() != d_y.size()) throw invalid_argument("setResp: Size mismatch"); d_y = yy; } void lmResp::setWeights(const VectorXd& ww) { if (ww.size() != d_weights.size()) throw invalid_argument("setWeights: Size mismatch"); d_weights = ww; } lmerResp::lmerResp(SEXP y, SEXP weights, SEXP offset, SEXP mu, SEXP sqrtXwt, SEXP sqrtrwt, SEXP wtres) : lmResp(y, weights, offset, mu, sqrtXwt, sqrtrwt, wtres), d_reml(0) { } double lmerResp::Laplace(double ldL2, double ldRX2, double sqrL) const { double lnum = std::log(2.* M_PI * (d_wrss + sqrL)); if (d_reml == 0) return ldL2 + d_y.size() * (1. + lnum - std::log(d_y.size())); double nmp = d_y.size() - d_reml; return ldL2 + ldRX2 + nmp * (1. + lnum - std::log(nmp)); } double lmerResp::Laplace(double ldL2, double ldRX2, double sqrL, double sigma_sq) const { double df = d_y.size() - d_reml; double result = df * (2.0 * M_LN_SQRT_2PI + std::log(sigma_sq)); // (2pi sigma_sq)^-df/2 result += (d_wrss + sqrL) / sigma_sq; // exp(-1/2sigma_sq x |pwrss|) result += ldL2 + (d_reml > 0 ? ldRX2 : 0.0); // det|LL'|^-1/2 and similar REML penalty return result; } void lmerResp::setReml(int rr) { if (rr < 0) throw invalid_argument("setReml: negative value for REML not meaningful"); d_reml = rr; } glmResp::glmResp(List fam, SEXP y, SEXP weights, SEXP offset, SEXP mu, SEXP sqrtXwt, SEXP sqrtrwt, SEXP wtres, SEXP eta, SEXP n) : lmResp(y, weights, offset, mu, sqrtXwt, sqrtrwt, wtres), d_fam(fam), d_eta(as(eta)), d_n(as(n)) { } double glmResp::aic() const { return d_fam.aic(d_y, d_n, d_mu, d_weights, resDev()); } ArrayXd glmResp::devResid() const { // Rcpp::Rcout << "\nd_mu\n" << d_mu << std::endl; return d_fam.devResid(d_y, d_mu, d_weights); } ArrayXd glmResp::muEta() const { return d_fam.muEta(d_eta); } ArrayXd glmResp::variance() const { return d_fam.variance(d_mu); } ArrayXd glmResp::wrkResids() const { return (d_y - d_mu).array() / muEta(); } ArrayXd glmResp::wrkResp() const { return (d_eta - d_offset).array() + wrkResids(); } ArrayXd glmResp::wtWrkResp() const { return wrkResp() * sqrtWrkWt(); } ArrayXd glmResp::sqrtWrkWt() const { int debug=0; if (debug) Rcpp::Rcout << "(sqrtWrkWt) min muEta: " << muEta().minCoeff() << " min weights: " << d_weights.array().minCoeff() << std::endl; return muEta() * (d_weights.array() / variance()).sqrt(); } double glmResp::Laplace(double ldL2, double ldRX2, double sqrL) const { return ldL2 + sqrL + aic(); } double glmResp::resDev() const { return devResid().sum(); } double glmResp::updateMu(const VectorXd& gamma) { int debug=0; // Rcpp::Rcout << "\nstart of updateMu:\nminimum mu 1:\n" << d_mu.minCoeff() << std::endl; // Rcpp::Rcout << "maximum mu 1:\n" << d_mu.maxCoeff() << std::endl; // Rcpp::Rcout << "minimum gamma 1:\n" << gamma.minCoeff() << std::endl; // Rcpp::Rcout << "maximum gamma 1:\n" << gamma.maxCoeff() << std::endl; // Rcpp::Rcout << "minimum offset 1:\n" << d_offset.minCoeff() << std::endl; // Rcpp::Rcout << "maximum offset 1:\n" << d_offset.maxCoeff() << std::endl; // Rcpp::Rcout << "minimum eta 1:\n" << d_eta.minCoeff() << std::endl; // Rcpp::Rcout << "maximum eta 1:\n" << d_eta.maxCoeff() << std::endl; d_eta = d_offset + gamma; // lengths are checked here // Rcpp::Rcout << "\n after offset+gamma:\nminimum mu 2:\n" << d_mu.minCoeff() << std::endl; // Rcpp::Rcout << "maximum mu 2:\n" << d_mu.maxCoeff() << std::endl; // Rcpp::Rcout << "minimum gamma 2:\n" << gamma.minCoeff() << std::endl; // Rcpp::Rcout << "maximum gamma 2:\n" << gamma.maxCoeff() << std::endl; // Rcpp::Rcout << "minimum offset 2:\n" << d_offset.minCoeff() << std::endl; // Rcpp::Rcout << "maximum offset 2:\n" << d_offset.maxCoeff() << std::endl; // Rcpp::Rcout << "minimum eta 2:\n" << d_eta.minCoeff() << std::endl; // Rcpp::Rcout << "maximum eta 2:\n" << d_eta.maxCoeff() << std::endl; d_mu = d_fam.linkInv(d_eta); if (debug) Rcpp::Rcout << "updateMu: min mu:" << d_mu.minCoeff() << " max mu: " << d_mu.maxCoeff() << std::endl; // Rcpp::Rcout << "maximum mu 3:\n" << d_mu.maxCoeff() << std::endl; // Rcpp::Rcout << "minimum gamma 3:\n" << gamma.minCoeff() << std::endl; // Rcpp::Rcout << "maximum gamma 3:\n" << gamma.maxCoeff() << std::endl; // Rcpp::Rcout << "minimum offset 3:\n" << d_offset.minCoeff() << std::endl; // Rcpp::Rcout << "maximum offset 3:\n" << d_offset.maxCoeff() << std::endl; // Rcpp::Rcout << "minimum eta 3:\n" << d_eta.minCoeff() << std::endl; // Rcpp::Rcout << "maximum eta 3:\n" << d_eta.maxCoeff() << std::endl; return updateWrss(); } double glmResp::updateWts() { d_sqrtrwt = (d_weights.array() / variance()).sqrt(); d_sqrtXwt = muEta() * d_sqrtrwt.array(); return updateWrss(); } void glmResp::setN(const VectorXd& n) { if (n.size() != d_n.size()) throw invalid_argument("n size mismatch"); d_n = n; } nlsResp::nlsResp(SEXP y, SEXP weights, SEXP offset, SEXP mu, SEXP sqrtXwt, SEXP sqrtrwt, SEXP wtres, SEXP gamma, SEXP mm, SEXP ee, SEXP pp) : lmResp(y, weights, offset, mu, sqrtXwt, sqrtrwt, wtres), d_gamma(as(gamma)), d_nlenv(as(ee)), d_nlmod(as(mm)), d_pnames(as(pp)) { } double nlsResp::Laplace(double ldL2, double ldRX2, double sqrL) const { double lnum = 2.* PI * (d_wrss + sqrL), n = d_y.size(); return ldL2 + n * (1 + std::log(lnum / n)); } double nlsResp::updateMu(const VectorXd& gamma) { int n = d_y.size(); if (gamma.size() != d_gamma.size()) throw invalid_argument("size mismatch in updateMu"); std::copy(gamma.data(), gamma.data() + gamma.size(), d_gamma.data()); const VectorXd lp(d_gamma + d_offset); // linear predictor const double *gg = lp.data(); for (int p = 0; p < d_pnames.size(); ++p) { std::string pn(d_pnames[p]); NumericVector pp = d_nlenv.get(pn); std::copy(gg + n * p, gg + n * (p + 1), pp.begin()); } NumericVector rr = d_nlmod.eval(SEXP(d_nlenv)); if (rr.size() != n) throw invalid_argument("dimension mismatch"); std::copy(rr.begin(), rr.end(), d_mu.data()); NumericMatrix gr = rr.attr("gradient"); std::copy(gr.begin(), gr.end(), d_sqrtXwt.data()); return updateWrss(); } } lme4/NAMESPACE0000644000176000001440000000704412273465465012405 0ustar ripleyusersuseDynLib(lme4,.registration=TRUE) ## base packages importFrom("graphics", plot) importFrom("splines", backSpline,interpSpline,periodicSpline) importFrom("methods", show) importFrom("stats", anova, coef, confint, deviance, drop1, extractAIC, family, fitted, formula, logLik, model.matrix, nobs, profile, residuals, simulate, terms, update, vcov, weights) ## Recommended packages importClassesFrom("Matrix", corMatrix, dgCMatrix, dpoMatrix) importFrom("grid", gpar,viewport) importFrom("lattice", densityplot, dotplot, qqmath, splom, xyplot) importFrom("MASS", negative.binomial, theta.ml) importFrom("Matrix", drop0, rankMatrix, rBind, sparseMatrix) importFrom("nlme", fixef,ranef,VarCorr) importMethodsFrom("Matrix", "%*%",coerce,crossprod,diag,t,tcrossprod) ## other CRAN packages: importFrom("minqa", bobyqa) importFrom("Rcpp", evalCpp) export(bootMer) export(devcomp) export(findbars) export(fixef) export(fortify) export(getL) export(getME) export(GHrule) export(glFormula) export(glmer.nb) export(glmer) export(glmerControl) export(glmFamily) export(glmResp) export(golden) export(GQdk) export(isGLMM) export(isLMM) export(isNested) export(isNLMM) export(isREML) export(lFormula) export(lmer) export(lmerControl) export(lmerResp) export(lmList) export(lmResp) export(merPredD) export(mkGlmerDevfun) export(mkLmerDevfun) export(mkMerMod) export(mkRespMod) export(mkReTrms) export(mkVarCorr) export(Nelder_Mead) export(NelderMead) export(nlformula) export(nlmer) export(nlmerControl) export(nlsResp) export(nobars) export(optimizeGlmer) export(optimizeLmer) export(ranef) export(refit) export(refitML) export(rePos) export(sigma) export(subbars) ## export(tnames) export(updateGlmerDevfun) export(VarCorr) export(varianceProf) exportClasses(glmerMod) exportClasses(lmerMod) exportClasses(lmList) exportClasses(merMod) exportClasses(nlmerMod) exportMethods(getL) exportMethods(show) S3method(anova,merMod) S3method(as.data.frame,boot) S3method(as.data.frame,thpr) S3method(as.function,merMod) S3method(coef,lmList) S3method(coef,merMod) S3method(confint,lmList) S3method(confint,merMod)# but don't hide: export(confint.merMod) S3method(confint,thpr) S3method(densityplot,thpr) S3method(deviance,merMod) S3method(dotplot,coef.mer) S3method(dotplot,ranef.mer) S3method(drop1,merMod) S3method(extractAIC,merMod) S3method(family,glmResp) S3method(family,lmResp) S3method(family,merMod) S3method(family,nlsResp) S3method(fitted,merMod) S3method(fixef,merMod) S3method(formula,lmList) S3method(formula,merMod) S3method(fortify,lmerMod)# but don't hide: export(fortify.lmerMod) S3method(isGLMM,merMod) S3method(isLMM,merMod) S3method(isNLMM,merMod) S3method(isREML,merMod) S3method(log,thpr) S3method(logLik,merMod) S3method(model.frame,merMod) S3method(model.matrix,merMod) S3method(nobs,merMod) S3method(plot,coef.mer) S3method(plot,lmList.confint) S3method(plot,merMod) S3method(plot,ranef.mer) S3method(predict,merMod) S3method(print,merMod) S3method(print,summary.merMod) S3method(print,VarCorr.merMod) S3method(profile,merMod) S3method(qqmath,ranef.mer) S3method(ranef,merMod) S3method(refit,merMod) S3method(refitML,merMod) S3method(residuals,glmResp) S3method(residuals,lmResp) S3method(residuals,merMod) S3method(sigma,merMod) S3method(simulate,merMod) S3method(splom,thpr) S3method(summary,merMod) S3method(summary,summary.merMod) S3method(terms,merMod) S3method(update,lmList) S3method(update,merMod) S3method(VarCorr,merMod) S3method(vcov,merMod)# but do not hide: export(vcov.merMod) S3method(vcov,summary.merMod) S3method(weights,merMod) S3method(xyplot,thpr) lme4/data/0000755000176000001440000000000012051752003012047 5ustar 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ripleyuserslme4/R/modular.R0000644000176000001440000005224612232467522013150 0ustar ripleyusers##' @rdname modular ##' @name modular ##' @title Modular functions for mixed model fits ##' @aliases modular ##' @inheritParams lmer ##' @param \dots other potential arguments. ##' @details These functions make up the internal components of a(n) [gn]lmer fit. ##' \itemize{ ##' \item \code{[g]lFormula} takes the arguments that would normally be passed to \code{[g]lmer}, ##' checking for errors and processing the formula and data input to create ##' \item \code{mk(Gl|L)merDevfun} takes the output of the previous step (minus the \code{formula} ##' component) and creates a deviance function ##' \item \code{optimize(Gl|L)mer} takes a deviance function and optimizes over \code{theta} (or over \code{theta} and \code{beta}, if \code{stage} is set to 2 for \code{optimizeGlmer} ##' \item \code{updateGlmerDevfun} takes the first stage of a GLMM optimization (with \code{nAGQ=0}, ##' optimizing over \code{theta} only) and produces a second-stage deviance function ##' \item \code{\link{mkMerMod}} takes the \emph{environment} of a deviance function, the results of ##' an optimization, a list of random-effect terms, a model frame, and a model all and produces a ##' \code{[g]lmerMod} object ##' } ##' @examples ##' ##' ### Fitting a linear mixed model in 4 modularized steps ##' ##' ## 1. Parse the data and formula: ##' lmod <- lFormula(Reaction ~ Days + (Days|Subject), sleepstudy) ##' names(lmod) ##' ## 2. Create the deviance function to be optimized: ##' (devfun <- do.call(mkLmerDevfun, lmod)) ##' ls(environment(devfun)) # the environment of devfun contains objects required for its evaluation ##' ## 3. Optimize the deviance function: ##' opt <- optimizeLmer(devfun) ##' opt[1:3] ##' ## 4. Package up the results: ##' mkMerMod(environment(devfun), opt, lmod$reTrms, fr = lmod$fr) ##' ##' ##' ### Same model in one line ##' lmer(Reaction ~ Days + (Days|Subject), sleepstudy) ##' ##' ##' ### Fitting a generalized linear mixed model in six modularized steps ##' ##' ## 1. Parse the data and formula: ##' glmod <- glFormula(cbind(incidence, size - incidence) ~ period + (1 | herd), ##' data = cbpp, family = binomial) ##' names(glmod) ##' ## 2. Create the deviance function for optimizing over theta: ##' (devfun <- do.call(mkGlmerDevfun, glmod)) ##' ls(environment(devfun)) # the environment of devfun contains lots of info ##' ## 3. Optimize over theta using a rough approximation (i.e. nAGQ = 0): ##' (opt <- optimizeGlmer(devfun)) ##' ## 4. Update the deviance function for optimizing over theta and beta: ##' (devfun <- updateGlmerDevfun(devfun, glmod$reTrms)) ##' ## 5. Optimize over theta and beta: ##' opt <- optimizeGlmer(devfun, stage=2) ##' opt[1:3] ##' ## 6. Package up the results: ##' mkMerMod(environment(devfun), opt, glmod$reTrms, fr = glmod$fr) ##' ##' ##' ### Same model in one line ##' glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), ##' data = cbpp, family = binomial) ##' NULL ### Small utilities to be used in lFormula() and glFormula() doCheck <- function(x) { is.character(x) && !any(x == "ignore") } checkZrank <- function(Zt, n, ctrl, nonSmall = 1e6, allow.n=FALSE) { stopifnot(is.list(ctrl), is.numeric(n), is.numeric(nonSmall)) cstr <- "check.nobs.vs.rankZ" if (doCheck(cc <- ctrl[[cstr]])) { ## not NULL or "ignore" d <- dim(Zt) doTr <- d[1L] < d[2L] # Zt is "wide" => qr needs transpose(Zt) if(!(grepl("Small",cc) && prod(d) > nonSmall)) { rankZ <- rankMatrix(if(doTr) t(Zt) else Zt, method="qr", sval = numeric(min(d))) if (allow.n) { unident <- n 1 sampled level" switch(cc, "warning" = warning(wstr), "stop" = stop(wstr), stop(gettextf("unknown check level for '%s'", cstr), domain=NA)) } ## Part 2 ---------------- cstr <- "check.nobs.vs.nlev" if (doCheck(cc <- ctrl[[cstr]])) { if (any(if(allow.n) nlevelVec > n else nlevelVec >= n)) stop(gettextf( "number of levels of each grouping factor must be %s number of observations", if(allow.n) "<=" else "<"), domain=NA) } ## Part 3 ---------------- cstr <- "check.nlev.gtreq.5" if (doCheck(cc <- ctrl[[cstr]]) && any(nlevelVec < 5)) { wstr <- "grouping factors with < 5 sampled levels may give unreliable estimates" switch(cc, "warning" = warning(wstr), "stop" = stop(wstr), stop(gettextf("unknown check level for '%s'", cstr), domain=NA)) } } ##' @rdname modular ##' @param control a list giving (for \code{[g]lFormula}) all options (see \code{\link{lmerControl}} for running the model; ##' (for \code{mkLmerDevfun,mkGlmerDevfun}) options for inner optimization step; ##' (for \code{optimizeLmer} and \code{optimize[Glmer}) control parameters for nonlinear optimizer (typically inherited from the \dots argument to \code{lmerControl}) ##' @return \bold{lFormula, glFormula}: A list containing components, ##' \item{fr}{model frame} ##' \item{X}{fixed-effect design matrix} ##' \item{reTrms}{list containing information on random effects structure: result of \code{\link{mkReTrms}}} ##' \item{REML}{(lFormula only): logical flag: use restricted maximum likelihood? (Copy of argument.)} ##' @importFrom Matrix rankMatrix ##' @export lFormula <- function(formula, data=NULL, REML = TRUE, subset, weights, na.action, offset, contrasts = NULL, control=lmerControl(), ...) { control <- control$checkControl ## this is all we really need mf <- mc <- match.call() ignoreArgs <- c("start","verbose","devFunOnly","control") l... <- list(...) l... <- l...[!names(l...) %in% ignoreArgs] do.call("checkArgs",c(list("lmer"),l...)) if (!is.null(list(...)[["family"]])) { ## lmer(...,family=...); warning issued within checkArgs mc[[1]] <- quote(lme4::glFormula) if (missing(control)) mc[["control"]] <- glmerControl() return(eval(mc, parent.frame())) } denv <- checkFormulaData(formula,data) mc$formula <- formula <- as.formula(formula,env=denv) ## substitute evaluated call m <- match(c("data", "subset", "weights", "na.action", "offset"), names(mf), 0) mf <- mf[c(1, m)] mf$drop.unused.levels <- TRUE mf[[1]] <- as.name("model.frame") fr.form <- subbars(formula) # substitute "|" by "+" environment(fr.form) <- environment(formula) mf$formula <- fr.form fr <- eval(mf, parent.frame()) ## store full, original formula & offset attr(fr,"formula") <- formula attr(fr,"offset") <- mf$offset n <- nrow(fr) ## random effects and terms modules reTrms <- mkReTrms(findbars(formula[[3]]), fr) checkNlevels(reTrms$ flist, n=n, control) checkZrank (reTrms$ Zt, n=n, control, nonSmall = 1e6) ## fixed-effects model matrix X - remove random effects from formula: fixedform <- formula fixedform[[3]] <- if(is.null(nb <- nobars(fixedform[[3]]))) 1 else nb mf$formula <- fixedform ## re-evaluate model frame to extract predvars component fixedfr <- eval(mf, parent.frame()) attr(attr(fr,"terms"),"predvars.fixed") <- attr(attr(fixedfr,"terms"),"predvars") X <- model.matrix(fixedform, fr, contrasts)#, sparse = FALSE, row.names = FALSE) ## sparseX not yet p <- ncol(X) if ((rankX <- rankMatrix(X)) < p) stop(gettextf("rank of X = %d < ncol(X) = %d", rankX, p)) list(fr = fr, X = X, reTrms = reTrms, REML = REML, formula = formula) } ## utility f'n for checking starting values getStart <- function(start,lower,pred,returnVal=c("theta","all")) { returnVal <- match.arg(returnVal) ## default values theta <- pred$theta fixef <- pred$delb if (!is.null(start)) { if (is.numeric(start)) { theta <- start } else { if (!is.list(start)) stop("start must be a list or a numeric vector") if (!all(sapply(start,is.numeric))) stop("all elements of start must be numeric") if (length((badComp <- setdiff(names(start),c("theta","fixef"))))>0) { stop("incorrect components in start list: ",badComp) } if (!is.null(start$theta)) theta <- start$theta if (!is.null(start$fixef)) fixef <- start$fixef } } if (length(theta)!=length(pred$theta)) stop("incorrect number of theta components (!=",length(pred$theta),")") if (length(fixef)!=length(pred$delb)) stop("incorrect number of fixef components (!=",length(pred$delb),")") if (returnVal=="theta") theta else c(theta,fixef) } ## update start ## should refactor this to ## turn numeric start into start=list(theta=start) immediately ... ?? updateStart <- function(start,theta) { if (is.null(start)) return(NULL) if (is.numeric(start)) { start <- theta } else if (!is.null(start$theta)) start$theta <- theta start } ##' @rdname modular ##' @param fr A model frame containing the variables needed to create an ##' \code{\link{lmerResp}} or \code{\link{glmResp}} instance ##' @param X fixed-effects design matrix ##' @param reTrms information on random effects structure (see \code{\link{mkReTrms}}) ##' @param REML (logical) fit restricted maximum likelihood model? ##' @param start starting values ##' @param verbose print output? ##' @return \bold{mkLmerDevfun, mkGlmerDevfun}: A function to calculate deviance ##' (or restricted deviance) as a function of the theta (random-effect) parameters ##' (for GlmerDevfun, of beta (fixed-effect) parameters as well). These deviance ##' functions have an environment containing objects required for their evaluation. ##' CAUTION: The output object of \code{mk(Gl|L)merDevfun} is an \code{\link{environment}} ##' containing reference class objects (see \code{\link{ReferenceClasses}}, \code{\link{merPredD-class}}, ##' \code{\link{lmResp-class}}), which behave in ways that may surprise many users. For example, if the ##' output of \code{mk(Gl|L)merDevfun} is naively copied, then modifications to the original will ##' also appear in the copy (and vice versa). To avoid this behavior one must make a deep copy ##' (see \code{\link{ReferenceClasses}} for details). ##' \cr ##' \cr ##' @export mkLmerDevfun <- function(fr, X, reTrms, REML = TRUE, start = NULL, verbose=0, control=lmerControl(), ...) { ## FIXME: make sure verbose gets handled properly #if (missing(fr)) { ## reconstitute frame #} ## pull necessary arguments for making the model frame out of ... p <- ncol(X) # maybe also do rank check on X here?? rho <- new.env(parent=parent.env(environment())) rho$pp <- do.call(merPredD$new, c(reTrms[c("Zt","theta","Lambdat","Lind")], n=nrow(X), list(X=X))) REMLpass <- if(REML) p else 0L if(missing(fr)) rho$resp <- mkRespMod(REML = REMLpass, ...) else rho$resp <- mkRespMod(fr, REML = REMLpass) ## note: REML does double duty as rank of X and a flag for using REML ## maybe this should be mentioned in the help file for mkRespMod?? ## currently that help file says REML is logical devfun <- mkdevfun(rho, 0L, verbose, control) theta <- getStart(start,reTrms$lower,rho$pp) devfun(rho$pp$theta) # one evaluation to ensure all values are set rho$lower <- reTrms$lower # SCW: in order to be more consistent with mkLmerDevfun return(devfun) # this should pass the rho environment implicitly } ##' @rdname modular ##' @inheritParams lmer ##' @inheritParams lmerControl ##' @param devfun a deviance function, as generated by \code{\link{mkLmerDevfun}} ##' @return \bold{optimizeLmer}: Results of an optimization. ##' \cr ##' \cr ##' @export optimizeLmer <- function(devfun, optimizer="Nelder_Mead", restart_edge=FALSE, start = NULL, verbose = 0L, control = list()) { verbose <- as.integer(verbose) rho <- environment(devfun) opt <- optwrap(optimizer, devfun, getStart(start,rho$lower,rho$pp), lower=rho$lower, control=control, adj=FALSE, verbose=verbose) if (restart_edge) { ## FIXME: should we be looking at rho$pp$theta or opt$par ## at this point??? in koller example (for getData(13)) we have ## rho$pp$theta=0, opt$par=0.08 if (length(bvals <- which(rho$pp$theta==rho$lower))>0) { ## *don't* use numDeriv -- cruder but fewer dependencies, no worries ## about keeping to the interior of the allowed space theta0 <- new("numeric",rho$pp$theta) ## 'deep' copy ... d0 <- devfun(theta0) btol <- 1e-5 ## FIXME: make user-settable? bgrad <- sapply(bvals, function(i) { bndval <- rho$lower[i] theta <- theta0 theta[i] <- bndval+btol (devfun(theta)-d0)/btol }) ## what do I need to do to reset rho$pp$theta to original value??? devfun(theta0) ## reset rho$pp$theta after tests ## FIXME: allow user to specify ALWAYS restart if on boundary? if (any(bgrad<0)) { if (verbose) message("some theta parameters on the boundary, restarting") opt <- optwrap(optimizer, devfun, opt$par, lower=rho$lower, control=control, adj=FALSE, verbose=verbose) } } } return(opt) } ## TODO: remove any arguments that aren't actually used by glFormula (same for lFormula) ## TODO(?): lFormula() and glFormula() are very similar: merge or use common baseFun() ##' @rdname modular ##' @inheritParams glmer ##' @export glFormula <- function(formula, data=NULL, family = gaussian, subset, weights, na.action, offset, contrasts = NULL, mustart, etastart, control=glmerControl(), ...) { ## FIXME: does start= do anything? test & fix control <- control$checkControl ## this is all we really need mf <- mc <- match.call() ## extract family, call lmer for gaussian if (is.character(family)) family <- get(family, mode = "function", envir = parent.frame(2)) if( is.function(family)) family <- family() if (isTRUE(all.equal(family, gaussian()))) { mc[[1]] <- quote(lme4::lFormula) mc["family"] <- NULL # to avoid an infinite loop return(eval(mc, parent.frame())) } if (family$family %in% c("quasibinomial", "quasipoisson", "quasi")) stop('"quasi" families cannot be used in glmer') ignoreArgs <- c("start","verbose","devFunOnly","optimizer", "control", "nAGQ") l... <- list(...) l... <- l...[!names(l...) %in% ignoreArgs] do.call("checkArgs",c(list("glmer"),l...)) denv <- checkFormulaData(formula,data) mc$formula <- formula <- as.formula(formula,env=denv) ## substitute evaluated version m <- match(c("data", "subset", "weights", "na.action", "offset", "mustart", "etastart"), names(mf), 0) mf <- mf[c(1, m)] mf$drop.unused.levels <- TRUE mf[[1]] <- as.name("model.frame") fr.form <- subbars(formula) # substitute "|" by "+" environment(fr.form) <- environment(formula) mf$formula <- fr.form fr <- eval(mf, parent.frame()) ## store full, original formula & offset attr(fr,"formula") <- formula attr(fr,"offset") <- mf$offset n <- nrow(fr) ## random effects and terms modules reTrms <- mkReTrms(findbars(formula[[3]]), fr) ## TODO: allow.n = !useSc {see FIXME below} checkNlevels(reTrms$ flist, n=n, control, allow.n=TRUE) checkZrank (reTrms$ Zt, n=n, control, nonSmall = 1e6, allow.n=TRUE) ## FIXME: adjust test for families with estimated scale parameter: ## useSc is not defined yet/not defined properly? ## if (useSc && maxlevels == n) ## stop("number of levels of each grouping factor must be", ## "greater than number of obs") ## fixed-effects model matrix X - remove random parts from formula: fixedform <- formula fixedform[[3]] <- if(is.null(nb <- nobars(fixedform[[3]]))) 1 else nb mf$formula <- fixedform ## re-evaluate model frame to extract predvars component fixedfr <- eval(mf, parent.frame()) attr(attr(fr,"terms"),"predvars.fixed") <- attr(attr(fixedfr,"terms"),"predvars") X <- model.matrix(fixedform, fr, contrasts)#, sparse = FALSE, row.names = FALSE) ## sparseX not yet p <- ncol(X) if ((rankX <- rankMatrix(X)) < p) stop(gettextf("rank of X = %d < ncol(X) = %d", rankX, p)) list(fr = fr, X = X, reTrms = reTrms, family = family, formula = formula) } ##' @rdname modular ##' @export mkGlmerDevfun <- function(fr, X, reTrms, family, nAGQ = 1L, verbose = 0L, control=glmerControl(), ...){ stopifnot(length(nAGQ <- as.integer(nAGQ)) == 1L, nAGQ >= 0L, nAGQ <= 25L) verbose <- as.integer(verbose) rho <- as.environment(list(verbose=verbose, tolPwrss=control$tolPwrss, compDev=control$compDev)) parent.env(rho) <- parent.frame() rho$pp <- do.call(merPredD$new, c(reTrms[c("Zt","theta","Lambdat","Lind")], n=nrow(X), list(X=X))) if (missing(fr)) rho$resp <- mkRespMod(family=family, ...) else rho$resp <- mkRespMod(fr, family=family) if (length(unique(rho$resp$y)) < 2L) stop("Response is constant - cannot fit the model") rho$verbose <- as.integer(verbose) ## initialize (from mustart) .Call(glmerLaplace, rho$pp$ptr(), rho$resp$ptr(), 0L, control$tolPwrss, verbose) rho$lp0 <- rho$pp$linPred(1) # each pwrss opt begins at this eta rho$pwrssUpdate <- glmerPwrssUpdate rho$lower <- reTrms$lower # not needed in rho? devfun <- mkdevfun(rho, 0L, verbose, control) #if (devFunOnly && !nAGQ) return(devfun) return(devfun) # this should pass the rho environment implicitly } ##' @rdname modular ##' @param nAGQ number of Gauss-Hermite quadrature points ##' @param stage optimization stage (1: nAGQ=0, optimize over theta only; 2: nAGQ possibly >0, optimize over theta and beta) ##' @export optimizeGlmer <- function(devfun, optimizer="bobyqa", restart_edge=FALSE, verbose = 0L, control = list(), nAGQ = 1L, stage = 1, start = NULL) { ## FIXME: do we need nAGQ here?? or can we clean up? verbose <- as.integer(verbose) rho <- environment(devfun) if (stage==1) { start <- getStart(start, lower=rho$lower, pred=rho$pp, "theta") adj <- FALSE } else { ## stage == 2 start <- getStart(start, lower=rho$lower, pred=rho$pp, returnVal="all") adj <- TRUE if (missing(optimizer)) optimizer <- "Nelder_Mead" ## BMB: too clever? } opt <- optwrap(optimizer, devfun, start, rho$lower, control=control, adj=adj, verbose=verbose) if (stage==1) { rho$control <- attr(opt,"control") rho$nAGQ <- nAGQ } else { ## stage == 2 rho$resp$setOffset(rho$baseOffset) } ## FIXME: implement this ... if (restart_edge) stop("restart_edge not implemented for optimizeGlmer yet") return(opt) } ## only do this function if nAGQ > 0L ##' @rdname modular ##' @export updateGlmerDevfun <- function(devfun, reTrms, nAGQ = 1L){ rho <- environment(devfun) rho$nAGQ <- nAGQ rho$lower <- c(rho$lower, rep.int(-Inf, length(rho$pp$beta0))) rho$lp0 <- rho$pp$linPred(1) rho$dpars <- seq_along(rho$pp$theta) rho$baseOffset <- rho$resp$offset + 0 # forcing a copy (!) rho$GQmat <- GHrule(nAGQ) rho$fac <- reTrms$flist[[1]] if (nAGQ > 1L) { if (length(reTrms$flist) != 1L || length(reTrms$cnms[[1]]) != 1L) stop("nAGQ > 1 is only available for models with a single, scalar random-effects term") } devfun <- mkdevfun(rho, nAGQ) # does this attach rho to devfun?? return(devfun) } lme4/R/vcconv.R0000644000176000001440000001255312232467515013002 0ustar ripleyusers## These files are not currently exported; we are still trying to figure ## out the most appropriate user interface/naming convention/etc ## These functions will become more important, and need to be ## modified/augmented, if we start allowing for varying V-C structures ## (e.g. diagonal matrices, compound symmetry ...) ## In principle we might want to extract or input information: ## 1. as variance-covariance matrices ## 2. as Cholesky factors ## 3. as 'sdcorr' matrices (std dev on diagonal, correlations off diagonal) ## and we might want the structure to be: ## 1. a concatenated vector representing the lower triangles ## (with an attribute carrying the information about group sizes) ## 2. a list of lower-triangle vectors ## 3. a list of matrices ## 4. a block-diagonal matrix ## If we are trying to convert to and from theta vectors, we also ## have to consider whether we are returning scaled Cholesky factors/ ## var-cov matrices or unscaled ones. For the code below I have ## chosen to allow the residual variance etc. to be appended as ## the last element of a variance-covariance vector. (This last ## part is a little less generic than the rest of it.) ## Convert list of matrices to concatenated vector of lower triangles ## with an attribute that gives the dimension of each matrix ## in the original list mlist2vec <- function(L) { n <- sapply(L,nrow) ## allow for EITHER upper- or lower-triangular input; ## in either case, read off in "lower-triangular" order ## (column-wise) ff <- function(x) { if (all(x[upper.tri(x)]==0)) t(x[lower.tri(x,diag=TRUE)]) else t(x)[lower.tri(x,diag=TRUE)] } r <- unlist(lapply(L,ff)) attr(r,"clen") <- n r } ## Compute dimensions of a square matrix from the size ## of the lower triangle (length as a vector) get_clen <- function(v,n=NULL) { if (is.null(n)) { if (is.null(n <- attr(v,"clen"))) { ## single component n <- (sqrt(8*length(v)+1)-1)/2 } } n } ## Convert concatenated vector to list of Cholesky factors ## (lower triangle or symmetric) vec2mlist <- function(v,n=NULL,symm=TRUE) { n <- get_clen(v,n) s <- split(v,rep.int(seq_along(n),n*(n+1)/2)) m <- mapply(function(x,n0) { m0 <- diag(nrow=n0) m0[lower.tri(m0,diag=TRUE)] <- x if (symm) m0[upper.tri(m0)] <- t(m0)[upper.tri(m0)] m0 },s,n,SIMPLIFY=FALSE) m } ## Convert concatenated vector to list of ST matrices vec2STlist <- function(v, n = NULL){ ch <- vec2mlist(v, n, FALSE) # cholesky nch <- length(ch) sdiag <- function(x) { ## 'safe' diag() if (length(x)==1) matrix(x,1,1) else diag(x) } lapply(ch, function(L) { ST <- L%*%sdiag(1/sdiag(L)) diag(ST) <- diag(L) ST }) } ## convert 'sdcor' format -- diagonal = std dev, off-diag=cor ## to and from variance-covariance matrix sdcor2cov <- function(m) { sd <- diag(m) diag(m) <- 1 m * outer(sd,sd) } ## convert cov to sdcor cov2sdcor <- function(m) { v <- diag(m) m1 <- cov2cor(m) diag(m1) <- sqrt(v) m1 } dmult <- function(m,s) { diag(m) <- diag(m)*s m } ## attempt to compute Cholesky, allow for positive semi-definite cases ## (hackish) safe_chol <- function(m) { if (all(m==0)) return(m) if (nrow(m)==1) return(sqrt(m)) if (all(dmult(m,0)==0)) { ## diagonal return(diag(sqrt(diag(m)))) } ## attempt regular Chol. decomp if (!is.null(cc <- tryCatch(chol(m), error=function(e) NULL))) return(cc) ## ... pivot if necessary ... cc <- suppressWarnings(chol(m,pivot=TRUE)) oo <- order(attr(cc,"pivot")) cc[,oo] ## FIXME: pivot is here to deal with semidefinite cases, ## but results might be returned in a strange format: TEST } ## from var-cov to scaled Cholesky: Vv_to_Cv <- function(v,n=NULL,s=1) { if (!missing(s)) { v <- v[-length(v)] } r <- mlist2vec(lapply(vec2mlist(v,n,symm=TRUE), function(m) t(safe_chol(m/s^2)))) attr(r,"clen") <- get_clen(v,n) r } ## from sd-cor to scaled Cholesky: Sv_to_Cv <- function(v,n=NULL,s=1) { if (!missing(s)) { v <- v[-length(v)] } r <- mlist2vec(lapply(vec2mlist(v,n,symm=TRUE), function(m) t(safe_chol(sdcor2cov(m)/s^2)))) attr(r,"clen") <- get_clen(v,n) r } ## from unscaled Cholesky vector to (possibly scaled) ## variance-covariance vector Cv_to_Vv <- function(v,n=NULL,s=1) { r <- mlist2vec(lapply(vec2mlist(v,n,symm=FALSE), function(m) tcrossprod(m)*s^2)) if (!missing(s)) r <- c(r,s^2) attr(r,"clen") <- get_clen(v,n) r } ## from unscaled Chol to sd-cor vector Cv_to_Sv <- function(v,n=NULL,s=1) { r <- mlist2vec(lapply(vec2mlist(v,n,symm=FALSE), function(m) cov2sdcor(tcrossprod(m)*s^2))) if (!missing(s)) r <- c(r,s) attr(r,"clen") <- get_clen(v,n) r } if (FALSE) { cvec1 <- 1:6 Cv_to_Vv(cvec1) Vv_to_Cv(Cv_to_Vv(0)) Cv_to_Vv(cvec1,s=2) Sv_to_Cv(Cv_to_Sv(cvec1)) Vv_to_Cv(Cv_to_Vv(cvec1)) ## for length-1 matrices, Cv_to_Sv should be equivalent ## to multiplying Cv by sigma and appending sigma .... clist2 <- list(matrix(1),matrix(2),matrix(3)) cvec2 <- mlist2vec(clist2) all((cvec3 <- Cv_to_Sv(cvec2,s=2))==c(cvec2*2,2)) all(Sv_to_Cv(cvec3,n=rep(1,3),s=2)== cvec3[-length(cvec3)]/cvec3[length(cvec3)]) } lme4/R/GHrule.R0000644000176000001440000002451712156422372012672 0ustar ripleyusers##' Create a univariate Gauss-Hermite quadrature rule ##' ##' This version of Gauss-Hermite quadrature provides the node ##' positions and weights for a scalar integral of a function ##' multiplied by the standard normal density. ##' @title Univariate Gauss-Hermite quadrature rule ##' @param ord scalar integer between 1 and 25 - the order, or number of ##' nodes and weights, in the rule. When the function being ##' multiplied by the standard normal density is a polynomial of ##' order 2k-1 the rule of order k integrates the product exactly. ##' @param asMatrix logical scalar - should the result be returned as ##' a matrix. If \code{FALSE} a data frame is returned. Defaults ##' to \code{TRUE}. ##' @return a matrix with \code{ord} rows and three columns which are ##' \code{z} the node positions, \code{w} the weights and ##' \code{ldnorm}, the logarithm of the normal density evaluated at ##' the nodes. ##' @examples ##' (r5 <- GHrule(5, asMatrix=FALSE)) ##' ## second, fourth, sixth, eighth and tenth central moments of the ##' ## standard Gaussian density ##' with(r5, sapply(seq(2, 10, 2), function(p) sum(w * z^p))) ##' @export GHrule <- function (ord, asMatrix=TRUE) { stopifnot(length(ord) == 1, (ord <- as.integer(ord)) >= 0L, ord < 26L) if (ord == 0L) { if (asMatrix) return(matrix(0, nrow=0L, ncol=3L)) stop ("combination of ord==0 and asMatrix==TRUE not implemented") } fr <- as.data.frame(switch(ord, list(z = 0, w = 1), list(z = 1, w = 0.5), list(z = c(0, 1.73205080756888), w = c(0.666666666666667, 0.166666666666667)), list(z = c(0.741963784302726, 2.33441421833898), w = c(0.454124145231931, 0.0458758547680685)), list(z = c(0, 1.35562617997427, 2.85697001387281), w = c(0.533333333333333, 0.222075922005613, 0.0112574113277207)), list(z = c(0.616706590192594, 1.88917587775371, 3.32425743355212), w = c(0.408828469556029, 0.0886157460419145, 0.00255578440205624)), list(z = c(0, 1.15440539473997, 2.36675941073454, 3.75043971772574), w = c(0.457142857142858, 0.240123178605013, 0.0307571239675865, 0.000548268855972219)), list(z = c(0.539079811351375, 1.63651904243511, 2.80248586128754, 4.14454718612589), w = c(0.373012257679077, 0.117239907661759, 0.00963522012078826, 0.000112614538375368)), list(z = c(0, 1.02325566378913, 2.07684797867783, 3.20542900285647, 4.51274586339978), w = c(0.406349206349207, 0.244097502894939, 0.049916406765218, 0.00278914132123177, 2.23458440077466e-05)), list(z = c(0.484935707515498, 1.46598909439116, 2.48432584163895, 3.58182348355193, 4.85946282833231), w = c(0.344642334932019, 0.135483702980267, 0.0191115805007703, 0.00075807093431222, 4.31065263071831e-06)), list(z = c(0, 0.928868997381064, 1.87603502015485, 2.86512316064364, 3.93616660712998, 5.18800122437487), w = c(0.36940836940837, 0.24224029987397, 0.0661387460710576, 0.00672028523553727, 0.000195671930271223, 8.1218497902149e-07)), list(z = c(0.444403001944139, 1.34037519715162, 2.2594644510008, 3.2237098287701, 4.27182584793228, 5.50090170446775), w = c(0.32166436151283, 0.14696704804533, 0.0291166879123641, 0.00220338068753318, 4.83718492259061e-05, 1.49992716763716e-07)), list(z = c(0, 0.85667949351945, 1.72541837958824, 2.62068997343221, 3.56344438028163, 4.59139844893652, 5.8001672523865), w = c(0.340992340992341, 0.237871522964136, 0.0791689558604501, 0.0117705605059965, 0.000681236350442926, 1.15265965273339e-05, 2.7226276428059e-08)), list(z = c(0.412590457954602, 1.24268895548546, 2.08834474570194, 2.96303657983867, 3.88692457505977, 4.89693639734556, 6.08740954690129), w = c(0.302634626813019, 0.154083339842514, 0.0386501088242534, 0.00442891910694741, 0.000200339553760744, 2.66099134406763e-06, 4.86816125774839e-09)), list(z = c(0, 0.799129068324548, 1.60671006902873, 2.43243682700976, 3.28908242439877, 4.19620771126902, 5.19009359130478, 6.36394788882984), w = c(0.318259518259518, 0.232462293609732, 0.0894177953998444, 0.0173657744921376, 0.00156735750354996, 5.64214640518902e-05, 5.9754195979206e-07, 8.58964989963318e-10)), list(z = c(0.386760604500557, 1.16382910055496, 1.95198034571633, 2.7602450476307, 3.60087362417155, 4.49295530252001, 5.47222570594934, 6.63087819839313), w = c(0.286568521238012, 0.158338372750949, 0.0472847523540141, 0.00726693760118474, 0.00052598492657391, 1.53000321624873e-05, 1.30947321628682e-07, 1.49781472316183e-10)), list(z = c(0, 0.751842600703896, 1.50988330779674, 2.28101944025299, 3.07379717532819, 3.90006571719801, 4.77853158962998, 5.74446007865941, 6.88912243989533), w = c(0.299538370126608, 0.226706308468979, 0.0974063711627181, 0.0230866570257112, 0.00285894606228465, 0.000168491431551339, 4.01267944797987e-06, 2.80801611793058e-08, 2.58431491937492e-11)), list(z = c(0.365245755507698, 1.0983955180915, 1.83977992150865, 2.59583368891124, 3.37473653577809, 4.1880202316294, 5.05407268544274, 6.0077459113596, 7.13946484914648), w = c(0.272783234654288, 0.160685303893513, 0.0548966324802227, 0.0105165177519414, 0.00106548479629165, 5.1798961441162e-05, 1.02155239763698e-06, 5.90548847883655e-09, 4.41658876935871e-12)), list(z = c(0, 0.71208504404238, 1.42887667607837, 2.15550276131694, 2.89805127651575, 3.66441654745064, 4.46587262683103, 5.32053637733604, 6.26289115651325, 7.38257902403043), w = c(0.283773192751521, 0.220941712199144, 0.103603657276144, 0.0286666910301185, 0.00450723542034204, 0.000378502109414268, 1.53511459546667e-05, 2.53222003209287e-07, 1.22037084844748e-09, 7.48283005405723e-13)), list(z = c(0.346964157081356, 1.04294534880275, 1.74524732081413, 2.45866361117237, 3.18901481655339, 3.94396735065732, 4.73458133404606, 5.5787388058932, 6.51059015701366, 7.61904854167976), w = c(0.260793063449555, 0.161739333984, 0.061506372063976, 0.013997837447101, 0.00183010313108049, 0.000128826279961929, 4.40212109023086e-06, 6.12749025998296e-08, 2.48206236231518e-10, 1.25780067243793e-13)), list(z = c(0, 0.678045692440644, 1.35976582321123, 2.04910246825716, 2.75059298105237, 3.46984669047538, 4.21434398168842, 4.99496394478203, 5.82938200730447, 6.75144471871746, 7.84938289511382), w = c(0.270260183572877, 0.21533371569506, 0.108392285626419, 0.0339527297865428, 0.00643969705140878, 0.000708047795481537, 4.21923474255159e-05, 1.22535483614825e-06, 1.45066128449307e-08, 4.97536860412175e-11, 2.09899121956567e-14)), list(z = c(0.331179315715274, 0.995162422271216, 1.66412483911791, 2.34175999628771, 3.03240422783168, 3.74149635026652, 4.47636197731087, 5.24772443371443, 6.0730749511229, 6.98598042401882, 8.07402998402171), w = c(0.250243596586935, 0.161906293413675, 0.0671963114288899, 0.0175690728808058, 0.00280876104757721, 0.000262283303255964, 1.33459771268087e-05, 3.319853749814e-07, 3.36651415945821e-09, 9.84137898234601e-12, 3.47946064787714e-15)), list(z = c(0, 0.648471153534496, 1.29987646830398, 1.95732755293342, 2.62432363405918, 3.30504002175297, 4.0047753217333, 4.73072419745147, 5.49347398647179, 6.3103498544484, 7.21465943505186, 8.29338602741735), w = c(0.258509740808839, 0.209959669577543, 0.112073382602621, 0.0388671837034809, 0.00857967839146566, 0.00116762863749786, 9.3408186090313e-05, 4.08997724499215e-06, 8.77506248386172e-08, 7.67088886239991e-10, 1.92293531156779e-12, 5.73238316780209e-16)), list(z = c(0.317370096629452, 0.953421922932109, 1.59348042981642, 2.24046785169175, 2.89772864322331, 3.56930676407356, 4.26038360501991, 4.97804137463912, 5.7327471752512, 6.54167500509863, 7.43789066602166, 8.50780351919526), w = c(0.240870115546641, 0.161459512867, 0.0720693640171784, 0.021126344408967, 0.00397660892918131, 0.000464718718779398, 3.2095005652746e-05, 1.21765974544258e-06, 2.26746167348047e-08, 1.71866492796487e-10, 3.71497415276242e-13, 9.39019368904192e-17)), list(z = c(0, 0.622462279186076, 1.24731197561679, 1.87705836994784, 2.51447330395221, 3.16277567938819, 3.82590056997249, 4.50892992296729, 5.21884809364428, 5.9660146906067, 6.76746496380972, 7.65603795539308, 8.71759767839959), w = c(0.248169351176485, 0.20485102565034, 0.114880924303952, 0.043379970167645, 0.0108567559914623, 0.0017578504052638, 0.000177766906926527, 1.06721949052025e-05, 3.5301525602455e-07, 5.73802386889938e-09, 3.79115000047719e-11, 7.10210303700393e-14, 1.53003899799868e-17)) ) ) nr <- nrow(fr) if (ord %% 2L) { # ord is odd if (nr > 1L) { fr <- rbind(fr[rev(2:nr), ], fr) } } else fr <- rbind(fr[rev(seq_len(nr)),], fr) if (ord > 1L) fr[seq_len(ord %/% 2L), "z"] <- -fr[seq_len(ord %/% 2L), "z"] rownames(fr) <- z <- NULL fr <- within(fr, ldnorm <- dnorm(z, log=TRUE)) if (asMatrix) return(as.matrix(fr)) fr } lme4/R/sparsegrid.R0000644000176000001440000000313412156422372013637 0ustar ripleyusers##' Generate the sparse multidimensional Gaussian quadrature grids ##' ##' @title Sparse Gaussian Quadrature grid ##' @param d integer scalar - the dimension of the function to be ##' integrated with respect to the standard \code{d}-dimensional ##' Gaussian density ##' @param k integer scalar - the order of the grid. A grid of order ##' \code{k} provides an exact result for a polynomial of total order ##' of \code{2k - 1} or less multiplied by the ##' @return a matrix with \code{d + 1} columns. The first column is ##' the weights and the remaining \code{d} columns are the node ##' coordinates. ##' @note The number of nodes gets very large very quickly with ##' increasing \code{d} and \code{k}. See the charts at ##' \url{http://www.sparse-grids.de}. ##' @examples ##' GQdk(2,5) ##' @export GQdk <- function(d=1L, k=1L) { stopifnot(0L < (d <- as.integer(d)[1]), d <= 20L, 0L < (k <- as.integer(k)[1]), k <= length(GQNd <- GQN[[d]])) tmat <- t(GQNd[[k]]) dseq <- seq_len(d) rperms <- lapply(.Call(allPerm_int, dseq + 1L), function(v) c(1L, v)) unname(unique(t(do.call(cbind, lapply(as.data.frame(t(cbind(1, as.matrix(do.call(expand.grid, lapply(dseq, function(i) c(-1,1))))))), "*", e2=do.call(cbind, lapply(rperms, function(ind) tmat[ind,]))))))) } lme4/R/plot.R0000644000176000001440000003773212204271665012466 0ustar ripleyusers## copied/modified from nlme splitFormula <- ## split, on the nm call, the rhs of a formula into a list of subformulas function(form, sep = "/") { if (inherits(form, "formula") || mode(form) == "call" && form[[1]] == as.name("~")) return(splitFormula(form[[length(form)]], sep = sep)) if (mode(form) == "call" && form[[1]] == as.name(sep)) return(do.call("c", lapply(as.list(form[-1]), splitFormula, sep = sep))) if (mode(form) == "(") return(splitFormula(form[[2]], sep = sep)) if (length(form) < 1) return(NULL) list(asOneSidedFormula(form)) } ## Recursive version of all.vars allVarsRec <- function(object) { if (is.list(object)) { unlist(lapply(object, allVarsRec)) } else { all.vars(object) } } ## crippled version of getData.gnls from nlme getData <- function(object) { mCall <- object@call data <- eval(mCall$data) if (is.null(data)) return(data) ## FIXME: deal with NAs, subset appropriately ## naPat <- eval(mCall$naPattern) ## if (!is.null(naPat)) { ## data <- data[eval(naPat[[2]], data), , drop = FALSE] ## } ## naAct <- eval(mCall$na.action) ## if (!is.null(naAct)) { ## data <- naAct(data) ## } ## subset <- mCall@subset ## if (!is.null(subset)) { ## subset <- eval(asOneSidedFormula(subset)[[2]], data) ## data <- data[subset, ] ## } return(data) } asOneFormula <- ## Constructs a linear formula with all the variables used in a ## list of formulas, except for the names in omit function(..., omit = c(".", "pi")) { names <- unique(allVarsRec(list(...))) names <- names[is.na(match(names, omit))] if (length(names)) as.formula(paste("~", paste(names, collapse = "+"))) # else NULL } getIDLabels <- function(object, form) { if (missing(form)) { grps <- names(getME(object,"flist")) } else { ## whitespace-stripped elements of formula grps <- gsub("(^ +| +$)","",strsplit(as.character(form)[[2]],"\\+")[[1]]) } if (grps==".obs") return(seq(fitted(object))) as.character(do.call(interaction,model.frame(object)[grps])) } ## Return the formula(s) for the groups associated with object. ## The result is a one-sided formula unless asList is TRUE in which case ## it is a list of formulas, one for each level. getGroupsFormula <- function(object, asList = FALSE, sep = "+") UseMethod("getGroupsFormula") getGroupsFormula.default <- ## Return the formula(s) for the groups associated with object. ## The result is a one-sided formula unless asList is TRUE in which case ## it is a list of formulas, one for each level. function(object, asList = FALSE, sep = "/") { form <- formula(object) if (!inherits(form, "formula")){ stop("\"Form\" argument must be a formula") } form <- form[[length(form)]] if (!((length(form) == 3) && (form[[1]] == as.name("|")))) { ## no conditioning expression return(NULL) } ## val <- list( asOneSidedFormula( form[[ 3 ]] ) ) val <- splitFormula(asOneSidedFormula(form[[3]]), sep = sep) names(val) <- unlist(lapply(val, function(el) deparse(el[[2]]))) # if (!missing(level)) { # if (length(level) == 1) { # return(val[[level]]) # } else { # val <- val[level] # } # } if (asList) as.list(val) else as.formula(paste("~", paste(names(val), collapse = sep))) } getGroupsFormula.merMod <- function(object,asList=FALSE, sep="+") { if (asList) { lapply(names(object@flist),asOneSidedFormula) } else { asOneSidedFormula(paste(names(object@flist),collapse=sep)) } } getCovariateFormula <- function (object) { form <- formula(object) if (!(inherits(form, "formula"))) { stop("formula(object) must return a formula") } form <- form[[length(form)]] if (length(form) == 3 && form[[1]] == as.name("|")) { form <- form[[2]] } eval(substitute(~form)) } getResponseFormula <- function(object) { ## Return the response formula as a one sided formula form <- formula(object) if (!(inherits(form, "formula") && (length(form) == 3))) { stop("\"Form\" must be a two sided formula") } as.formula(paste("~", deparse(form[[2]]))) } ##' diagnostic plots for merMod fits ##' @param x a fitted [ng]lmer model ##' @param form an optional formula specifying the desired type of plot. Any ##' variable present in the original data frame used to obtain ##' \code{x} can be referenced. In addition, \code{x} itself can be ##' referenced in the formula using the symbol \code{"."}. Conditional ##' expressions on the right of a \code{|} operator can be used to ##' define separate panels in a lattice display. Default is ##' \code{resid(., type = "pearson") ~ fitted(.)}, corresponding to a plot ##' of the standardized residuals versus fitted values. ##' @param abline an optional numeric value, or numeric vector of length ##' two. If given as a single value, a horizontal line will be added to the ##' plot at that coordinate; else, if given as a vector, its values are ##' used as the intercept and slope for a line added to the plot. If ##' missing, no lines are added to the plot. ##' @param id an optional numeric value, or one-sided formula. If given as ##' a value, it is used as a significance level for a two-sided outlier ##' test for the standardized, or normalized residuals. Observations with ##' absolute standardized (normalized) residuals greater than the \eqn{1-value/2} ##' quantile of the standard normal distribution are ##' identified in the plot using \code{idLabels}. If given as a one-sided ##' formula, its right hand side must evaluate to a logical, integer, or ##' character vector which is used to identify observations in the ##' plot. If missing, no observations are identified. ##' @param idLabels an optional vector, or one-sided formula. If given as a ##' vector, it is converted to character and used to label the ##' observations identified according to \code{id}. If given as a ##' vector, it is converted to character and used to label the ##' observations identified according to \code{id}. If given as a ##' one-sided formula, its right hand side must evaluate to a vector ##' which is converted to character and used to label the identified ##' observations. Default is the interaction of all the grouping variables ##' in the data frame. The special formula ##' @param grid an optional logical value indicating whether a grid should ##' be added to plot. Default depends on the type of lattice plot used: ##' if \code{xyplot} defaults to \code{TRUE}, else defaults to ##' \code{FALSE}. ##' @param \dots optional arguments passed to the lattice plot function. ##' @details Diagnostic plots for the linear mixed-effects fit are obtained. The ##' \code{form} argument gives considerable flexibility in the type of ##' plot specification. A conditioning expression (on the right side of a ##' \code{|} operator) always implies that different panels are used for ##' each level of the conditioning factor, according to a lattice ##' display. If \code{form} is a one-sided formula, histograms of the ##' variable on the right hand side of the formula, before a \code{|} ##' operator, are displayed (the lattice function \code{histogram} is ##' used). If \code{form} is two-sided and both its left and ##' right hand side variables are numeric, scatter plots are displayed ##' (the lattice function \code{xyplot} is used). Finally, if \code{form} ##' is two-sided and its left had side variable is a factor, box-plots of ##' the right hand side variable by the levels of the left hand side ##' variable are displayed (the lattice function \code{bwplot} is used). ##' @author original version in \code{nlme} package by Jose Pinheiro and Douglas Bates ##' @examples ##' data(Orthodont,package="nlme") ##' fm1 <- lmer(distance ~ age + (age|Subject), data=Orthodont) ##' ## standardized residuals versus fitted values by gender ##' plot(fm1, resid(., scaled=TRUE) ~ fitted(.) | Sex, abline = 0) ##' ## box-plots of residuals by Subject ##' plot(fm1, Subject ~ resid(., scaled=TRUE)) ##' ## observed versus fitted values by Subject ##' plot(fm1, distance ~ fitted(.) | Subject, abline = c(0,1)) ##' ## residuals by age, separated by Subject ##' plot(fm1, resid(., scaled=TRUE) ~ age | Sex, abline = 0) ##' if (require(ggplot2)) { ##' ## we can create the same plots using ggplot2 and the fortify() function ##' fm1F <- fortify(fm1) ##' ggplot(fm1F, aes(.fitted,.resid)) + geom_point(colour="blue") + ##' facet_grid(.~Sex) + geom_hline(yintercept=0) ##' ## note: Subjects are ordered by mean distance ##' ggplot(fm1F, aes(Subject,.resid)) + geom_boxplot() + coord_flip() ##' ggplot(fm1F, aes(.fitted,distance))+ geom_point(colour="blue") + ##' facet_wrap(~Subject) +geom_abline(intercept=0,slope=1) ##' ggplot(fm1F, aes(age,.resid)) + geom_point(colour="blue") + facet_grid(.~Sex) + ##' geom_hline(yintercept=0)+geom_line(aes(group=Subject),alpha=0.4)+geom_smooth(method="loess") ##' ## (warnings about loess are due to having only 4 unique x values) ##' detach("package:ggplot2") ##' } ##' @S3method plot merMod ##' @method plot merMod ##' @export plot.merMod <- function(x, form = resid(., type = "pearson") ~ fitted(.), abline, id = NULL, idLabels = NULL, grid, ...) ## Diagnostic plots based on residuals and/or fitted values { object <- x if (!inherits(form, "formula")) stop("\"form\" must be a formula") ## constructing data ## can I get away with using object@frame??? allV <- all.vars(asOneFormula(form, id, idLabels)) allV <- allV[is.na(match(allV,c("T","F","TRUE","FALSE")))] if (length(allV) > 0) { data <- getData(object) if (is.null(data)) { # try to construct data alist <- lapply(as.list(allV), as.name) names(alist) <- allV alist <- c(list(as.name("data.frame")), alist) mode(alist) <- "call" data <- eval(alist, sys.parent(1)) } else if (any(naV <- is.na(match(allV, names(data))))) stop(allV[naV], " not found in data") } else data <- NULL ## this won't do because there may well be variables we want ## that were not in the model call ## data <- object@frame ## argument list dots <- list(...) args <- if (length(dots) > 0) dots else list() ## appending object to data, and adding observation-number variable data <- as.list(c(as.list(cbind(data,.obs=seq(nrow(data)))), . = list(object))) ## covariate - must always be present covF <- getCovariateFormula(form) .x <- eval(covF[[2]], data) if (!is.numeric(.x)) { stop("Covariate must be numeric") } argForm <- ~ .x argData <- data.frame(.x = .x, check.names = FALSE) if (is.null(args$xlab)) { if (is.null(xlab <- attr(.x, "label"))) xlab <- deparse(covF[[2]]) args$xlab <- xlab } ## response - need not be present respF <- getResponseFormula(form) if (!is.null(respF)) { .y <- eval(respF[[2]], data) if (is.null(args$ylab)) { if (is.null(ylab <- attr(.y, "label"))) ylab <- deparse(respF[[2]]) args$ylab <- ylab } argForm <- .y ~ .x argData[, ".y"] <- .y } ## groups - need not be present grpsF <- getGroupsFormula(form) if (!is.null(grpsF)) { ## ?? FIXME ??? gr <- splitFormula(grpsF, sep = "*") for(i in seq_along(gr)) { auxGr <- all.vars(gr[[i]]) for(j in auxGr) argData[[j]] <- eval(as.name(j), data) } argForm <- as.formula(paste(if (length(argForm) == 2) "~ .x |" else ".y ~ .x |", deparse(grpsF[[2]]))) } ## adding to args list args <- c(list(argForm, data = argData), args) if (is.null(args$strip)) { args$strip <- function(...) strip.default(..., style = 1) } if (is.null(args$cex)) args$cex <- par("cex") if (is.null(args$adj)) args$adj <- par("adj") if (!is.null(id)) { ## identify points in plot idResType <- "pearson" ## diff from plot.lme: 'normalized' not available id <- switch(mode(id), numeric = { if (id <= 0 || id >= 1) stop("Id must be between 0 and 1") as.logical(abs(resid(object, type = idResType)) > -qnorm(id / 2)) }, call = eval(asOneSidedFormula(id)[[2]], data), stop("\"id\" can only be a formula or numeric.") ) if (is.null(idLabels)) { idLabels <- getIDLabels(object) } else { if (inherits(idLabels,"formula")) { idLabels <- getIDLabels(object,idLabels) } else if (is.vector(idLabels)) { if (length(idLabels <- unlist(idLabels)) != length(id)) { stop("\"idLabels\" of incorrect length") } } else stop("\"idLabels\" can only be a formula or a vector") } idLabels <- as.character(idLabels)[id] } ## defining abline, if needed if (missing(abline)) { abline <- if (missing(form)) # r ~ f c(0, 0) else NULL } #assign("id", id , where = 1) #assign("idLabels", idLabels, where = 1) #assign("abl", abline, where = 1) assign("abl", abline) ## defining the type of plot if (length(argForm) == 3) { if (is.numeric(.y)) { # xyplot plotFun <- "xyplot" if (is.null(args$panel)) { args <- c(args, panel = list(function(x, y, subscripts, ...) { x <- as.numeric(x) y <- as.numeric(y) dots <- list(...) if (grid) panel.grid() panel.xyplot(x, y, ...) if (any(ids <- id[subscripts])){ ltext(x[ids], y[ids], idLabels[subscripts][ids], cex = dots$cex, adj = dots$adj) } if (!is.null(abl)) { if (length(abl) == 2) panel.abline(a = abl, ...) else panel.abline(h = abl, ...) } })) } } else { # assume factor or character plotFun <- "bwplot" if (is.null(args$panel)) { args <- c(args, panel = list(function(x, y, ...) { if (grid) panel.grid() panel.bwplot(x, y, ...) if (!is.null(abl)) { panel.abline(v = abl[1], ...) } })) } } } else { plotFun <- "histogram" if (is.null(args$panel)) { args <- c(args, panel = list(function(x, ...) { if (grid) panel.grid() panel.histogram(x, ...) if (!is.null(abl)) { panel.abline(v = abl[1], ...) } })) } } ## defining grid if (missing(grid)) { grid <- (plotFun == "xyplot") } # assign("grid", grid, where = 1) do.call(plotFun, as.list(args)) } ##' add information to data based on a fitted model ##' @param model fitted model ##' @param data original data set, if needed ##' @param \dots additional arguments ##' @details \code{fortify} is a function defined in the \code{ggplot2} package, q.v. for more details; the ##' S3 generic is just defined here to avoid inducing an additional \code{Imports:} dependency. ##' This is currently an experimental feature. ##' @export fortify <- function(model, data, ...) UseMethod("fortify") ##' @rdname fortify ##' @S3method fortify lmerMod ##' @method fortify lmerMod ##' @export fortify.lmerMod <- function(model, data=getData(model), ...) { ## FIXME: ## FIXME: get influence measures via influence.ME? ## (expensive, induces dependency ...) ## FIXME: different kinds of residuals? ## FIXME: deal with na.omit/predict etc. data$.fitted <- predict(model) data$.resid <- resid(model) data$.scresid <- resid(model,type="pearson",scaled=TRUE) data } ## FIXME: can we do without this?? ## S3method fortify lmerMod ## S3method fortify glmerMod ## S3method fortify nlmerMod ## fortify.lmerMod <- fortify.nlmerMod <- fortify.glmerMod <- fortify.merMod ## autoplot??? ## plot method for plot.summary.mer ... coefplot-style ## horizontal, vertical? other options??? ## scale? plot.summary.mer <- function(object, type="fixef", ...) { if(any(!type %in% c("fixef","vcov"))) stop("'type' not yet implemented: ", type) stop("FIXME -- not yet implemented") } lme4/R/AllClass.R0000644000176000001440000014653112211706636013203 0ustar ripleyusers### Class definitions for the package ##' @useDynLib lme4 .registration=TRUE NULL ##' Class "lmList" of 'lm' Objects on Common Model ##' ##' Class \code{"lmList"} is an S4 class with basically a list of objects of ##' class \code{\link{lm}} with a common model. ##' @name lmList-class ##' @aliases lmList-class show,lmList-method ##' @docType class ##' @section Objects from the Class: Objects can be created by calls of the form ##' \code{new("lmList", ...)} or, more commonly, by a call to ##' \code{\link{lmList}}. ##' @keywords classes ##' @export setClass("lmList", representation(call = "call", pool = "logical"), contains = "list") ## TODO: export setClass("lmList.confint", contains = "array") ### FIXME ### shouldn't we have "merPred" with two *sub* classes "merPredD" and "merPredS" ### for the dense and sparse X cases ? ##' Generator object for the \code{\linkS4class{merPredD}} class ##' ##' The generator object for the \code{\linkS4class{merPredD}} reference class. ##' Such an object is primarily used through its \code{new} method. ##' ##' @param ... List of arguments (see Note). ##' @section Methods: ##' \describe{ ##' \item{new(X, Zt, Lambdat, Lind, theta, n):}{Create a new \code{\linkS4class{merPredD}} object} ##' } ##' @note Arguments to the \code{new} methods must be named arguments: ##' \itemize{ ##' \item{X}{ dense model matrix for the fixed-effects parameters, to be stored ##' in the \code{X} field.} ##' \item{Zt}{ transpose of the sparse model matrix for the random effects. It ##' is stored in the \code{Zt} field.} ##' \item{Lambdat}{ transpose of the sparse lower triangular relative variance ##' factor (stored in the \code{Lambdat} field).} ##' \item{Lind}{ integer vector of the same length as the \code{"x"} slot in the ##' \code{Lambdat} field. Its elements should be in the range 1 to the length ##' of the \code{theta} field.} ##' \item{theta}{ numeric vector of variance component parameters (stored in the ##' \code{theta} field).} ##' \item{n}{ sample size, usually \code{nrow(X)}.} ##' } ##' @seealso \code{\linkS4class{merPredD}} ##' @keywords classes ##' @export merPredD <- setRefClass("merPredD", # Predictor class for mixed-effects models with dense X fields = list(Lambdat = "dgCMatrix", LamtUt = "dgCMatrix", Lind = "integer", Ptr = "externalptr", RZX = "matrix", Ut = "dgCMatrix", Utr = "numeric", V = "matrix", VtV = "matrix", Vtr = "numeric", X = "matrix", Xwts = "numeric", Zt = "dgCMatrix", beta0 = "numeric", delb = "numeric", delu = "numeric", theta = "numeric", u0 = "numeric"), methods = list( initialize = function(X, Zt, Lambdat, Lind, theta, n, ...) { if (!nargs()) return X <<- as(X, "matrix") Zt <<- as(Zt, "dgCMatrix") Lambdat <<- as(Lambdat, "dgCMatrix") Lind <<- as.integer(Lind) theta <<- as.numeric(theta) N <- nrow(X) p <- ncol(X) q <- nrow(Zt) stopifnot(length(theta) > 0L, length(Lind) > 0L, all(sort(unique(Lind)) == seq_along(theta))) RZX <<- array(0, c(q, p)) Utr <<- numeric(q) V <<- array(0, c(n, p)) VtV <<- array(0, c(p, p)) Vtr <<- numeric(p) b0 <- list(...)$beta0 beta0 <<- if (is.null(b0)) numeric(p) else b0 delb <<- numeric(p) delu <<- numeric(q) uu <- list(...)$u0 u0 <<- if (is.null(uu)) numeric(q) else uu Ut <<- if (n == N) Zt + 0 else Zt %*% sparseMatrix(i=seq_len(N), j=as.integer(gl(n, 1, N)), x=rep.int(1,N)) ## The following is a kludge to overcome problems when Zt is square ## by making LamtUt rectangular LtUt <- Lambdat %*% Ut if (nrow(LtUt) == ncol(LtUt)) LtUt <- cbind2(LtUt, sparseMatrix(i=integer(0), j=integer(0), x=numeric(0), dims=c(nrow(LtUt),1))) LamtUt <<- LtUt Xw <- list(...)$Xwts Xwts <<- if (is.null(Xw)) rep.int(1, N) else as.numeric(Xw) initializePtr() }, CcNumer = function() { 'returns the numerator of the orthogonality convergence criterion' .Call(merPredDCcNumer, ptr()) }, L = function() { 'returns the current value of the sparse Cholesky factor' .Call(merPredDL, ptr()) }, P = function() { 'returns the permutation vector for the sparse Cholesky factor' .Call(merPredDPvec, ptr()) }, RX = function() { 'returns the dense downdated Cholesky factor for the fixed-effects parameters' .Call(merPredDRX, ptr()) }, RXi = function() { 'returns the inverse of the dense downdated Cholesky factor for the fixed-effects parameters' .Call(merPredDRXi, ptr()) }, RXdiag = function() { 'returns the diagonal of the dense downdated Cholesky factor' .Call(merPredDRXdiag, ptr()) }, b = function(fac) { 'random effects on original scale for step factor fac' .Call(merPredDb, ptr(), as.numeric(fac)) }, beta = function(fac) { 'fixed-effects coefficients for step factor fac' .Call(merPredDbeta, ptr(), as.numeric(fac)) }, copy = function(shallow = FALSE) { def <- .refClassDef selfEnv <- as.environment(.self) vEnv <- new.env(parent=emptyenv()) for (field in setdiff(names(def@fieldClasses), "Ptr")) { if (shallow) assign(field, get(field, envir = selfEnv), envir = vEnv) else { current <- get(field, envir = selfEnv) if (is(current, "envRefClass")) current <- current$copy(FALSE) assign(field, current, envir = vEnv) } } do.call(new, c(as.list(vEnv), n=nrow(vEnv$V), Class=def)) }, ldL2 = function() { 'twice the log determinant of the sparse Cholesky factor' .Call(merPredDldL2, ptr()) }, ldRX2 = function() { 'twice the log determinant of the downdated dense Cholesky factor' .Call(merPredDldRX2, ptr()) }, unsc = function() { 'the unscaled variance-covariance matrix of the fixed-effects parameters' .Call(merPredDunsc, ptr()) }, linPred = function(fac) { 'evaluate the linear predictor for step factor fac' .Call(merPredDlinPred, ptr(), as.numeric(fac)) }, installPars = function(fac) { 'update u0 and beta0 to the values for step factor fac' .Call(merPredDinstallPars, ptr(), as.numeric(fac)) }, initializePtr = function() { Ptr <<- .Call(merPredDCreate, as(X, "matrix"), Lambdat, LamtUt, Lind, RZX, Ut, Utr, V, VtV, Vtr, Xwts, Zt, beta0, delb, delu, theta, u0) .Call(merPredDsetTheta, Ptr, theta) .Call(merPredDupdateXwts, Ptr, Xwts) .Call(merPredDupdateDecomp, Ptr, NULL) }, ptr = function() { 'returns the external pointer, regenerating if necessary' if (length(theta)) { if (.Call(isNullExtPtr, Ptr)) initializePtr() } Ptr }, setBeta0 = function(beta0) { 'install a new value of theta' .Call(merPredDsetBeta0, ptr(), as.numeric(beta0)) }, setTheta = function(theta) { 'install a new value of theta' .Call(merPredDsetTheta, ptr(), as.numeric(theta)) }, solve = function() { 'solve for the coefficient increments delu and delb' .Call(merPredDsolve, ptr()) }, solveU = function() { 'solve for the coefficient increment delu only (beta is fixed)' .Call(merPredDsolveU, ptr()) }, setDelu = function(val) { 'set the coefficient increment delu' .Call(merPredDsetDelu , ptr(), as.numeric(val)) }, setDelb = function(val) { 'set the coefficient increment delb' .Call(merPredDsetDelb , ptr(), as.numeric(val)) }, sqrL = function(fac) { 'squared length of u0 + fac * delu' .Call(merPredDsqrL, ptr(), as.numeric(fac)) }, u = function(fac) { 'orthogonal random effects for step factor fac' .Call(merPredDu, ptr(), as.numeric(fac)) }, updateDecomp = function(XPenalty = NULL) { 'update L, RZX and RX from Ut, Vt and VtV' invisible(.Call(merPredDupdateDecomp, ptr(), XPenalty)) }, updateL = function() { 'update LamtUt and L' .Call(merPredDupdateL, ptr()) }, updateLamtUt = function() { 'update LamtUt and L' .Call(merPredDupdateLamtUt, ptr()) }, updateRes = function(wtres) { 'update Vtr and Utr using the vector of weighted residuals' .Call(merPredDupdateRes, ptr(), as.numeric(wtres)) }, updateXwts = function(wts) { 'update Ut and V from Zt and X using X weights' .Call(merPredDupdateXwts, ptr(), wts) } ) ) merPredD$lock("Lambdat", "LamtUt", "Lind", "RZX", "Ut", "Utr", "V", "VtV", "Vtr", "X", "Xwts", "Zt", "beta0", "delb", "delu", "theta", "u0") ##' Class \code{"merPredD"} - a dense predictor reference class ##' ##' A reference class for a mixed-effects model predictor module with a dense ##' model matrix for the fixed-effects parameters. The reference class is ##' associated with a C++ class of the same name. As is customary, the ##' generator object, \code{\link{merPredD}}, for the class has the same name as ##' the class. ##' @name merPredD-class ##' @note Objects from this reference class correspond to objects in a C++ ##' class. Methods are invoked on the C++ class object using the external ##' pointer in the \code{Ptr} field. When saving such an object the external ##' pointer is converted to a null pointer, which is why there are redundant ##' fields containing enough information as R objects to be able to regenerate ##' the C++ object. The convention is that a field whose name begins with an ##' upper-case letter is an R object and the corresponding field, whose name ##' begins with the lower-case letter is a method. References to the ##' external pointer should be through the method, not directly through the ##' \code{Ptr} field. ##' @section Extends: All reference classes extend and inherit methods from ##' \code{"\linkS4class{envRefClass}"}. ##' @seealso \code{\link{lmer}}, \code{\link{glmer}}, \code{\link{nlmer}}, ##' \code{\link{merPredD}}, \code{\linkS4class{merMod}}. ##' @keywords classes ##' @examples ##' ##' showClass("merPredD") ##' str(slot(lmer(Yield ~ 1|Batch, Dyestuff), "pp")) ##' NULL ##' Generator objects for the response classes ##' ##' The generator objects for the \code{\linkS4class{lmResp}}, ##' \code{\linkS4class{lmerResp}}, \code{\linkS4class{glmResp}} and ##' \code{\linkS4class{nlsResp}} reference classes. Such objects are ##' primarily used through their \code{new} methods. ##' ##' @aliases lmResp lmerResp glmResp nlsResp ##' @param ... List of arguments (see Note). ##' @note Arguments to the \code{new} methods must be named arguments. ##' \itemize{ ##' \item{y}{ the numeric response vector} ##' \item{family}{ a \code{\link{family}} object} ##' \item{nlmod}{ the nonlinear model function} ##' \item{nlenv}{ an environment holding data objects for evaluation of \code{nlmod}} ##' \item{pnames}{ a character vector of parameter names} ##' \item{gam}{ a numeric vector - the initial linear predictor} ##'} ##' @section Methods: ##' \describe{ ##' \item{\code{new(y=y)}:}{Create a new ##' \code{\linkS4class{lmResp}} or \code{\linkS4class{lmerResp}} object.} ##' \item{\code{new(family=family, y=y)}:}{Create a new ##' \code{\linkS4class{glmResp}} object.} ##' \item{\code{new(y=y, nlmod=nlmod, nlenv=nlenv, pnames=pnames, ##' gam=gam)}:}{Create a new ##' \code{\linkS4class{nlsResp}} object.} ##' } ##' @seealso \code{\linkS4class{lmResp}}, \code{\linkS4class{lmerResp}}, ##' \code{\linkS4class{glmResp}}, \code{\linkS4class{nlsResp}} ##' @keywords classes ##' @export lmResp <- # base class for response modules setRefClass("lmResp", fields = list(Ptr = "externalptr", mu = "numeric", offset = "numeric", sqrtXwt = "numeric", sqrtrwt = "numeric", weights = "numeric", wtres = "numeric", y = "numeric"), methods = list( allInfo = function() { 'return all the information available on the object' data.frame(y=y, offset=offset, weights=weights, mu=mu, rwt=sqrtrwt, wres=wtres, Xwt=sqrtXwt) }, initialize = function(...) { if (!nargs()) return() ll <- list(...) if (is.null(ll$y)) stop("y must be specified") y <<- as.numeric(ll$y) n <- length(y) mu <<- if (!is.null(ll$mu)) as.numeric(ll$mu) else numeric(n) offset <<- if (!is.null(ll$offset)) as.numeric(ll$offset) else numeric(n) weights <<- if (!is.null(ll$weights)) as.numeric(ll$weights) else rep.int(1,n) sqrtXwt <<- if (!is.null(ll$sqrtXwt)) as.numeric(ll$sqrtXwt) else sqrt(weights) sqrtrwt <<- if (!is.null(ll$sqrtrwt)) as.numeric(ll$sqrtrwt) else sqrt(weights) wtres <<- sqrtrwt * (y - mu) }, copy = function(shallow = FALSE) { def <- .refClassDef selfEnv <- as.environment(.self) vEnv <- new.env(parent=emptyenv()) for (field in setdiff(names(def@fieldClasses), "Ptr")) { if (shallow) assign(field, get(field, envir = selfEnv), envir = vEnv) else { current <- get(field, envir = selfEnv) if (is(current, "envRefClass")) current <- current$copy(FALSE) assign(field, current, envir = vEnv) } } do.call(new, c(as.list(vEnv), Class=def)) }, initializePtr = function() { Ptr <<- .Call(lm_Create, y, weights, offset, mu, sqrtXwt, sqrtrwt, wtres) .Call(lm_updateMu, Ptr, mu) }, ptr = function() { 'returns the external pointer, regenerating if necessary' if (length(y)) { if (.Call(isNullExtPtr, Ptr)) initializePtr() } Ptr }, setOffset = function(oo) { 'change the offset in the model (used in profiling)' .Call(lm_setOffset, ptr(), as.numeric(oo)) }, setResp = function(rr) { 'change the response in the model, usually after a deep copy' .Call(lm_setResp, ptr(), as.numeric(rr)) }, setWeights = function(ww) { 'change the prior weights in the model' .Call(lm_setWeights, ptr(), as.numeric(ww)) }, updateMu = function(gamma) { 'update mu, wtres and wrss from the linear predictor' .Call(lm_updateMu, ptr(), as.numeric(gamma)) }, wrss = function() { 'returns the weighted residual sum of squares' .Call(lm_wrss, ptr()) }) ) lmResp$lock("mu", "offset", "sqrtXwt", "sqrtrwt", "weights", "wtres")#, "y") ##' Classes \code{"lmResp"}, \code{"glmResp"}, \code{"nlsResp"} and ##' \code{"lmerResp"} ##' ##' Reference classes for response modules, including linear models, ##' \code{"lmResp"}, generalized linear models, \code{"glmResp"}, nonlinear ##' models, \code{"nlsResp"} and linear mixed-effects models, \code{"lmerResp"}. ##' Each reference class is associated with a C++ class of the same name. As is ##' customary, the generator object for each class has the same name as the ##' class. ##' @name lmResp-class ##' @aliases lmResp-class glmResp-class lmerResp-class nlsResp-class ##' @note Objects from these reference classes correspond to objects in C++ ##' classes. Methods are invoked on the C++ classes using the external pointer ##' in the \code{ptr} field. When saving such an object the external pointer is ##' converted to a null pointer, which is why there are redundant fields ##' containing enough information as R objects to be able to regenerate the C++ ##' object. The convention is that a field whose name begins with an upper-case ##' letter is an R object and the corresponding field whose name begins with the ##' lower-case letter is a method. Access to the external pointer should be ##' through the method, not through the field. ##' @section Extends: All reference classes extend and inherit methods from ##' \code{"\linkS4class{envRefClass}"}. Furthermore, \code{"glmResp"}, ##' \code{"nlsResp"} and \code{"lmerResp"} all extend the \code{"lmResp"} class. ##' @seealso \code{\link{lmer}}, \code{\link{glmer}}, \code{\link{nlmer}}, ##' \code{\linkS4class{merMod}}. ##' @examples ##' ##' showClass("lmResp") ##' str(lmResp$new(y=1:4)) ##' showClass("glmResp") ##' str(glmResp$new(family=poisson(), y=1:4)) ##' showClass("nlsResp") ##' showClass("lmerResp") ##' str(lmerResp$new(y=1:4)) ##' @keywords classes NULL ##' @export lmerResp <- setRefClass("lmerResp", fields= list(REML="integer"), contains="lmResp", methods= list(initialize = function(...) { REML <<- as.integer(list(...)$REML) if (length(REML) != 1L) REML <<- 0L callSuper(...) }, initializePtr = function() { Ptr <<- .Call(lmer_Create, y, weights, offset, mu, sqrtXwt, sqrtrwt, wtres) .Call(lm_updateMu, Ptr, mu - offset) .Call(lmer_setREML, Ptr, REML) }, ptr = function() { 'returns the external pointer, regenerating if necessary' if (length(y)) if (.Call(isNullExtPtr, Ptr)) initializePtr() Ptr }, objective = function(ldL2, ldRX2, sqrL, sigma.sq = NULL) { 'returns the profiled deviance or REML criterion' .Call(lmer_Laplace, ptr(), ldL2, ldRX2, sqrL, sigma.sq) }) ) setOldClass("family") ##' @export glmResp <- setRefClass("glmResp", fields= list(eta="numeric", family="family", n="numeric"), contains="lmResp", methods= list(initialize = function(...) { callSuper(...) ll <- list(...) if (is.null(ll$family)) stop("family must be specified") family <<- ll$family n <<- if (!is.null(ll$n)) as.numeric(ll$n) else rep.int(1,length(y)) eta <<- numeric(length(y)) }, aic = function() { .Call(glm_aic, ptr()) }, allInfo = function() { 'return all the information available on the object' cbind(callSuper(), data.frame(eta=eta, muEta=muEta(), var=variance(), WrkWt=sqrtWrkWt(), wrkRes=wrkResids(), wrkResp=wrkResp(), devRes=devResid())) }, devResid = function() { 'returns the vector of deviance residuals' .Call(glm_devResid, ptr()) }, fam = function() { 'returns the name of the glm family' .Call(glm_family, ptr()) }, Laplace = function(ldL2, ldRX2, sqrL) { 'returns the Laplace approximation to the profiled deviance' .Call(glm_Laplace, ptr(), ldL2, ldRX2, sqrL) }, link = function() { 'returns the name of the glm link' .Call(glm_link, ptr()) }, muEta = function() { 'returns the diagonal of the Jacobian matrix, d mu/d eta' .Call(glm_muEta, ptr()) }, ptr = function() { 'returns the external pointer, regenerating if necessary' if (length(y)) { if (.Call(isNullExtPtr, Ptr)) { Ptr <<- .Call(glm_Create, family, y, weights, offset, mu, sqrtXwt, sqrtrwt, wtres, eta, n) .Call(glm_updateMu, Ptr, eta - offset) } } Ptr }, resDev = function() { 'returns the sum of the deviance residuals' .Call(glm_resDev, ptr()) }, setTheta = function(theta) { 'sets a new value of theta, for negative binomial distribution only' .Call(glm_setTheta, ptr(), as.numeric(theta)) }, sqrtWrkWt = function() { 'returns the square root of the working X weights' .Call(glm_sqrtWrkWt, ptr()) }, theta = function() { 'query the value of theta, for negative binomial distribution only' .Call(glm_theta, ptr()) }, updateMu = function(gamma) { 'update mu, residuals, weights, etc. from the linear predictor' .Call(glm_updateMu, ptr(), as.numeric(gamma)) }, updateWts = function() { 'update the residual and X weights from the current value of eta' .Call(glm_updateWts, ptr()) }, variance = function() { 'returns the vector of variances' .Call(glm_variance, ptr()) }, wtWrkResp = function() { 'returns the vector of weighted working responses' .Call(glm_wtWrkResp, ptr()) }, wrkResids = function() { 'returns the vector of working residuals' .Call(glm_wrkResids, ptr()) }, wrkResp = function() { 'returns the vector of working responses' .Call(glm_wrkResp, ptr()) } ) ) glmResp$lock("family", "n", "eta") ##' @export nlsResp <- setRefClass("nlsResp", fields= list(gam="numeric", nlmod="formula", nlenv="environment", pnames="character" ), contains="lmResp", methods= list(initialize = function(...) { callSuper(...) ll <- list(...) if (is.null(ll$nlmod)) stop("nlmod must be specified") nlmod <<- ll$nlmod if (is.null(ll$nlenv)) stop("nlenv must be specified") nlenv <<- ll$nlenv if (is.null(ll$pnames)) stop("pnames must be specified") pnames <<- ll$pnames if (is.null(ll$gam)) stop("gam must be specified") stopifnot(length(ll$gam) == length(offset)) gam <<- ll$gam }, Laplace =function(ldL2, ldRX2, sqrL) { 'returns the profiled deviance or REML criterion' .Call(nls_Laplace, ptr(), ldL2, ldRX2, sqrL) }, ptr = function() { 'returns the external pointer, regenerating if necessary' if (length(y)) { if (.Call(isNullExtPtr, Ptr)) { Ptr <<- .Call(nls_Create, y, weights, offset, mu, sqrtXwt, sqrtrwt, wtres, gam, nlmod[[2]], nlenv, pnames) .Call(nls_updateMu, Ptr, gam) } } Ptr }, updateMu=function(gamma) { 'update mu, residuals, gradient, etc. given the linear predictor matrix' .Call(nls_updateMu, ptr(), as.numeric(gamma)) }) ) nlsResp$lock("nlmod", "nlenv", "pnames") ##' Generator object for the \code{\linkS4class{glmFamily}} class ##' ##' The generator object for the \code{\linkS4class{glmFamily}} reference class. ##' Such an object is primarily used through its \code{new} method. ##' ##' ##' @param ... Named argument (see Note below) ##' @note Arguments to the \code{new} method must be named arguments. ##' @section Methods: \describe{ ##' \item{\code{new(family=family)}}{Create a new ##' \code{\linkS4class{glmFamily}} object} ##' } ##' @seealso \code{\linkS4class{glmFamily}} ##' @keywords classes ##' @export glmFamily <- # used in tests of family definitions setRefClass("glmFamily", fields=list(Ptr="externalptr", family="family"), methods= list( aic = function(y, n, mu, wt, dev) { 'returns the value from the aic member function, which is actually the deviance' nn <- length(y <- as.numeric(y)) stopifnot(length(n <- as.numeric(n)) == nn, length(mu <- as.numeric(mu)) == nn, length(wt <- as.numeric(wt)) == nn, all(wt >= 0), length(dev <- as.numeric(dev)) == 1L) .Call(glmFamily_aic, ptr(), y, n, mu, wt, dev) }, devResid = function(y, mu, wt) { 'applies the devResid function to y, mu and wt' mu <- as.numeric(mu) wt <- as.numeric(wt) y <- as.numeric(y) stopifnot(length(mu) == length(wt), length(mu) == length(y), all(wt >= 0)) .Call(glmFamily_devResid, ptr(), y, mu, wt) }, link = function(mu) { 'applies the (forward) link function to mu' .Call(glmFamily_link, ptr(), as.numeric(mu)) }, linkInv = function(eta) { 'applies the inverse link function to eta' .Call(glmFamily_linkInv, ptr(), as.numeric(eta)) }, muEta = function(eta) { 'applies the muEta function to eta' .Call(glmFamily_muEta, ptr(), as.numeric(eta)) }, ptr = function() { if (length(family)) if (.Call(isNullExtPtr, Ptr)) Ptr <<- .Call(glmFamily_Create, family) Ptr }, setTheta = function(theta) { 'sets a new value of theta, for negative binomial distribution only' .Call(glmFamily_setTheta, ptr(), as.numeric(theta)) }, theta = function() { 'query the value of theta, for negative binomial distribution only' .Call(glmFamily_theta, ptr()) }, variance = function(mu) { 'applies the variance function to mu' .Call(glmFamily_variance, ptr(), as.numeric(mu)) }) ) ##' Class \code{"glmFamily"} - a reference class for \code{\link{family}} ##' ##' This class is a wrapper class for \code{\link{family}} objects specifying a ##' distibution family and link function for a generalized linear model ##' (\code{\link{glm}}). The reference class contains an external pointer to a ##' C++ object representing the class. For common families and link functions ##' the functions in the family are implemented in compiled code so they can be ##' accessed from other compiled code and for a speed boost. ##' ##' ##' @name glmFamily-class ##' @docType class ##' @note Objects from this reference class correspond to objects in a C++ ##' class. Methods are invoked on the C++ class using the external pointer in ##' the \code{Ptr} field. When saving such an object the external pointer is ##' converted to a null pointer, which is why there is a redundant field ##' \code{ptr} that is an active-binding function returning the external ##' pointer. If the \code{Ptr} field is a null pointer, the external pointer is ##' regenerated for the stored \code{family} field. ##' @section Extends: All reference classes extend and inherit methods from ##' \code{"\linkS4class{envRefClass}"}. ##' @seealso \code{\link{family}}, \code{\link{glmFamily}} ##' @keywords classes ##' @examples ##' ##' str(glmFamily$new(family=poisson())) NULL ##' Generator object for the golden search optimizer class. ##' ##' The generator objects for the \code{\linkS4class{golden}} class of a scalar ##' optimizer for a parameter within an interval. The optimizer uses reverse ##' communications. ##' ##' @param \dots additional, optional arguments. None are used at present. ##' @note Arguments to the \code{new} methods must be named arguments. ##' \code{lower} and \code{upper} are the bounds for the scalar parameter; they must be finite. ##' @section Methods: ##' \describe{ ##' \item{\code{new(lower=lower, upper=upper)}}{Create a new ##' \code{\linkS4class{golden}} object.} ##' } ##' @seealso \code{\linkS4class{golden}} ##' @keywords classes ##' @export golden <- setRefClass("golden", # Reverse communication implementation of Golden Search fields = list( Ptr = "externalptr", lowerbd = "numeric", upperbd = "numeric" ), methods = list( initialize = function(lower, upper, ...) { stopifnot(length(lower <- as.numeric(lower)) == 1L, length(upper <- as.numeric(upper)) == 1L, lower > -Inf, upper < Inf, lower < upper) lowerbd <<- lower upperbd <<- upper Ptr <<- .Call(golden_Create, lower, upper) }, ptr = function() { if (length(lowerbd)) if (.Call(isNullExtPtr, Ptr)) Ptr <<- .Call(golden_Create, lowerbd, upperbd) Ptr }, newf = function(value) { stopifnot(length(value <- as.numeric(value)) == 1L) .Call(golden_newf, ptr(), value) }, value = function() .Call(golden_value, ptr()), xeval = function() .Call(golden_xeval, ptr()), xpos = function() .Call(golden_xpos, ptr()) ) ) ##' Class \code{"golden"} ##' ##' A reference class for a golden search scalar optimizer using reverse ##' communication. ##' ##' ##' @name golden-class ##' @docType class ##' @section Extends: All reference classes extend and inherit methods from ##' \code{"\linkS4class{envRefClass}"}. ##' @keywords classes ##' @examples ##' ##' showClass("golden") ##' NULL ##' Generator object for the Nelder-Mead optimizer class. ##' ##' The generator objects for the \code{\linkS4class{NelderMead}} class of ##' optimizers subject to box constraints and using reverse communications. ##' ##' @rdname NelderMead ##' @param \dots Argument list (see Note below). ##' @section Methods: ##' \describe{\code{NelderMead$new(lower, upper, xst, x0, xt)}}{Create a new ##' \code{\linkS4class{NelderMead}} object} ##' @seealso \code{\linkS4class{NelderMead}} ##' @note Arguments to the \code{new} method must be named arguments: ##' \describe{ ##' \item{lower}{numeric vector of lower bounds - elements may be \code{-Inf}.} ##' \item{upper}{numeric vector of upper bounds - elements may be \code{Inf}.} ##' \item{xst}{numeric vector of initial step sizes to establish the simplex - ##' all elements must be non-zero.} ##' \item{x0}{numeric vector of starting values for the parameters.} ##' \item{xt}{numeric vector of tolerances on the parameters.} ##' } ##' @keywords classes ##' @export NelderMead <- setRefClass("NelderMead", # Reverse communication implementation of Nelder-Mead simplex optimizer fields = list( Ptr = "externalptr", lowerbd = "numeric", upperbd = "numeric", xstep = "numeric", xtol = "numeric" ), methods = list( initialize = function(lower, upper, xst, x0, xt, ...) { stopifnot((n <- length(lower <- as.numeric(lower))) > 0L, length(upper <- as.numeric(upper)) == n, all(lower < upper), length(xst <- as.numeric(xst)) == n, all(xst != 0), length(x0 <- as.numeric(x0)) == n, all(x0 >= lower), all(x0 <= upper), all(is.finite(x0)), length(xt <- as.numeric(xt)) == n, all(xt > 0)) lowerbd <<- lower upperbd <<- upper xstep <<- xst xtol <<- xt Ptr <<- .Call(NelderMead_Create, lowerbd, upperbd, xstep, x0, xtol) }, ptr = function() { if (length(lowerbd)) if (.Call(isNullExtPtr, Ptr)) Ptr <<- .Call(NelderMead_Create, lowerbd, upperbd, xstep, x0, xtol) Ptr }, newf = function(value) { stopifnot(length(value <- as.numeric(value)) == 1L) .Call(NelderMead_newf, ptr(), value) }, setForceStop = function(stp=TRUE) .Call(NelderMead_setForce_stop, ptr(), stp), setFtolAbs = function(fta) .Call(NelderMead_setFtol_abs, ptr(), fta), setFtolRel = function(ftr) .Call(NelderMead_setFtol_rel, ptr(), ftr), setMaxeval = function(mxev) .Call(NelderMead_setMaxeval, ptr(), mxev), setMinfMax = function(minf) .Call(NelderMead_setMinf_max, ptr(), minf), setIprint = function(iprint) .Call(NelderMead_setIprint, ptr(), iprint), value = function() .Call(NelderMead_value, ptr()), xeval = function() .Call(NelderMead_xeval, ptr()), xpos = function() .Call(NelderMead_xpos, ptr()) ) ) ##' Class \code{"NelderMead"} ##' ##' A reference class for a Nelder-Mead simplex optimizer allowing box ##' constraints on the parameters and using reverse communication. ##' ##' @docType class ##' @name NelderMead-class ##' @note This is the default optimizer for the second stage of ##' \code{\link{glmer}} and \code{\link{nlmer}} fits. We found that it was more ##' reliable and often faster than more sophisticated optimizers. ##' @section Extends: All reference classes extend and inherit methods from ##' \code{"\linkS4class{envRefClass}"}. ##' @seealso \code{\link{glmer}}, \code{\link{nlmer}} ##' @references Based on code in the NLopt collection. ##' @keywords classes ##' @examples ##' ##' showClass("NelderMead") NULL ##' Class "merMod" of Fitted Mixed-Effect Models ##' ##' A mixed-effects model is represented as a \code{\linkS4class{merPredD}} object ##' and a response module of a class that inherits from class ##' \code{\linkS4class{lmResp}}. A model with a \code{\linkS4class{lmerResp}} ##' response has class \code{lmerMod}; a \code{\linkS4class{glmResp}} response ##' has class \code{glmerMod}; and a \code{\linkS4class{nlsResp}} response has ##' class \code{nlmerMod}. ##' ##' @name merMod-class ##' @aliases merMod-class lmerMod-class glmerMod-class nlmerMod-class merMod ##' show,merMod-method ##' anova.merMod coef.merMod deviance.merMod ##' fitted.merMod formula.merMod logLik.merMod ##' model.frame.merMod model.matrix.merMod print.merMod ##' show.merMod summary.merMod ##' terms.merMod update.merMod ##' vcov.merMod print.summary.merMod show.summary.merMod ##' summary.summary.merMod vcov.summary.merMod ##' @docType class ##' @section Objects from the Class: Objects are created by calls to ##' \code{\link{lmer}}, \code{\link{glmer}} or \code{\link{nlmer}}. ##' @seealso \code{\link{lmer}}, \code{\link{glmer}}, \code{\link{nlmer}}, ##' \code{\linkS4class{merPredD}}, \code{\linkS4class{lmerResp}}, ##' \code{\linkS4class{glmResp}}, \code{\linkS4class{nlsResp}} ##' @keywords classes ##' @examples ##' ##' showClass("merMod") ##' methods(class="merMod") ##' @export setClass("merMod", representation(Gp = "integer", call = "call", frame = "data.frame", # "model.frame" is not S4-ized yet flist = "list", cnms = "list", lower = "numeric", theta = "numeric", beta = "numeric", u = "numeric", devcomp = "list", pp = "merPredD", optinfo = "list")) ##' @export setClass("lmerMod", representation(resp="lmerResp"), contains="merMod") ##' @export setClass("glmerMod", representation(resp="glmResp"), contains="merMod") ##' @export setClass("nlmerMod", representation(resp="nlsResp"), contains="merMod") ##' Generator object for the rePos (random-effects positions) class ##' ##' The generator object for the \code{\linkS4class{rePos}} class used ##' to determine the positions and orders of random effects associated ##' with particular random-effects terms in the model. ##' @param \dots Argument list (see Note). ##' @note Arguments to the \code{new} methods must be named arguments. ##' \code{mer}, an object of class \code{"\linkS4class{merMod}"}, is ##' the only required/expected argument. ##' @section Methods: ##' \describe{ ##' \item{\code{new(mer=mer)}}{Create a new ##' \code{\linkS4class{rePos}} object.} ##' } ##' @seealso \code{\linkS4class{rePos}} ##' @keywords classes ##' @export rePos <- setRefClass("rePos", fields = list( cnms = "list", flist = "list", ncols = "integer", nctot = "integer", nlevs = "integer", offsets = "integer", terms = "list" ), methods = list( initialize = function(mer, ...) { stopifnot((ntrms <- length(Cnms <- mer@cnms)) > 0L, (length(Flist <- mer@flist)) > 0L, length(asgn <- as.integer(attr(Flist, "assign"))) == ntrms) cnms <<- Cnms flist <<- Flist ncols <<- unname(vapply(cnms, length, 0L)) nctot <<- unname(as.vector(tapply(ncols, asgn, sum))) nlevs <<- unname(vapply(flist, function(el) length(levels(el)), 0L)) offsets <<- c(0L, cumsum(sapply(seq_along(asgn), function(i) ncols[i] * nlevs[asgn[i]]))) terms <<- lapply(seq_along(flist), function(i) which(asgn == i)) } ) ) ##' Class \code{"rePos"} ##' ##' A reference class for determining the positions in the random-effects vector ##' that correspond to particular random-effects terms in the model formula ##' ##' @name rePos-class ##' @docType class ##' @section Extends: All reference classes extend and inherit methods from ##' \code{"\linkS4class{envRefClass}"}. ##' @keywords classes ##' @examples ##' ##' showClass("rePos") ##' rePos$lock("cnms", "flist", "ncols", "nctot", "nlevs", "terms") vcRep <- setRefClass("vcRep", fields = list( theta = "numeric", lower = "numeric", Lambdat = "dgCMatrix", Lind = "integer", Gp = "integer", flist = "list", cnms = "list", ncols = "integer", nctot = "integer", nlevs = "integer", offsets = "integer", terms = "list", sig = "numeric", nms = "character", covar = "list", useSc = "logical" ), methods = list( initialize = function(mer, ...) { stopifnot((ntrms <- length(Cnms <- mer@cnms)) > 0L, (length(Flist <- mer@flist)) > 0L, length(asgn <- as.integer(attr(Flist, "assign"))) == ntrms) lower <<- getME(mer, "lower") theta <<- getME(mer, "theta") Lambdat <<- getME(mer, "Lambdat") Lind <<- getME(mer, "Lind") Gp <<- getME(mer, "Gp") cnms <<- Cnms flist <<- Flist ncols <<- unname(vapply(cnms, length, 0L)) nctot <<- unname(as.vector(tapply(ncols, asgn, sum))) nlevs <<- unname(vapply(flist, function(el) length(levels(el)), 0L)) offsets <<- c(0L, cumsum(sapply(seq_along(asgn), function(i) ncols[i] * nlevs[asgn[i]]))) terms <<- lapply(seq_along(Flist), function(i) which(asgn == i)) sig <<- sigma(mer) nms <<- names(Flist)[asgn] covar <<- mkVarCorr(sig, cnms, ncols, theta, nms) useSc <<- as.logical(getME(mer, "devcomp")$dims['useSc']) }, asCovar = function() { ans <- lapply(covar, function(x) { attr(x, "correlation") <- attr(x, "stddev") <- NULL x }) attr(ans, "residVar") <- attr(covar, "sc")^2 ans }, asCorr = function() { ans <- lapply(covar, function(x) list(correlation=attr(x, "correlation"), stddev=attr(x, "stddev"))) attr(ans, "residSD") <- attr(covar, "sc") ans }, setTheta = function(ntheta) { stopifnot(length(ntheta <- as.numeric(ntheta)) == length(lower), all(ntheta >= lower)) theta <<- ntheta covar <<- mkVarCorr(sig, cnms, ncols, theta, nms) }, setSc = function(nSc) { stopifnot(useSc, length(nSc <- as.numeric(nSc)) == 1L) sig <<- nSc covar <<- mkVarCorr(sig, cnms, ncols, theta, nms) }, setResidVar = function(nVar) setSc(sqrt(as.numeric(nVar))), setRECovar = function(CV) { if (is.matrix(CV) && length(covar) == 1L) { CV <- list(CV) names(CV) <- names(covar) } covsiz <- sapply(covar, ncol) stopifnot(is.list(CV), all(names(CV) == names(covar)), all(sapply(CV, isSymmetric)), all(sapply(CV, ncol) == covsiz)) if (!all(vapply(cnms, length, 1L) == covsiz)) error("setRECovar currently requires distinct grouping factors") theta <<- sapply(CV, function(mm) { ff <- t(chol(mm))/sig ff[upper.tri(ff, diag=TRUE)] }) }) ) lme4/R/predict.R0000644000176000001440000002034312204271665013130 0ustar ripleyusers##' \code{\link{predict}} method for \code{\linkS4class{merMod}} objects ##' ##' @title Predictions from a model at new data values ##' @param object a fitted model object ##' @param newdata data frame for which to evaluate predictions ##' @param REform formula for random effects to include. If NULL, ##' include all random effects; if NA, include no random effects ##' @param terms a \code{\link{terms}} object - not used at present ##' @param type character string - either \code{"link"}, the default, ##' or \code{"response"} indicating the type of prediction object returned ##' @param allow.new.levels (logical) if FALSE (default), then any new levels ##' (or NA values) detected in \code{newdata} will trigger an error; if TRUE, then ##' the prediction will use the unconditional (population-level) ##' values for data with previously unobserved levels (or NAs) ##' @param na.action function determining what should be done with missing values for fixed effects in \code{newdata}. The default is to predict \code{NA}: see \code{\link{na.pass}}. ##' @param ... optional additional parameters. None are used at present. ##' @return a numeric vector of predicted values ##' @note There is no option for computing standard errors of predictions because it is difficult to define an efficient method that incorporates uncertainty in the variance parameters; we recommend \code{\link{bootMer}} for this task. ##' @examples ##' (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 |herd), cbpp, binomial)) ##' str(p0 <- predict(gm1)) # fitted values ##' str(p1 <- predict(gm1,REform=NA)) # fitted values, unconditional (level-0) ##' newdata <- with(cbpp, expand.grid(period=unique(period), herd=unique(herd))) ##' str(p2 <- predict(gm1,newdata)) # new data, all RE ##' str(p3 <- predict(gm1,newdata,REform=NA)) # new data, level-0 ##' str(p4 <- predict(gm1,newdata,REform=~(1|herd))) # explicitly specify RE ##' @method predict merMod ##' @export predict.merMod <- function(object, newdata=NULL, REform=NULL, terms=NULL, type=c("link","response"), allow.new.levels=FALSE, na.action=na.pass, ...) { ## FIXME: appropriate names for result vector? ## FIXME: make sure behaviour is entirely well-defined for NA in grouping factors if (length(list(...)>0)) warning("unused arguments ignored") if (isLMM(object) && !missing(type)) warning("type argument ignored for linear mixed models") fit.na.action <- attr(object@frame,"na.action") type <- match.arg(type) if (!is.null(terms)) stop("terms functionality for predict not yet implemented") ## FIXME/WARNING: how do we/can we do this in an eval-safe way??? form_orig <- formula(object) if (is.null(newdata) && is.null(REform)) { ## raw predict() call, just return fitted values (inverse-link if appropriate) if (isLMM(object) || isNLMM(object)) { pred <- fitted(object) } else { ## inverse-link pred <- switch(type,response=object@resp$mu, ## fitted(object), link=object@resp$eta) ## fixme: getME() ? } if (!is.null(fit.na.action)) { pred <- napredict(fit.na.action,pred) } return(pred) } else { ## newdata and/or REform specified X_orig <- getME(object, "X") if (is.null(newdata)) { X <- X_orig } else { ## evaluate new fixed effect RHS <- formula(object,fixed.only=TRUE)[-2] Terms <- terms(object,fixed.only=TRUE) X <- model.matrix(RHS, mfnew <- model.frame(delete.response(Terms), newdata, na.action=na.action), contrasts.arg=attr(X_orig,"contrasts")) } pred <- drop(X %*% fixef(object)) ## modified from predict.glm ... offset <- rep(0, nrow(X)) tt <- terms(object) ## FIXME:: need to unname() ? if (!is.null(off.num <- attr(tt, "offset"))) { for (i in off.num) offset <- offset + eval(attr(tt,"variables")[[i + 1]], newdata) } ## FIXME: is this redundant?? if (!is.null(frOffset <- attr(object@frame,"offset"))) offset <- offset + eval(frOffset, newdata) pred <- pred+offset if (is.null(REform)) { REform <- form_orig[-2] } ## FIXME: ??? can't apply is.na() to a 'language' object? ## what's the appropriate test? if (is.language(REform)) { na.action.name <- deparse(match.call()$na.action) ## ugh if (!is.null(newdata) && na.action.name %in% c("na.exclude","na.omit")) { ## strip NAs from data for random-effects matrix construction if (length(nadrop <- attr(mfnew,"na.action"))>0) { newdata <- newdata[-nadrop,] } } re <- ranef(object) ## ok? -- newdata used even though it was just tested for null if(is.null(newdata)) rfd <- object@frame else rfd <- newdata # get data for REform ReTrms <- mkReTrms(findbars(REform[[2]]), rfd) if (!allow.new.levels && any(sapply(ReTrms$flist,function(x) any(is.na(x))))) stop("NAs are not allowed in prediction data for grouping variables unless allow.new.levels is TRUE") unames <- unique(sort(names(ReTrms$cnms))) ## FIXME: same as names(ReTrms$flist) ? ## convert numeric grouping variables to factors as necessary for (i in all.vars(REform[[2]])) { newdata[[i]] <- factor(newdata[[i]]) } Rfacs <- setNames(lapply(unames,function(x) factor(eval(parse(text=x),envir=newdata))), unames) new_levels <- lapply(Rfacs,function(x) levels(droplevels(factor(x)))) ## FIXME: should this be unique(as.character(x)) instead? ## (i.e., what is the proper way to protect against non-factors?) levelfun <- function(x,n) { ## find and deal with new levels if (any(!new_levels[[n]] %in% rownames(x))) { if (!allow.new.levels) stop("new levels detected in newdata") ## create an all-zero data frame corresponding to the new set of levels ... newx <- as.data.frame(matrix(0,nrow=length(new_levels[[n]]),ncol=ncol(x), dimnames=list(new_levels[[n]],names(x)))) ## then paste in the matching RE values from the original fit/set of levels newx[rownames(x),] <- x x <- newx } ## find and deal with missing old levels if (any(!rownames(x) %in% new_levels[[n]])) { x <- x[rownames(x) %in% new_levels[[n]],,drop=FALSE] } x } ## fill in/delete levels as appropriate re_x <- mapply(levelfun,re,names(re),SIMPLIFY=FALSE) re_new <- list() if (any(!names(ReTrms$cnms) %in% names(re))) stop("grouping factors specified in REform that were not present in original model") ## pick out random effects values that correspond to ## random effects incorporated in REform ... for (i in seq_along(ReTrms$cnms)) { rname <- names(ReTrms$cnms)[i] if (any(!ReTrms$cnms[[rname]] %in% names(re[[rname]]))) stop("random effects specified in REform that were not present in original model") re_new[[i]] <- re_x[[rname]][,ReTrms$cnms[[rname]]] } re_newvec <- unlist(lapply(re_new,t)) ## must TRANSPOSE RE matrices before unlisting if(!is.null(newdata)) pred <- pred + drop(as.matrix(re_newvec %*% ReTrms$Zt)) } ## predictions with REform!=NA if (isGLMM(object) && type=="response") { pred <- object@resp$family$linkinv(pred) } ## fill in NAs as appropriate if (is.null(newdata) && !is.null(fit.na.action)) { pred <- napredict(fit.na.action,pred) } else { pred <- napredict(na.action,pred) } return(pred) } } lme4/R/mcmcsamp.R0000644000176000001440000000652712232467515013310 0ustar ripleyusers##' @name pvalues ##' @aliases mcmcsamp ##' @title Getting p-values for fitted models ##' ##' @description One of the most frequently asked questions about \code{lme4} ##' is "how do I calculate p-values for estimated parameters?" ##' Previous versions of \code{lme4} provided the \code{mcmcsamp} ##' function, which efficiently generated a Markov chain Monte Carlo sample ##' from the posterior distribution of the parameters, assuming ##' flat (scaled likelihood) priors. Due to difficulty in ##' constructing a version of \code{mcmcsamp} that was reliable ##' even in cases where the estimated random effect variances were near ##' zero (e.g. \url{https://stat.ethz.ch/pipermail/r-sig-mixed-models/2009q4/003115.html}), \code{mcmcsamp} has been withdrawn (or more precisely, ##' not updated to work with \code{lme4} versions >=1.0.0). ##' ##' Many users, including users of the \code{aovlmer.fnc} function ##' from the \code{languageR} package which relies on \code{mcmcsamp}, ##' will be deeply disappointed by this lacuna. Users who need p-values have ##' a variety of options: ##' \itemize{ ##' \item likelihood ratio tests via \code{anova} (MC,+) ##' \item profile confidence intervals via \code{\link{profile.merMod}} and \code{\link{confint.merMod}} (CI,+) ##' \item parametric bootstrap confidence intervals and model comparisons via \code{\link{bootMer}} (or \code{PBmodcomp} in the \code{pbkrtest} package) (MC/CI,*,+) ##' \item for random effects, simulation tests via the \code{RLRsim} package (MC,*) ##' \item for fixed effects, F tests via Kenward-Roger approximation using \code{KRmodcomp} from the \code{pbkrtest} package (MC) ##' \item \code{car::Anova} and \code{lmerTest::anova} provide wrappers for \code{pbkrtest}: the latter also provides t tests via the Satterthwaite approximation (P,*) ##' } ##' In the list above, the methods marked \code{MC} provide explicit model comparisons; \code{CI} denotes confidence intervals; and \code{P} denotes parameter-level or sequential tests of all effects in a model. The starred (*) suggestions provide finite-size corrections (important when the number of groups is <50); those marked (+) support GLMMs as well as LMMs. ##' ##' When all else fails, don't forget to keep p-values in perspective: \url{http://www.phdcomics.com/comics/archive.php?comicid=905} ##' if(FALSE) ## C++ code in ../src/mcmcsamp.cpp -- is also #ifdef 0 # @S3method mcmcsamp merMod mcmcsamp.merMod <- function(object, n=1L, verbose=FALSE, saveb=FALSE, ...) { n <- max(1L, as.integer(n)[1]) dd <- getME(object, "devcomp")$dims ranef <- matrix(numeric(0), nrow = dd[["q"]], ncol = 0) if (saveb) ranef <- matrix(, nrow = dd[["q"]], ncol = n) sigma <- matrix(unname(sigma(object)), nrow = 1, ncol = (if (dd[["useSc"]]) n else 0)) ff <- fixef(object) fixef <- matrix(ff, nrow=dd[["p"]], ncol=n) rownames(fixef) <- names(ff) ## FIXME create a copy of the resp and pred modules ans <- new("merMCMC", Gp = object@Gp, # ST = matrix(.Call(mer_ST_getPars, object), dd[["np"]], n), call = object@call, dims = object@dims, deviance = rep.int(unname(object@deviance[["ML"]]), n), fixef = fixef, nc = sapply(object@ST, nrow), ranef = ranef, sigma = sigma) .Call(mer_MCMCsamp, ans, object) } lme4/R/profile.R0000644000176000001440000012712512232467515013146 0ustar ripleyusers##' Methods for profile() of [ng]lmer fitted models ##' ##' Methods for function \code{\link{profile}} (package \pkg{stats}), here for ##' profiling (fitted) mixed effect models. ##' ##' ##' @name profile-methods ##' @title Profile method for merMod objects ##' @aliases profile-methods profile.merMod ##' @docType methods ##' @param fitted a fitted model, e.g., the result of \code{\link{lmer}(..)}. ##' @param which (integer) which parameters to profile: default is all parameters. The parameters are ordered as follows: (1) random effects (theta) parameters; (2) residual standard deviation (or scale parameter for GLMMs where appropriate); (3) fixed effect parameters. Random effects parameters are ordered as in \code{getME(.,"theta")}, i.e. as the lower triangle of a matrix with standard deviations on the diagonal and correlations off the diagonal. FIXME: allow parameter names. ##' @param alphamax maximum alpha value for likelihood ratio confidence regions; used to establish the range of values to be profiled ##' @param maxpts maximum number of points (in each direction, for each parameter) to evaluate in attempting to construct the profile ##' @param delta stepping scale for deciding on next point to profile ##' @param verbose level of output from internal calculations ##' @param devtol tolerance for fitted deviances less than baseline (supposedly minimum) deviance ##' @param maxmult maximum multiplier of the original step size allowed, defaults to 10. ##' @param startmethod method for picking starting conditions for optimization (STUB) ##' @param optimizer (character or function) optimizer to use (see \code{\link{lmer}} for details) ##' @param signames (logical) if \code{TRUE} use abbreviated names of the form \code{.sigNN}, otherwise more meaningful (but longer) names of the form \code{(sd|cor)_(effects)|(group)}. Note that some code for profile transformations (e.g. \code{\link{varianceProf}}) depends on \code{signames==TRUE} ##' @param \dots potential further arguments for \code{profile} methods. ##' @section Methods: ##' \describe{ ##' \item{signature(fitted = \"merMod\")}{ ... } } ##' @seealso For (more expensive) alternative confidence intervals: ##' \code{\link{bootMer}}. ##' @keywords methods ##' @examples ##' fm01ML <- lmer(Yield ~ 1|Batch, Dyestuff, REML = FALSE) ##' system.time( tpr <- profile(fm01ML, optimizer="Nelder_Mead") ) ##' ## ~2.6s (on a 2010 Macbook Pro) ##' system.time( tpr <- profile(fm01ML)) ##' ## ~1s, + possible warning about bobyqa convergence ##' (confint(tpr) -> CIpr) ##' \donttest{% too much precision (etc). but just FYI: ##' stopifnot(all.equal(CIpr, ##' array(c(12.1985292, 38.2299848, 1486.4515, ##' 84.0630513, 67.6576964, 1568.54849), dim = 3:2, ##' dimnames = list(c(".sig01", ".sigma", "(Intercept)"), ##' c("2.5 \%", "97.5 \%"))), ##' tol= 1e-07))# 1.37e-9 {64b} ##' } ##' xyplot(tpr) ##' densityplot(tpr, main="densityplot( profile(lmer(..)) )") ##' splom(tpr) ##' \donttest{% for time constraint ##' system.time(tpr2 <- profile(fm01ML, which=1:2, optimizer="Nelder_Mead")) ## Batch and residual variance only ##' ## GLMM example (running time ~11 seconds on a modern machine) ##' gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), ##' data = cbpp, family = binomial) ##' system.time(pr4 <- profile(gm1)) ##' xyplot(pr4,layout=c(5,1),as.table=TRUE) ##' splom(pr4) ##' }%donttest ##' @importFrom splines backSpline interpSpline periodicSpline ##' @importFrom stats profile ##' @method profile merMod ##' @export profile.merMod <- function(fitted, which=1:nptot, alphamax = 0.01, maxpts = 100, delta = cutoff/8, ## tr = 0, ## FIXME: remove if not doing anything ... verbose=0, devtol=1e-9, maxmult = 10, startmethod = "prev", optimizer="bobyqa", signames=TRUE, ...) { ## FIXME: allow choice of nextstep/nextstart algorithm? ## FIXME: by default, get optimizer from within fitted object ## FIXME: allow selection of individual variables to profile by name? ## FIXME: allow for failure of bounds (non-pos-definite correlation matrices) when >1 cor parameter ## FIXME: generalize to GLMMs ## (use different devfun; ## be careful with scale parameter; ## profile all parameters at once rather than RE first and then fixed) useSc <- isLMM(fitted) || isNLMM(fitted) dd <- devfun2(fitted,useSc,signames) ## FIXME: figure out to what do here ... if (isGLMM(fitted) && fitted@devcomp$dims["useSc"]) stop("can't (yet) profile GLMMs with non-fixed scale parameters") base <- attr(dd, "basedev") thopt <- attr(dd, "thopt") stderr <- attr(dd, "stderr") pp <- environment(dd)$pp X.orig <- pp$X n <- environment(dd)$n p <- length(pp$beta0) opt <- attr(dd, "optimum") nptot <- length(opt) ans <- lapply(opt[which], function(el) NULL) bakspl <- forspl <- ans nvp <- nptot - p # number of variance-covariance pars fe.orig <- opt[-seq_len(nvp)] res <- c(.zeta = 0, opt) res <- matrix(res, nrow = maxpts, ncol = length(res), dimnames = list(NULL, names(res)), byrow = TRUE) ## FIXME: why is cutoff based on nptot (i.e. boundary of simultaneous LRT conf region for nptot values) ## when we are computing (at most) 2-parameter profiles here? cutoff <- sqrt(qchisq(1 - alphamax, nptot)) ## helper functions ## nextpar calculates the next value of the parameter being ## profiled based on the desired step in the profile zeta ## (absstep) and the values of zeta and column cc for rows ## r-1 and r. The parameter may not be below lower (or above upper) nextpar <- function(mat, cc, r, absstep, lower = -Inf, upper = Inf, minstep=1e-6) { rows <- r - (1:0) # previous two row numbers pvals <- mat[rows, cc] zeta <- mat[rows, ".zeta"] num <- diff(pvals) if (is.na(denom <- diff(zeta)) || denom==0) { warning("Last two rows have identical or NA .zeta values: using minstep") step <- minstep } else { step <- absstep*num/denom if (r>1) { if (abs(step) > (maxstep <- abs(maxmult*num))) { maxstep <- sign(step)*maxstep if (verbose) cat(sprintf("capped step at %1.2f (multiplier=%1.2f > %1.2f)\n", maxstep,abs(step/num),maxmult)) step <- maxstep } } } min(upper, max(lower, pvals[2] + sign(num) * step)) } nextstart <- function(mat, pind, r, method="global") { ## FIXME: indexing may need to be checked (e.g. for fixed-effect parameters) switch(method, global=opt[seqpar1][-pind], ## address opt, no zeta column prev=mat[r,1+seqpar1][-pind], extrap=stop("stub")) ## do something with mat[r-(1:0),1+seqnvp])[-pind] ... } ## mkpar generates the parameter vector of theta and ## sigma from the values being profiled in position w mkpar <- function(np, w, pw, pmw) { par <- numeric(np) par[w] <- pw par[-w] <- pmw par } ## fillmat fills the third and subsequent rows of the matrix ## using nextpar and zeta ### FIXME: add code to evaluate more rows near the minimum if that ### constraint was active. fillmat <- function(mat, lowcut, upcut, zetafun, cc) { nr <- nrow(mat) i <- 2L while (i < nr && mat[i, cc] > lowcut && mat[i,cc] < upcut && (is.na(curzeta <- abs(mat[i, ".zeta"])) || curzeta <= cutoff)) { np <- nextpar(mat, cc, i, delta, lowcut, upcut) ns <- nextstart(mat, cc-1, i, startmethod) mat[i + 1L, ] <- zetafun(np,ns) if (verbose>0) { cat(i,cc,mat[i+1L,],"\n") } i <- i + 1L } if (mat[i-1,cc]==lowcut) { ## fill in more values near the minimum } if (mat[i-1,cc]==upcut) { ## fill in more values near the maximum } mat } ## bounds on Cholesky: [0,Inf) for diag, (-Inf,Inf) for diag ## bounds on sd-corr: [0,Inf) for diag, (-1.0,1.0) for diag lower <- pmax(fitted@lower,-1.0) upper <- 1/(fitted@lower != 0)## = ifelse(fitted@lower==0, Inf, 1.0) if (useSc) { lower <- c(lower,0) upper <- c(upper,Inf) } ## bounds on fixed parameters (could allow user-specified bounds for esp. NLMMs?) lower <- c(lower,rep.int(-Inf, p)) upper <- c(upper, rep.int(Inf, p)) npar1 <- if (isLMM(fitted)) nvp else nptot stopifnot(all.equal(unname(dd(opt[seq(npar1)])),base,tol=1e-5)) seqnvp <- intersect(seq_len(npar1),which) seqpar1 <- seq_len(npar1) lowvp <- lower[seqpar1] upvp <- upper[seqpar1] form <- .zeta ~ foo # pattern for interpSpline formula for (w in seqnvp) { if (verbose) cat(if (isLMM(fitted)) "var-cov " else "", "parameter ",w,":\n",sep="") wp1 <- w + 1L start <- opt[seqpar1][-w] pw <- opt[w] lowcut <- lower[w] upcut <- upper[w] zeta <- function(xx,start) { ores <- tryCatch(optwrap(optimizer, par=start, fn=function(x) dd(mkpar(npar1, w, xx, x)), lower = lowvp[-w], upper = upvp [-w]), error=function(e)NULL) if (is.null(ores)) { devdiff <- NA pars <- NA } else { devdiff <- ores$fval-base pars <- ores$par } if (is.na(devdiff)) { warning("NAs detected in profiling") } else { if (devdiff < (-devtol)) stop("profiling detected new, lower deviance") if(devdiff < 0) warning("slightly lower deviances (diff=",devdiff,") detected") } devdiff <- max(0,devdiff) zz <- sign(xx - pw) * sqrt(devdiff) r <- c(zz, mkpar(npar1, w, xx, pars)) if (isLMM(fitted)) r <- c(r,pp$beta(1)) r } ### FIXME: The starting values for the conditional optimization should ### be determined from recent starting values, not always the global ### optimum values. ### Can do this most easily by taking the change in the other parameter values at ### the two last points and extrapolating. ## intermediate storage for pos. and neg. increments pres <- nres <- res ## assign one row, determined by inc. sign, from a small shift ## FIXME:: do something if pw==0 ??? nres[1, ] <- pres[2, ] <- zeta(pw * 1.01, start=opt[seqpar1][-w]) ## fill in the rest of the arrays and collapse them upperf <- fillmat(pres,lowcut, upcut, zeta, wp1) lowerf <- fillmat(nres,lowcut, upcut, zeta, wp1) bres <- as.data.frame(unique(rbind2(upperf,lowerf))) pname <- names(opt)[w] bres$.par <- pname ans[[pname]] <- bres[order(bres[, wp1]), ] form[[3]] <- as.name(pname) ## FIXME: test for bad things here?? bakspl[[pname]] <- tryCatch(backSpline(forspl[[pname]] <- interpSpline(form, bres,na.action=na.omit)), error=function(e)e) if (inherits(bakspl[[pname]],"error")) { warning("non-monotonic profile") } } ## for(w in ..) ## profile fixed effects separately (for LMMs) if (isLMM(fitted)) { offset.orig <- fitted@resp$offset fp <- seq_len(p) fp <- fp[(fp+nvp) %in% which] for (j in fp) { if (verbose) cat("fixed-effect parameter ",j,":\n",sep="") pres <- # intermediate results for pos. incr. nres <- res # and negative increments est <- opt[nvp + j] std <- stderr[j] Xw <-X.orig[, j, drop=TRUE] Xdrop <- .modelMatrixDrop(X.orig, j) pp1 <- do.call(new, list(Class = class(pp), X = Xdrop, Zt = pp$Zt, Lambdat = pp$Lambdat, Lind = pp$Lind, theta = pp$theta, n = nrow(Xdrop)) ) ### FIXME Change this to use the deep copy and setWeights, setOffset, etc. rr <- new(Class=class(fitted@resp), y=fitted@resp$y) rr$setWeights(fitted@resp$weights) fe.zeta <- function(fw, start) { ## (start parameter ignored) rr$setOffset(Xw * fw + offset.orig) rho <- as.environment(list(pp=pp1, resp=rr)) parent.env(rho) <- parent.frame() ores <- optwrap(optimizer, par=thopt, fn=mkdevfun(rho, 0L), lower = pmax(fitted@lower, -1.0), upper = 1/(fitted@lower != 0))## = ifelse(fitted@lower==0, Inf, 1.0) fv <- ores$fval sig <- sqrt((rr$wrss() + pp1$sqrL(1))/n) c(sign(fw - est) * sqrt(fv - base), Cv_to_Sv(ores$par, sapply(fitted@cnms,length),s=sig), ## ores$par * sig, sig, mkpar(p, j, fw, pp1$beta(1))) } nres[1, ] <- pres[2, ] <- fe.zeta(est + delta * std) poff <- nvp + 1L + j bres <- as.data.frame(unique(rbind2(fillmat(pres,-Inf, Inf, fe.zeta, poff), fillmat(nres,-Inf, Inf, fe.zeta, poff)))) thisnm <- names(fe.orig)[j] bres$.par <- thisnm ans[[thisnm]] <- bres[order(bres[, poff]), ] form[[3]] <- as.name(thisnm) bakspl[[thisnm]] <- tryCatch(backSpline(forspl[[thisnm]] <- interpSpline(form, bres)), error=function(e)e) if (inherits(bakspl[[thisnm]],"error")) warning("non-monotonic profile") } ## for(j in 1..p) } ## if isLMM ans <- do.call(rbind, ans) row.names(ans) <- NULL ans$.par <- factor(ans$.par) attr(ans, "forward") <- forspl attr(ans, "backward") <- bakspl class(ans) <- c("thpr", "data.frame") ans } ## This is a hack. The preferred approach is to write a ## subset method for the ddenseModelMatrix and dsparseModelMatrix ## classes .modelMatrixDrop <- function(mm, w) { if (isS4(mm)) { ll <- list(Class = class(mm), assign = attr(mm,"assign")[-w], contrasts = NULL) ## FIXME: where did the contrasts information go?? ## mm@contrasts) X <- mm[, -w, drop = FALSE] ll <- c(ll, lapply(structure(slotNames(X), .Names=slotNames(X)), function(nm) slot(X, nm))) return(do.call("new", ll)) } ans <- mm[, -w, drop=FALSE] attr(ans, "assign") <- attr(mm, "assign")[-w] ans } ## The deviance is profiled with respect to the fixed-effects ## parameters but not with respect to sigma. The other parameters ## are on the standard deviation scale, not the theta scale. ## ## @title Return a function for evaluation of the deviance. ## @param fm a fitted model of class merMod ## @return a function for evaluating the deviance in the extended ## parameterization. This is profiled with respect to the ## variance-covariance parameters (fixed-effects done separately). devfun2 <- function(fm,useSc,signames) { ## FIXME: have to distinguish between ## 'useSc' (GLMM: report profiled scale parameter) and ## 'useSc' (NLMM/LMM: scale theta by sigma) ## GLMMuseSc <- fm@devcomp$dims["useSc"] stopifnot(is(fm, "merMod")) fm <- refitML(fm) basedev <- deviance(fm) vlist <- sapply(fm@cnms,length) sig <- sigma(fm) ## only if useSc=TRUE? stdErr <- unname(coef(summary(fm))[,2]) pp <- fm@pp$copy() ## opt <- c(pp$theta*sig, sig) if (useSc) { opt <- Cv_to_Sv(pp$theta, n=vlist, s=sig) names(opt) <- if (signames) { c(sprintf(".sig%02d", seq(length(opt)-1)), ".sigma") } else { c(tnames(fm,old=FALSE,prefix=c("sd","cor")),"sigma") } } else { opt <- Cv_to_Sv(pp$theta, n=vlist) names(opt) <- if (signames) { sprintf(".sig%02d", seq_along(opt)) } else { tnames(fm,old=FALSE,prefix=c("sd","cor")) } } opt <- c(opt, fixef(fm)) resp <- fm@resp$copy() np <- length(pp$theta) nf <- length(fixef(fm)) if (!isGLMM(fm)) np <- np + 1L n <- nrow(pp$V) # use V, not X so it works with nlmer if (isLMM(fm)) { ans <- function(pars) { stopifnot(is.numeric(pars), length(pars) == np) ## Assumption: all parameters, including the residual SD on SD-scale sigma <- pars[np] ## .Call(lmer_Deviance, pp$ptr(), resp$ptr(), pars[-np]/sigma) ## convert from sdcor vector back to 'unscaled theta' thpars <- Sv_to_Cv(pars,n=vlist,s=sigma) .Call(lmer_Deviance, pp$ptr(), resp$ptr(), thpars) sigsq <- sigma^2 pp$ldL2() + (resp$wrss() + pp$sqrL(1))/sigsq + n * log(2 * pi * sigsq) } } else { d0 <- update(fm,devFunOnly=TRUE) ## from glmer: ## rho <- new.env(parent=parent.env(environment())) ## rho$pp <- do.call(merPredD$new, c(reTrms[c("Zt","theta","Lambdat","Lind")], n=nrow(X), list(X=X))) ## rho$resp <- mkRespMod(fr, if(REML) p else 0L) ans <- function(pars) { stopifnot(is.numeric(pars), length(pars) == np+nf) ## FIXME: allow useSc (i.e. NLMMs) if (!useSc) { thpars <- Sv_to_Cv(pars[seq(np)],n=vlist) } else { thpars <- Sv_to_Cv(pars[seq(np)],n=vlist,s=pars[np]) } fixpars <- pars[-seq(np)] d0(c(thpars,fixpars)) } } attr(ans, "optimum") <- opt # w/ names() attr(ans, "basedev") <- basedev attr(ans, "thopt") <- pp$theta attr(ans, "stderr") <- stdErr class(ans) <- "devfun" ans } ## extract only the y component from a prediction predy <- function(sp, vv) { if (inherits(sp,"error")) rep(NA,length(vv)) else predict(sp, vv)$y } stripExpr <- function(ll, nms) { stopifnot(inherits(ll, "list"), is.character(nms)) sigNm <- which(nms == ".sigma") lsigNm <- which(nms == ".lsigma") sigNms <- grep("^.sig[0-9]+", nms) sigsub <- as.integer(substring(nms[sigNms], 5)) lsigNms <- grep("^.lsig[0-9]+", nms) lsigsub <- as.integer(substring(nms[lsigNms], 6)) fLevs <- as.expression(nms) fLevs[sigNm] <- expression(sigma) fLevs[lsigNm] <- expression(log(sigma)) fLevs[sigNms] <- parse(text=paste("sigma[", sigsub, "]")) fLevs[lsigNms] <- parse(text=paste("log(sigma[", lsigsub, "])")) levsExpr <- substitute(strip.custom(factor.levels=foo), list(foo=fLevs)) llNms <- names(ll) snames <- c("strip", "strip.left") if (all(!(snames %in% llNms))) { ll$strip <- levsExpr } else { lapply(snames, function(nm) { if (nm %in% llNms) { vv <- ll[[nm]] if (is.logical(vv) && vv) ll[[nm]] <<- levsExpr } }) } ll } ## A lattice-based plot method for profile objects ##' @importFrom lattice xyplot ##' @S3method xyplot thpr xyplot.thpr <- function (x, data = NULL, levels = sqrt(qchisq(pmax.int(0, pmin.int(1, conf)), 1)), conf = c(50, 80, 90, 95, 99)/100, absVal = FALSE, ...) { levels <- sort(levels[is.finite(levels) & levels > 0]) spl <- attr(x, "forward") bspl <- attr(x, "backward") zeta <- c(-rev(levels), 0, levels) fr <- data.frame(zeta = rep.int(zeta, length(spl)), pval = unlist(lapply(bspl,predy,zeta)), pnm = gl(length(spl), length(zeta), labels = names(spl))) if (length(ind <- which(is.na(fr$pval)))) { fr[ind, "zeta"] <- 0 for (i in ind) ### FIXME: Should check which bound has been violated, although it ### will almost always be the minimum. if (!is.null(curspl <- spl[[fr[i, "pnm"] ]])) fr[i, "pval"] <- min(curspl$knots) } ylab <- expression(zeta) if (absVal) { fr$zeta <- abs(fr$zeta) ylab <- expression("|" * zeta * "|") } ll <- c(list(...), list(x = zeta ~ pval | pnm, data=fr, scales = list(x = list(relation = 'free')), ylab = ylab, xlab = NULL, panel = function(x, y, ...) { panel.grid(h = -1, v = -1) myspl <- spl[[panel.number()]] if (inherits(myspl,"error") || is.null(myspl)) { warning(sprintf("bad profile for variable %d: skipped",panel.number())) } else { lsegments(x, y, x, 0, ...) lims <- current.panel.limits()$xlim krange <- range(myspl$knots) pr <- predict(myspl, seq(max(lims[1], krange[1]), min(lims[2], krange[2]), len = 101)) if (absVal) { pr$y <- abs(pr$y) y[y == 0] <- NA lsegments(x, y, rev(x), y) } else { panel.abline(h = 0, ...) } panel.lines(pr$x, pr$y) } })) do.call(xyplot, stripExpr(ll, names(spl))) } ## copy of stats:::format.perc format.perc <- function (probs, digits) { paste(format(100 * probs, trim = TRUE, scientific = FALSE, digits = digits), "%") } ##' @importFrom stats confint ##' @S3method confint thpr confint.thpr <- function(object, parm, level = 0.95, zeta, ...) { bak <- attr(object, "backward") bnms <- names(bak) if (missing(parm)) parm <- bnms else if (is.numeric(parm)) parm <- bnms[parm] parm <- intersect(as.character(parm), bnms) cn <- NULL if (missing(zeta)) { a <- (1 - level)/2 a <- c(a, 1 - a) zeta <- qnorm(a) cn <- format.perc(a, 3) } ci <- t(sapply(parm, function(nm) predy(bak[[nm]], zeta))) colnames(ci) <- cn ci } ## FIXME: make bootMer more robust; make profiling more robust; ## more warnings; documentation ... ##' Compute confidence intervals on the parameters of an lme4 fit ##' @param object a fitted [ng]lmer model ##' @param parm parameters (specified by integer position) ##' @param level confidence level ##' @param method for computing confidence intervals ##' @param zeta likelihood cutoff ##' (if not specified, computed from \code{level}: "profile" only) ##' @param nsim number of simulations for parametric bootstrap intervals ##' @param boot.type bootstrap confidence interval type ##' @param quiet (logical) suppress messages about computationally intensive profiling? ##' @param oldNames (logical) use old-style names for \code{method="profile"}? (See \code{signames} argument to \code{\link{profile}} ##' @param \dots additional parameters to be passed to \code{\link{profile.merMod}} or \code{\link{bootMer}} ##' @return a numeric table of confidence intervals ##' @details Depending on the method specified, this function will ##' compute confidence intervals by ("profile") computing a likelihood profile ##' and finding the appropriate cutoffs based on the likelihood ratio test; ##' ("Wald") approximate the confidence intervals (of fixed-effect parameters ##' only) based on the estimated local curvature of the likelihood surface; ##' ("boot") perform parametric bootstrapping ##' with confidence intervals computed from the bootstrap distribution ##' according to \code{boot.type} (see \code{\link{boot.ci}}) ##' @importFrom stats confint ##' @S3method confint merMod ##' @method confint merMod ##' @examples ##' fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) ##' fm1W <- confint(fm1,method="Wald") ##' \dontrun{ ##' ## ~20 seconds, MacBook Pro laptop ##' system.time(fm1P <- confint(fm1,method="profile",oldNames=FALSE)) ## default ##' ## ~ 40 seconds ##' system.time(fm1B <- confint(fm1,method="boot", ##' .progress="txt", PBargs=list(style=3))) ##' } ##' load(system.file("testdata","confint_ex.rda",package="lme4")) ##' fm1P ##' fm1B confint.merMod <- function(object, parm, level = 0.95, method=c("profile","Wald","boot"), zeta, nsim=500, boot.type="perc", quiet=FALSE, oldNames=TRUE, ...) { method <- match.arg(method) if (!missing(parm) && !is.numeric(parm) && method %in% c("profile","boot")) stop("for method='",method,"', 'parm' must be specified as an integer") switch(method, "profile" = { if (!quiet) message("Computing profile confidence intervals ...") utils::flush.console() pp <- if(missing(parm)) { profile(object, signames=oldNames, ...) } else { profile(object, which=parm, signames=oldNames, ...) } confint(pp,level=level,zeta=zeta) }, "Wald" = { ## copied with small changes from confint.default cf <- fixef(object) ## coef() -> fixef() pnames <- names(cf) if (missing(parm)) parm <- pnames else if (is.numeric(parm)) parm <- pnames[parm] ## n.b. can't use sqrt(...)[parm] (diag() loses names) a <- (1 - level)/2 a <- c(a, 1 - a) pct <- format.perc(a, 3) fac <- qnorm(a) ci <- array(NA, dim = c(length(parm), 2L), dimnames = list(parm, pct)) sdiag <- function(x) if (length(x)==1) c(x) else diag(x) ses <- sqrt(sdiag(vcov(object)[parm,parm])) ci[] <- cf[parm] + ses %o% fac ci ## only gives confidence intervals on fixed effects ... }, "boot" = { if (!quiet) message("Computing bootstrap confidence intervals ...") utils::flush.console() bootFun <- function(x) { th <- getME(x,"theta") scaleTh <- (isLMM(x) || isNLMM(x)) useSc <- x@devcomp$dims["useSc"] ## FIXME: still ugly. Best cleanup via Cv_to_Sv ... if (scaleTh) { ## scale variances by sigma and include it ss <- setNames(Cv_to_Sv(th,s=sigma(x)), c(tnames(x,old=FALSE, prefix=c("sd","cor")),"sigma")) } else if (useSc) { ## don't scale variances but do include sigma ss <- setNames(c(Cv_to_Sv(th),sigma(x)), c(tnames(x,old=FALSE, prefix=c("sd","cor")),"sigma")) } else { ## no scaling, no sigma ss <- setNames(Cv_to_Sv(th), tnames(x,old=FALSE,prefix=c("sd","cor"))) } res <- c(ss, fixef(x)) res } bb <- bootMer(object, bootFun, nsim=nsim,...) bci <- lapply(seq_along(bb$t0), boot.out=bb, boot::boot.ci,type=boot.type,conf=level) citab <- t(sapply(bci,function(x) x[["percent"]][4:5])) a <- (1 - level)/2 a <- c(a, 1 - a) pct <- format.perc(a, 3) dimnames(citab) <- list(names(bb[["t0"]]),pct) pnames <- rownames(citab) if (missing(parm)) parm <- pnames else if (is.numeric(parm)) parm <- pnames[parm] citab[parm,] }, stop("unknown confidence interval method")) } ## Convert x-cosine and y-cosine to average and difference. ## Convert the x-cosine and the y-cosine to an average and difference ## ensuring that the difference is positive by flipping signs if ## necessary ## @param xc x-cosine ## @param yc y-cosine ad <- function(xc, yc) { a <- (xc + yc)/2 d <- (xc - yc) cbind(sign(d)* a, abs(d)) } ## convert d versus a (as an xyVector) and level to a matrix of taui and tauj ## @param xy an xyVector ## @param lev the level of the contour tauij <- function(xy, lev) lev * cos(xy$x + outer(xy$y/2, c(-1, 1))) ## @title safe arc-cosine ## @param x numeric vector argument ## @return acos(x) being careful of boundary conditions sacos <- function(x) acos(pmax.int(-0.999, pmin.int(0.999, x))) ## Generate a contour ## ## @title Generate a contour ## @param sij the arc-cosines of i on j ## @param sji the arc-cosines of j on i ## @param levels numeric vector of levels at which to interpolate ## @param nseg number of segments in the interpolated contour ## @return a list with components ## \item{tki}{the tau-scale predictions of i on j at the contour levels} ## \item{tkj}{the tau-scale predictions of j on i at the contour levels} ## \item{pts}{an array of dimension (length(levels), nseg, 2) containing the points on the contours} cont <- function(sij, sji, levels, nseg = 101) { ada <- array(0, c(length(levels), 2, 4)) ada[, , 1] <- ad(0, sacos(predy(sij, levels)/levels)) ada[, , 2] <- ad(sacos(predy(sji, levels)/levels), 0) ada[, , 3] <- ad(pi, sacos(predy(sij, -levels)/levels)) ada[, , 4] <- ad(sacos(predy(sji, -levels)/levels), pi) pts <- array(0, c(length(levels), nseg + 1, 2)) for (i in seq_along(levels)) pts[i, ,] <- tauij(predict(periodicSpline(ada[i, 1, ], ada[i, 2, ]), nseg = nseg), levels[i]) levs <- c(-rev(levels), 0, levels) list(tki = predict(sij, levs), tkj = predict(sji, levs), pts = pts) } ## copied from lattice:::chooseFace chooseFace <- function (fontface = NULL, font = 1) { if (is.null(fontface)) font else fontface } ##' Draws profile pairs plots. Contours are for the marginal ##' two-dimensional regions (i.e. using df = 2). ##' ##' @title Profile pairs plot ##' @param x the result of \code{\link{profile}} (or very similar structure) ##' @param data unused - only for compatibility with generic ##' @param levels the contour levels to be shown; usually derived from \code{conf} ##' @param conf numeric vector of confidence levels to be shown as contours ##' @param ... further arguments passed to \code{\link{splom}} ##' @importFrom grid gpar viewport ##' @importFrom lattice splom ##' @method splom thpr ##' @export splom.thpr <- function (x, data, levels = sqrt(qchisq(pmax.int(0, pmin.int(1, conf)), 2)), conf = c(50, 80, 90, 95, 99)/100, ...) { mlev <- max(levels) spl <- attr(x, "forward") frange <- sapply(spl, function(x) range(x$knots)) bsp <- attr(x, "backward") brange <- sapply(bsp, function(x) range(x$knots)) pfr <- do.call(cbind, lapply(bsp, predy, c(-mlev, mlev))) pfr[1, ] <- pmax.int(pfr[1, ], frange[1, ], na.rm = TRUE) pfr[2, ] <- pmin.int(pfr[2, ], frange[2, ], na.rm = TRUE) nms <- names(spl) ## Create data frame fr of par. vals in zeta coordinates fr <- x[, -1] for (nm in nms) fr[[nm]] <- predy(spl[[nm]], na.omit(fr[[nm]])) fr1 <- fr[1, nms] ## create a list of lists with the names of the parameters traces <- lapply(fr1, function(el) lapply(fr1, function(el1) list())) for (j in seq_along(nms)[-1]) { for (i in seq_len(j - 1)) { .par <- NULL ## suppress R CMD check warning fri <- subset(fr, .par == nms[i]) sij <- interpSpline(fri[ , i], fri[ , j]) frj <- subset(fr, .par == nms[j]) sji <- interpSpline(frj[ , j], frj[ , i]) ll <- cont(sij, sji, levels) traces[[j]][[i]] <- list(sij = sij, sji = sji, ll = ll) } } ## panel function for lower triangle lp <- function(x, y, groups, subscripts, i, j, ...) { tr <- traces[[j]][[i]] grid::pushViewport(viewport(xscale = c(-1.07, 1.07) * mlev, yscale = c(-1.07, 1.07) * mlev)) dd <- sapply(current.panel.limits(), diff)/50 psij <- predict(tr$sij) ll <- tr$ll ## now do the actual plotting panel.grid(h = -1, v = -1) llines(psij$y, psij$x, ...) llines(predict(tr$sji), ...) with(ll$tki, lsegments(y - dd[1], x, y + dd[1], x, ...)) with(ll$tkj, lsegments(x, y - dd[2], x, y + dd[2], ...)) for (k in seq_along(levels)) llines(ll$pts[k, , ], ...) grid::popViewport(1) } ## panel function for upper triangle up <- function(x, y, groups, subscripts, i, j, ...) { ## panels are transposed so reverse i and j jj <- i ii <- j tr <- traces[[jj]][[ii]] ll <- tr$ll pts <- ll$pts limits <- current.panel.limits() psij <- predict(tr$sij) psji <- predict(tr$sji) ## do the actual plotting panel.grid(h = -1, v = -1) llines(predy(bsp[[ii]], psij$x), predy(bsp[[jj]], psij$y), ...) llines(predy(bsp[[ii]], psji$y), predy(bsp[[jj]], psji$x), ...) for (k in seq_along(levels)) llines(predy(bsp[[ii]], pts[k, , 2]), predy(bsp[[jj]], pts[k, , 1]), ...) } dp <- function(x = NULL, # diagonal panel varname = NULL, limits, at = NULL, lab = NULL, draw = TRUE, varname.col = add.text$col, varname.cex = add.text$cex, varname.lineheight = add.text$lineheight, varname.font = add.text$font, varname.fontfamily = add.text$fontfamily, varname.fontface = add.text$fontface, axis.text.col = axis.text$col, axis.text.alpha = axis.text$alpha, axis.text.cex = axis.text$cex, axis.text.font = axis.text$font, axis.text.fontfamily = axis.text$fontfamily, axis.text.fontface = axis.text$fontface, axis.line.col = axis.line$col, axis.line.alpha = axis.line$alpha, axis.line.lty = axis.line$lty, axis.line.lwd = axis.line$lwd, i, j, ...) { n.var <- eval.parent(expression(n.var)) add.text <- trellis.par.get("add.text") axis.line <- trellis.par.get("axis.line") axis.text <- trellis.par.get("axis.text") if (!is.null(varname)) grid::grid.text(varname, gp = gpar(col = varname.col, cex = varname.cex, lineheight = varname.lineheight, fontface = chooseFace(varname.fontface, varname.font), fontfamily = varname.fontfamily)) if (draw) { at <- pretty(limits) sides <- c("left", "top") if (j == 1) sides <- "top" if (j == n.var) sides <- "left" for (side in sides) panel.axis(side = side, at = at, labels = format(at, trim = TRUE), ticks = TRUE, check.overlap = TRUE, half = side == "top" && j > 1, tck = 1, rot = 0, text.col = axis.text.col, text.alpha = axis.text.alpha, text.cex = axis.text.cex, text.font = axis.text.font, text.fontfamily = axis.text.fontfamily, text.fontface = axis.text.fontface, line.col = axis.line.col, line.alpha = axis.line.alpha, line.lty = axis.line.lty, line.lwd = axis.line.lwd) lims <- c(-1.07, 1.07) * mlev grid::pushViewport(viewport(xscale = lims, yscale = lims)) side <- if(j == 1) "right" else "bottom" which.half <- if(j == 1) "lower" else "upper" at <- pretty(lims) panel.axis(side = side, at = at, labels = format(at, trim = TRUE), ticks = TRUE, half = TRUE, which.half = which.half, tck = 1, rot = 0, text.col = axis.text.col, text.alpha = axis.text.alpha, text.cex = axis.text.cex, text.font = axis.text.font, text.fontfamily = axis.text.fontfamily, text.fontface = axis.text.fontface, line.col = axis.line.col, line.alpha = axis.line.alpha, line.lty = axis.line.lty, line.lwd = axis.line.lwd) grid::popViewport(1) } } splom(~ pfr, lower.panel = lp, upper.panel = up, diag.panel = dp, ...) } ## Transform an lmer profile to the scale of the logarithm of the ## standard deviation of the random effects. ## @title Transform an lmer profile to the logarithm scale ## @param x an object that inherits from class "thpr" ## @param base the base of the logarithm. Defaults to natural ## logarithms ## ## @return an lmer profile like x with all the .sigNN parameters ## replaced by .lsigNN. The forward and backward splines for ## these parameters are recalculated. ##' @S3method log thpr log.thpr <- function (x, base = exp(1)) { cn <- colnames(x) sigs <- grep("^\\.sig", cn) if (length(sigs)) { colnames(x) <- sub("^\\.sig", ".lsig", cn) levels(x[[".par"]]) <- sub("^\\.sig", ".lsig", levels(x[[".par"]])) names(attr(x, "backward")) <- names(attr(x, "forward")) <- sub("^\\.sig", ".lsig", names(attr(x, "forward"))) for (nm in colnames(x)[sigs]) { x[[nm]] <- log(x[[nm]], base = base) .par <- NULL ## suppress R CMD check warning fr <- subset(x, .par == nm & is.finite(x[[nm]])) ## FIXME: avoid subset for global-variable false positive ## fr <- x[x$.par == nm & is.finite(x[[nm]]),] form <- eval(substitute(.zeta ~ nm, list(nm = as.name(nm)))) attr(x, "forward")[[nm]] <- interpSpline(form, fr) attr(x, "backward")[[nm]] <- backSpline(attr(x, "forward")[[nm]]) } ## eliminate rows that produced non-finite logs x <- x[apply(is.finite(as.matrix(x[, sigs])), 1, all),] } x } ### FIXME: Not exported and nowhere used ## Transform a profile from the standard deviation parameters to the variance ## ## @title Transform to variance component scale ## @param x a profile object from a mixed-effects model ## @return a modified profile object varpr <- function (x) { .par <- NULL ## suppress R CMD check warning cn <- colnames(x) sigs <- grep("^\\.sig", cn) if (length(sigs)) { colnames(x) <- sub("^\\.sig", ".sigsq", cn) levels(x[[".par"]]) <- sub("^\\.sig", ".sigsq", levels(x[[".par"]])) names(attr(x, "backward")) <- names(attr(x, "forward")) <- sub("^\\.sig", ".sigsq", names(attr(x, "forward"))) for (nm in colnames(x)[sigs]) { x[[nm]] <- x[[nm]]^2 fr <- subset(x, .par == nm & is.finite(x[[nm]])) form <- eval(substitute(.zeta ~ nm, list(nm = as.name(nm)))) attr(x, "forward")[[nm]] <- interpSpline(form, fr) attr(x, "backward")[[nm]] <- backSpline(attr(x, "forward")[[nm]]) } ## eliminate rows the produced non-finite logs x <- x[apply(is.finite(as.matrix(x[, sigs])), 1, all),] } x } ## Create an approximating density from a profile object ## ## @title Approximate densities from profiles ## @param pr a profile object ## @param npts number of points at which to evaluate the density ## @param upper upper bound on cumulative for a cutoff ## @return a data frame ## @export dens <- function(pr, npts=201, upper=0.999) { stopifnot(inherits(pr, "thpr")) npts <- as.integer(npts) stopifnot(inherits(pr, "thpr"), npts > 0, is.numeric(upper), 0.5 < upper, upper < 1) spl <- attr(pr, "forward") bspl <- attr(pr, "backward") zeta <- c(qnorm(1-upper), qnorm(upper)) rng <- lapply(bspl, function(spl) { rng <- predy(spl, zeta) if (is.na(rng[1])) rng[1] <- 0 if (is.na(rng[2])) { ## try harder to pick an upper bound upper <- 1-10^seq(-4,-1,length=21) i <- 1 while (is.na(rng[2]) && i<=length(upper)) { rng[2] <- predy(spl,qnorm(upper[i])) i <- i + 1 } if (is.na(rng[2])) { warning("can't find an upper bound for the profile") return(rep(NA,npts)) } } seq(rng[1], rng[2], len=npts) }) fr <- data.frame(pval=unlist(rng), pnm=gl(length(rng), npts, labels=names(rng))) dd <- list() for (nm in names(rng)) { zz <- predy(spl[[nm]], rng[[nm]]) dd[[nm]] <- dnorm(zz) * predict(spl[[nm]], rng[[nm]], deriv=1)$y } fr$density <- unlist(dd) fr } ##' Densityplot method for a mixed-effects model profile ## ##' @title densityplot from a mixed-effects profile ##' @param x a mixed-effects profile ##' @param data not used - for compatibility with generic ##' @param ... optional arguments to \code{\link[lattice]{densityplot}()} ##' from package \pkg{lattice}. ##' @return a density plot ##' @examples ## see example("profile.merMod") ##' @importFrom lattice densityplot ##' @method densityplot thpr ##' @export densityplot.thpr <- function(x, data, ...) { ll <- c(list(...), list(x=density ~ pval|pnm, data=dens(x), type=c("l","g"), scales=list(relation="free"), xlab=NULL)) do.call(xyplot, stripExpr(ll, names(attr(x, "forward")))) } ##' Transform a mixed-effects profile to the variance scale ##' ##' @title Transform to the variance scale ##' @param pr a mixed-effects model profile ##' @return a transformed mixed-effects model profile ##' @export varianceProf <- function(pr) { .par <- NULL ## suppress R CMD check warning stopifnot(inherits(pr, "thpr")) spl <- attr(pr, "forward") onms <- names(spl) # names of original variables vc <- onms[grep("^.sig", onms)] # variance components ans <- subset(pr, .par %in% vc, select=c(".zeta", vc, ".par")) ans[[".par"]] <- factor(ans[[".par"]]) # drop unused levels if (".lsig" %in% vc) ans$.lsig <- exp(ans$.lsig) attr(ans, "forward") <- attr(ans, "backward") <- list() for (nm in vc) { ans[[nm]] <- ans[[nm]]^2 fr <- subset(ans, .par == nm & is.finite(ans[[nm]])) form <- eval(substitute(.zeta ~ nm, list(nm = as.name(nm)))) attr(ans, "forward")[[nm]] <- interpSpline(form, fr) attr(ans, "backward")[[nm]] <- backSpline(attr(ans, "forward")[[nm]]) } ans } ## convert profile to data frame, adding a .focal parameter to simplify lattice/ggplot plotting ##' @method as.data.frame thpr ##' @param x the result of \code{\link{profile}} (or very similar structure) ##' @export ##' @rdname profile-methods as.data.frame.thpr <- function(x,...) { class(x) <- "data.frame" m <- as.matrix(x[,seq(ncol(x))-1]) ## omit .par x.p <- x[[".par"]] x[[".focal"]] <- m[cbind(seq(nrow(x)),match(x.p,names(x)))] x[[".par"]] <- factor(x.p, levels=unique(as.character(x.p))) ## restore order x } lme4/R/utilities.R0000644000176000001440000007040712232467515013521 0ustar ripleyusersif(getRversion() < "2.15") paste0 <- function(...) paste(..., sep = '') ### Utilities for parsing and manipulating mixed-model formulas ##' From the result of \code{\link{findbars}} applied to a model formula and ##' and the evaluation frame, create the model matrix, etc. associated with ##' random-effects terms. See the description of the returned value for a ##' detailed list. ##' ##' @title Create Z, Lambda, Lind, etc. ##' @param bars a list of parsed random-effects terms ##' @param fr a model frame in which to evaluate these terms ##' @return a list with components ##' \item{Zt}{transpose of the sparse model matrix for the random effects} ##' \item{Lambdat}{transpose of the sparse relative covariance factor} ##' \item{Lind}{an integer vector of indices determining the mapping of the ##' elements of the \code{theta} to the \code{"x"} slot of \code{Lambdat}} ##' \item{theta}{initial values of the covariance parameters} ##' \item{lower}{lower bounds on the covariance parameters} ##' \item{flist}{list of grouping factors used in the random-effects terms} ##' \item{cnms}{a list of column names of the random effects according to ##' the grouping factors} ##' @importFrom Matrix sparseMatrix rBind drop0 ##' @importMethodsFrom Matrix coerce ##' @family utilities ##' @export mkReTrms <- function(bars, fr) { if (!length(bars)) stop("No random effects terms specified in formula") stopifnot(is.list(bars), all(sapply(bars, is.language)), inherits(fr, "data.frame")) names(bars) <- unlist(lapply(bars, function(x) deparse(x[[3]]))) ## auxiliary {named, for easier inspection}: mkBlist <- function(x) { frloc <- fr ## convert grouping variables to factors as necessary for (i in all.vars(x[[3]])) { frloc[[i]] <- factor(frloc[[i]]) } ff <- eval(substitute(factor(fac), list(fac = x[[3]])), frloc) if (all(is.na(ff))) stop("Invalid grouping factor specification, ", deparse(x[[3]])) nl <- length(levels(ff)) mm <- model.matrix(eval(substitute( ~ foo, list(foo = x[[2]]))), fr) nc <- ncol(mm) nseq <- seq_len(nc) sm <- as(ff, "sparseMatrix") if (nc > 1) sm <- do.call(rBind, lapply(nseq, function(i) sm)) ## hack for NA values contained in factor (FIXME: test elsewhere for consistency?) sm@x[] <- t(mm[!is.na(ff),]) ## When nc > 1 switch the order of the rows of sm ## so the random effects for the same level of the ## grouping factor are adjacent. if (nc > 1) sm <- sm[as.vector(matrix(seq_len(nc * nl), ncol = nl, byrow = TRUE)),] list(ff = ff, sm = sm, nl = nl, cnms = colnames(mm)) } blist <- lapply(bars, mkBlist) nl <- unlist(lapply(blist, "[[", "nl")) # no. of levels per term ## order terms stably by decreasing number of levels in the factor if (any(diff(nl)) > 0) { ord <- rev(order(nl)) blist <- blist[ord] nl <- nl[ord] } Zt <- do.call(rBind, lapply(blist, "[[", "sm")) q <- nrow(Zt) ## Create and install Lambdat, Lind, etc. This must be done after ## any potential reordering of the terms. cnms <- lapply(blist, "[[", "cnms") nc <- vapply(cnms, length, 1L) # no. of columns per term nth <- as.integer((nc * (nc+1))/2) # no. of parameters per term nb <- nc * nl # no. of random effects per term stopifnot(sum(nb) == q) boff <- cumsum(c(0L, nb)) # offsets into b thoff <- cumsum(c(0L, nth)) # offsets into theta ### FIXME: should this be done with cBind and avoid the transpose ### operator? In other words should Lambdat be generated directly ### instead of generating Lambda first then transposing? Lambdat <- t(do.call(sparseMatrix, do.call(rBind, lapply(seq_along(blist), function(i) { mm <- matrix(seq_len(nb[i]), ncol = nc[i], byrow = TRUE) dd <- diag(nc[i]) ltri <- lower.tri(dd, diag = TRUE) ii <- row(dd)[ltri] jj <- col(dd)[ltri] dd[cbind(ii, jj)] <- seq_along(ii) data.frame(i = as.vector(mm[, ii]) + boff[i], j = as.vector(mm[, jj]) + boff[i], x = as.double(rep.int(seq_along(ii), rep.int(nl[i], length(ii))) + thoff[i])) })))) thet <- numeric(sum(nth)) ll <- list(Zt=Matrix::drop0(Zt), theta=thet, Lind=as.integer(Lambdat@x), Gp=unname(c(0L, cumsum(nb)))) ## lower bounds on theta elements are 0 if on diagonal, else -Inf ll$lower <- -Inf * (thet + 1) ll$lower[unique(diag(Lambdat))] <- 0 ll$theta[] <- is.finite(ll$lower) # initial values of theta are 0 off-diagonal, 1 on Lambdat@x[] <- ll$theta[ll$Lind] # initialize elements of Lambdat ll$Lambdat <- Lambdat # massage the factor list fl <- lapply(blist, "[[", "ff") # check for repeated factors fnms <- names(fl) if (length(fnms) > length(ufn <- unique(fnms))) { fl <- fl[match(ufn, fnms)] asgn <- match(fnms, ufn) } else asgn <- seq_along(fl) names(fl) <- ufn fl <- do.call(data.frame, c(fl, check.names = FALSE)) attr(fl, "assign") <- asgn ll$flist <- fl ll$cnms <- cnms ll } ## {mkReTrms} ##' Create an lmerResp, glmResp or nlsResp instance ##' ##' @title Create an lmerResp, glmResp or nlsResp instance ##' @param fr a model frame ##' @param REML logical scalar, value of REML for an lmerResp instance ##' @param family the optional glm family (glmResp only) ##' @param nlenv the nonlinear model evaluation environment (nlsResp only) ##' @param nlmod the nonlinear model function (nlsResp only) ##' @param ... where to look for response information if \code{fr} is missing. ##' Can contain a model response, \code{y}, offset, \code{offset}, and weights, ##' \code{weights}. ##' @return an lmerResp or glmResp or nlsResp instance ##' @family utilities ##' @export mkRespMod <- function(fr, REML=NULL, family = NULL, nlenv = NULL, nlmod = NULL, ...) { if(!missing(fr)){ y <- model.response(fr) offset <- model.offset(fr) weights <- model.weights(fr) N <- n <- nrow(fr) etastart_update <- model.extract(fr, "etastart") } else{ fr <- list(...) y <- fr$y N <- n <- if(is.matrix(y)) nrow(y) else length(y) offset <- fr$offset weights <- fr$weights etastart_update <- fr$etastart } ## FIXME: may need to add X, or pass it somehow, if we want to use glm.fit #y <- model.response(fr) if(length(dim(y)) == 1) { ## avoid problems with 1D arrays, but keep names nm <- rownames(y) dim(y) <- NULL if(!is.null(nm)) names(y) <- nm } rho <- new.env() rho$y <- if (is.null(y)) numeric(0) else y if (!is.null(REML)) rho$REML <- REML rho$etastart <- fr$etastart rho$mustart <- fr$mustart #N <- n <- nrow(fr) if (!is.null(nlenv)) { stopifnot(is.language(nlmod), is.environment(nlenv), is.numeric(val <- eval(nlmod, nlenv)), length(val) == n, is.matrix(gr <- attr(val, "gradient")), mode(gr) == "numeric", nrow(gr) == n, !is.null(pnames <- colnames(gr))) N <- length(gr) rho$mu <- as.vector(val) rho$sqrtXwt <- as.vector(gr) rho$gam <- unname(unlist(lapply(pnames, function(nm) get(nm, envir=nlenv)))) } if (!is.null(offset)) { if (length(offset) == 1L) offset <- rep.int(offset, N) stopifnot(length(offset) == N) rho$offset <- unname(offset) } else rho$offset <- rep.int(0, N) if (!is.null(weights)) { stopifnot(length(weights) == n, all(weights >= 0)) rho$weights <- unname(weights) } else rho$weights <- rep.int(1, n) if (is.null(family)) { if (is.null(nlenv)) return(do.call(lmerResp$new, as.list(rho))) return(do.call(nlsResp$new, c(list(nlenv=nlenv, nlmod=substitute(~foo, list(foo=nlmod)), pnames=pnames), as.list(rho)))) } stopifnot(inherits(family, "family")) # need weights for initialize evaluation rho$nobs <- n eval(family$initialize, rho) family$initialize <- NULL # remove clutter from str output ll <- as.list(rho) ans <- do.call(new, c(list(Class="glmResp", family=family), ll[setdiff(names(ll), c("m", "nobs", "mustart"))])) ans$updateMu(if (!is.null(es <- etastart_update)) es else family$linkfun(get("mustart", rho))) ans } ##' From the right hand side of a formula for a mixed-effects model, ##' determine the pairs of expressions that are separated by the ##' vertical bar operator. Also expand the slash operator in grouping ##' factor expressions. ##' ##' @title Determine random-effects expressions from a formula ##' @seealso \code{\link{formula}}, \code{\link{model.frame}}, \code{\link{model.matrix}}. ##' @param term a mixed-model formula ##' @return pairs of expressions that were separated by vertical bars ##' @section Note: This function is called recursively on individual ##' terms in the model, which is why the argument is called \code{term} and not ##' a name like \code{form}, indicating a formula. ##' @examples ##' findbars(f1 <- Reaction ~ Days + (Days|Subject)) ##' ## => list( Days | Subject ) ##' findbars(y ~ Days + (1|Subject) + (0+Days|Subject)) ##' ## => list of length 2: list ( 1 | Subject , 0+Days|Subject) ##' findbars(~ 1 + (1|batch/cask)) ##' ## => list of length 2: list ( 1 | cask:batch , 1 | batch) ##' identical(findbars(~ 1 + (Days || Subject)), ##' findbars(~ 1 + (1|Subject) + (0+Days|Subject))) ##' \dontshow{ ##' stopifnot(identical(findbars(f1), ##' list(expression(Days | Subject)[[1]]))) ##' } ##' @family utilities ##' @keywords models utilities ##' @export findbars <- function(term) { ## Recursive function applied to individual terms fb <- function(term) { if (is.name(term) || !is.language(term)) return(NULL) if (term[[1]] == as.name("(")) return(fb(term[[2]])) stopifnot(is.call(term)) if (term[[1]] == as.name('|')) return(term) if (length(term) == 2) return(fb(term[[2]])) c(fb(term[[2]]), fb(term[[3]])) } ## Expand any slashes in the grouping factors returned by fb expandSlash <- function(bb) { ## Create the interaction terms for nested effects makeInteraction <- function(x) { if (length(x) < 2) return(x) trm1 <- makeInteraction(x[[1]]) trm11 <- if(is.list(trm1)) trm1[[1]] else trm1 list(substitute(foo:bar, list(foo=x[[2]], bar = trm11)), trm1) } ## Return the list of '/'-separated terms slashTerms <- function(x) { if (!("/" %in% all.names(x))) return(x) if (x[[1]] != as.name("/")) stop("unparseable formula for grouping factor") list(slashTerms(x[[2]]), slashTerms(x[[3]])) } if (!is.list(bb)) return(expandSlash(list(bb))) ## lapply(unlist(... - unlist returns a flattened list unlist(lapply(bb, function(x) { if (length(x) > 2 && is.list(trms <- slashTerms(x[[3]]))) return(lapply(unlist(makeInteraction(trms)), function(trm) substitute(foo|bar, list(foo = x[[2]], bar = trm)))) x })) } expandSlash(fb(term)) } ##' Remove the random-effects terms from a mixed-effects formula, ##' thereby producing the fixed-effects formula. ##' ##' @title Omit terms separated by vertical bars in a formula ##' @param term the right-hand side of a mixed-model formula ##' @return the fixed-effects part of the formula ##' @section Note: This function is called recursively on individual ##' terms in the model, which is why the argument is called \code{term} and not ##' a name like \code{form}, indicating a formula. ##' @examples ##' nobars(Reaction ~ Days + (Days|Subject)) ## => Reaction ~ Days ##' @seealso \code{\link{formula}}, \code{\link{model.frame}}, \code{\link{model.matrix}}. ##' @family utilities ##' @keywords models utilities ##' @export nobars <- function(term) { if (!('|' %in% all.names(term))) return(term) if (is.call(term) && term[[1]] == as.name('|')) return(NULL) if (length(term) == 2) { nb <- nobars(term[[2]]) if (is.null(nb)) return(NULL) term[[2]] <- nb return(term) } nb2 <- nobars(term[[2]]) nb3 <- nobars(term[[3]]) if (is.null(nb2)) return(nb3) if (is.null(nb3)) return(nb2) term[[2]] <- nb2 term[[3]] <- nb3 term } ##' Substitute the '+' function for the '|' function in a mixed-model ##' formula. This provides a formula suitable for the current ##' model.frame function. ##' ##' @title "Sub[stitute] Bars" ##' @param term a mixed-model formula ##' @return the formula with all | operators replaced by + ##' @section Note: This function is called recursively on individual ##' terms in the model, which is why the argument is called \code{term} and not ##' a name like \code{form}, indicating a formula. ##' @examples ##' subbars(Reaction ~ Days + (Days|Subject)) ## => Reaction ~ Days + (Days + Subject) ##' @seealso \code{\link{formula}}, \code{\link{model.frame}}, \code{\link{model.matrix}}. ##' @family utilities ##' @keywords models utilities ##' @export subbars <- function(term) { if (is.name(term) || !is.language(term)) return(term) if (length(term) == 2) { term[[2]] <- subbars(term[[2]]) return(term) } stopifnot(length(term) >= 3) if (is.call(term) && term[[1]] == as.name('|')) term[[1]] <- as.name('+') for (j in 2:length(term)) term[[j]] <- subbars(term[[j]]) term } ##' Does every level of f1 occur in conjunction with exactly one level ##' of f2? The function is based on converting a triplet sparse matrix ##' to a compressed column-oriented form in which the nesting can be ##' quickly evaluated. ##' ##' @title Is f1 nested within f2? ##' ##' @param f1 factor 1 ##' @param f2 factor 2 ##' ##' @return TRUE if factor 1 is nested within factor 2 ##' @examples ##' with(Pastes, isNested(cask, batch)) ## => FALSE ##' with(Pastes, isNested(sample, batch)) ## => TRUE ##' @export isNested <- function(f1, f2) { f1 <- as.factor(f1) f2 <- as.factor(f2) stopifnot(length(f1) == length(f2)) k <- length(levels(f1)) sm <- as(new("ngTMatrix", i = as.integer(f2) - 1L, j = as.integer(f1) - 1L, Dim = c(length(levels(f2)), k)), "CsparseMatrix") all(sm@p[2:(k+1L)] - sm@p[1:k] <= 1L) } subnms <- function(form, nms) { ## Recursive function applied to individual terms sbnm <- function(term) { if (is.name(term)) if (any(term == nms)) return(0) else return(term) switch(length(term), return(term), { term[[2]] <- sbnm(term[[2]]) return(term) }, { term[[2]] <- sbnm(term[[2]]) term[[3]] <- sbnm(term[[3]]) return(term) }) NULL } sbnm(form) } ## Check for a constant term (a literal 1) in an expression ## ## In the mixed-effects part of a nonlinear model formula, a constant ## term is not meaningful because every term must be relative to a ## nonlinear model parameter. This function recursively checks the ## expressions in the formula for a a constant, calling stop() if ## such a term is encountered. ## @title Check for constant terms. ## @param expr an expression ## @return NULL. The function is executed for its side effect. chck1 <- function(expr) { if ((le <- length(expr)) == 1) { if (is.numeric(expr) && expr == 1) stop("1 is not meaningful in a nonlinear model formula") return() } else for (j in seq_len(le)[-1]) Recall(expr[[j]]) } ##' Check and manipulate the formula for a nonlinear model. ##' ##' The model formula for a nonlinear mixed-effects model is of the form ##' \code{resp ~ nlmod ~ mixed} where \code{"resp"} is an expression ##' (usually just a name) for the response, \code{nlmod} is the ##' call to the nonlinear model function, and \code{mixed} is the ##' mixed-effects formula defining the linear predictor for the ##' parameter matrix. If the formula is to be used for optimizing ##' designs, the \code{"resp"} part can be omitted. ##' ##' ##' @title Manipulate a nonlinear model formula. ##' @param mc matched call from the calling function. Should have arguments named ##' \describe{ ##' \item{formula}{a formula of the form \code{resp ~ nlmod ~ meform} ##' where \code{resp} is an expression for the response, ##' \code{nlmod} is the nonlinear model expression and ##' \code{meform} is the mixed-effects model formula. \code{resp} ##' can be omitted when, e.g., optimizing a design.} ##' \item{data}{a data frame in which to evaluate the model function} ##' \item{start}{either a numeric vector containing initial estimates for the ##' nonlinear model parameters or a list with components ##' \describe{ ##' \item{nlpars}{the initial estimates of the nonlinear model parameters} ##' \item{theta}{the initial estimates of the variance component parameters} ##' } ##' } ##' } ##' @return a list with components ##' \item{"respMod"}{a response module of class \code{"\linkS4class{nlsResp}"}} ##' \item{"frame"}{the model frame, including a terms attribute} ##' \item{"X"}{the fixed-effects model matrix} ##' \item{"reTrms"}{the random-effects terms object} ##' @export ##' @family utilities nlformula <- function(mc) { start <- eval(mc$start, parent.frame(2L)) if (is.numeric(start)) start <- list(nlpars = start) stopifnot(is.numeric(nlpars <- start$nlpars), all(sapply(nlpars, length) == 1L), length(pnames <- names(nlpars)) == length(nlpars), length(form <- as.formula(mc$formula)) == 3L, is(nlform <- eval(form[[2]]), "formula"), all(pnames %in% (av <- all.vars(nlmod <- as.call(nlform[[lnl <- length(nlform)]]))))) nlform[[lnl]] <- parse(text= paste(setdiff(all.vars(form), pnames), collapse=' + '))[[1]] nlform <- eval(nlform) environment(nlform) <- environment(form) m <- match(c("data", "subset", "weights", "na.action", "offset"), names(mc), 0) mc <- mc[c(1, m)] mc$drop.unused.levels <- TRUE mc[[1]] <- as.name("model.frame") mc$formula <- nlform fr <- eval(mc, parent.frame(2L)) n <- nrow(fr) nlenv <- list2env(fr, parent=parent.frame(2L)) lapply(pnames, function(nm) nlenv[[nm]] <- rep.int(nlpars[[nm]], n)) respMod <- mkRespMod(fr, nlenv=nlenv, nlmod=nlmod) chck1(meform <- form[[3L]]) pnameexpr <- parse(text=paste(pnames, collapse='+'))[[1]] nb <- nobars(meform) fe <- eval(substitute(~ 0 + nb + pnameexpr)) environment(fe) <- environment(form) frE <- do.call(rbind, lapply(seq_along(nlpars), function(i) fr)) # rbind s copies of the frame for (nm in pnames) # convert these variables in fr to indicators frE[[nm]] <- as.numeric(rep(nm == pnames, each = n)) X <- model.matrix(fe, frE) rownames(X) <- NULL reTrms <- mkReTrms(lapply(findbars(meform), function(expr) { expr[[2]] <- substitute(0+foo, list(foo=expr[[2]])) expr }), frE) list(respMod=respMod, frame=fr, X=X, reTrms=reTrms, pnames=pnames) } ##' Create an object in a subclass of \code{\linkS4class{merMod}} ##' from the environment of the objective function and the value ##' returned by the optimizer. ##' ##' @title Create a merMod object ##' @param rho the environment of the objective function ##' @param opt the value returned by the optimizer ##' @param reTrms reTrms list from the calling function ##' @param fr model frame ##' @param mc matched call from the calling function ##' @return an object from a class that inherits from \code{\linkS4class{merMod}} ##' @export mkMerMod <- function(rho, opt, reTrms, fr, mc) { if(missing(mc)) mc <- match.call() stopifnot(is.environment(rho), is(pp <- rho$pp, "merPredD"), is(resp <- rho$resp, "lmResp"), is.list(opt), "par" %in% names(opt), all(c("conv","fval") %in% substr(names(opt),1,4)), ## "conv[ergence]", "fval[ues]" is.list(reTrms), all(c("flist", "cnms", "Gp", "lower") %in% names(reTrms))) rcl <- class(resp) n <- nrow(pp$V) p <- ncol(pp$V) dims <- c(N=nrow(pp$X), n=n, p=p, nmp=n-p, nth=length(pp$theta), q=nrow(pp$Zt), nAGQ=rho$nAGQ, compDev=rho$compDev, ## 'use scale' in the sense of whether dispersion parameter should ## be reported/used (*not* whether theta should be scaled by sigma) useSc=(rcl != "glmResp" || !resp$family$family %in% c("poisson","binomial")), reTrms=length(reTrms$cnms), spFe=0L, REML=if (rcl=="lmerResp") resp$REML else 0L, GLMM=(rcl=="glmResp"), NLMM=(rcl=="nlsResp")) storage.mode(dims) <- "integer" fac <- as.numeric(rcl != "nlsResp") sqrLenU <- pp$sqrL(fac) wrss <- resp$wrss() pwrss <- wrss + sqrLenU weights <- resp$weights beta <- pp$beta(fac) sigmaML <- pwrss/sum(weights) if (rcl != "lmerResp") { pars <- opt$par if (length(pars) > length(pp$theta)) beta <- pars[-(seq_along(pp$theta))] } cmp <- c(ldL2=pp$ldL2(), ldRX2=pp$ldRX2(), wrss=wrss, ussq=sqrLenU, pwrss=pwrss, drsum=if (rcl=="glmResp") resp$resDev() else NA, REML=if (rcl=="lmerResp" && resp$REML != 0L) opt$fval else NA, ## FIXME: construct 'REML deviance' here? dev=if (rcl=="lmerResp" && resp$REML != 0L) NA else opt$fval, sigmaML=sqrt(unname(if (!dims["useSc"]) NA else sigmaML)), sigmaREML=sqrt(unname(if (rcl!="lmerResp") NA else sigmaML*(dims['n']/dims['nmp']))), tolPwrss=rho$tolPwrss) # TODO: improve this hack to get something in frame slot (maybe need weights, etc...) if(missing(fr)) fr <- data.frame(resp$y) new(switch(rcl, lmerResp="lmerMod", glmResp="glmerMod", nlsResp="nlmerMod"), call=mc, frame=fr, flist=reTrms$flist, cnms=reTrms$cnms, Gp=reTrms$Gp, theta=pp$theta, beta=beta, u=pp$u(fac), lower=reTrms$lower, devcomp=list(cmp=cmp, dims=dims), pp=pp, resp=resp, optinfo=list(optimizer=attr(opt,"optimizer"), control=attr(opt,"control"), conv=opt$conv, warnings=attr(opt,"warnings")) ) } ## generic argument checking ## 'type': name of calling function ("glmer", "lmer", "nlmer") ## checkArgs <- function(type,...) { l... <- list(...) if (isTRUE(l...[["sparseX"]])) warning("sparseX = TRUE has no effect at present") ## '...' handling up front, safe-guarding against typos ("familiy") : if(length(l... <- list(...))) { if (!is.null(l...[["family"]])) { # call glmer if family specified ## we will only get here if 'family' is *not* in the arg list warning("calling lmer with family() is deprecated: please use glmer() instead") type <- "glmer" } ## Check for method argument which is no longer used ## (different meanings/hints depending on glmer vs lmer) if (!is.null(method <- l...[["method"]])) { msg <- paste("Argument", sQuote("method"), "is deprecated.") if (type=="lmer") msg <- paste(msg,"Use the REML argument to specify ML or REML estimation.") if (type=="glmer") msg <- paste(msg,"Use the nAGQ argument to specify Laplace (nAGQ=1) or adaptive", "Gauss-Hermite quadrature (nAGQ>1). PQL is no longer available.") warning(msg) l... <- l...[names(l...) != "method"] } if(length(l...)) { warning("extra argument(s) ", paste(sQuote(names(l...)), collapse=", "), " disregarded") } } } ## check formula and data: return an environment suitable for evaluating ## the formula. ## (1) if data is specified, return it ## (2) otherwise, if formula has an environment, use it ## (3) otherwise [e.g. if formula was passed as a string], try to use parent.frame(2) ## if #3 is true *and* the user is doing something tricky with nested functions, ## this may fail ... checkFormulaData <- function(formula,data,debug=FALSE) { dataName <- deparse(substitute(data)) missingData <- inherits(tryCatch(eval(data), error=function(e)e), "error") ## data not found (this *should* only happen with garbage input, ## OR when strings used as formulae -> drop1/update/etc.) ## ## alternate attempt (fails) ## ## ff <- sys.frames() ## ex <- substitute(data) ## ii <- rev(seq_along(ff)) ## for(i in ii) { ## ex <- eval(substitute(substitute(x, env=sys.frames()[[n]]), ## env = list(x = ex, n=i))) ## } ## origName <- deparse(ex) ## missingData <- !exists(origName) ## (!dataName=="NULL" && !exists(dataName)) if (missingData) { varex <- function(v,env) exists(v,envir=env,inherits=FALSE) allvars <- all.vars(as.formula(formula)) allvarex <- function(vvec=allvars,...) { all(sapply(vvec,varex,...)) } if (allvarex(env=(ee <- environment(formula)))) { stop("'data' not found, but variables found in environment of formula: ", "try specifying 'formula' as a formula rather ", "than a string in the original model") } else stop("'data' not found, and some variables missing from formula environment") } else { if (is.null(data)) { if (!is.null(ee <- environment(formula))) { ## use environment of formula denv <- ee } else { ## e.g. no environment, e.g. because formula is a character vector ## parent.frame(2L) works because [g]lFormula (our calling environment) ## has been called within [g]lmer with env=parent.frame(1L) ## If you call checkFormulaData in some other bizarre way such that ## parent.frame(2L) is *not* OK, you deserve what you get ## calling checkFormulaData directly from the global ## environment should be OK, since trying to go up beyond the global ## environment keeps bringing you back to the global environment ... denv <- parent.frame(2L) } } else { ## data specified denv <- list2env(data) } } ## FIXME: set enclosing environment of denv to environment(formula), or parent.frame(2L) ? if (debug) { cat("Debugging parent frames in checkFormulaData:\n") ## find global environment -- could do this with sys.nframe() ? glEnv <- 1 while (!identical(parent.frame(glEnv),.GlobalEnv)) { glEnv <- glEnv+1 } ## where are vars? for (i in 1:glEnv) { OK <- allvarex(env=parent.frame(i)) cat("vars exist in parent frame ",i) if (i==glEnv) cat(" (global)") cat(" ",OK,"\n") } cat("vars exist in env of formula ",allvarex(env=denv),"\n") } ## if (debug) stopifnot(length(as.formula(formula,env=denv)) == 3) ## check for two-sided formula return(denv) } ## checkFormulaData <- function(formula,data) { ## ee <- environment(formula) ## if (is.null(ee)) { ## ee <- parent.frame(2) ## } ## if (missing(data)) data <- ee ## stopifnot(length(as.formula(formula,env=as.environment(data))) == 3) ## return(data) ## } lme4/R/nbinom.R0000644000176000001440000000552412156422372012763 0ustar ripleyusers##' @importFrom MASS negative.binomial ##' @importFrom MASS theta.ml ## should be getME(object,"NBdisp") ? ## MM: should the *user* use it? if yes, consider method sigma() or AIC() ? getNBdisp <- function(object) { get(".Theta",envir=environment(object@resp$family$aic)) } ## should be setME(object,"NBdisp") ? setNBdisp <- function(object,theta) { ## assign(".Theta",theta,envir=environment(object@resp$family$aic)) ff <- setdiff(names(getRefClass("glmResp")$fields()),c("Ptr","family")) rr <- object@resp arg1 <- lapply(ff,rr$field) names(arg1) <- ff newresp <- do.call(glmResp$new, c(arg1, list(family=negative.binomial(theta=theta)))) newresp$setOffset(rr$offset) newresp$updateMu(rr$eta - rr$offset) object@resp <- newresp object } refitNB <- function(object,theta) { refit(setNBdisp(object,theta)) } optTheta <- function(object, interval=c(-5,5), maxit=20, verbose=FALSE) { lastfit <- object evalcnt <- 0 optval <- optimize(function(t) { ## FIXME: kluge to retain last value and evaluation count ## Perhaps use a reference class object to keep track of this ## auxilliary information? DB dev <- deviance(lastfit <<- refitNB(lastfit,theta=exp(t))) evalcnt <<- evalcnt+1 if (verbose) cat(evalcnt,exp(t),dev,"\n") dev }, interval=interval) stopifnot(all.equal(optval$minimum,log(getNBdisp(lastfit)))) ## FIXME: return eval count info somewhere else? MM: new slot there, why not? attr(lastfit,"nevals") <- evalcnt lastfit } ## use MASS machinery to estimate theta from residuals est_theta <- function(object) { Y <- model.response(model.frame(object)) mu <- fitted(object) w <- object@resp$weights control <- list(maxit=20,trace=0) th <- theta.ml(Y, mu, sum(w), w, limit = control$maxit, trace = control$trace > 2) } ## FIXME: really should document glmer.nb() on the same help page as glmer() ## I (MM) don't know how to use roxygen for that.. ##' glmer() for Negative Binomial ##' @param ... formula, data, etc: the arguments for \code{\link{glmer}(..)} (apart from \code{family}!). ##' @param interval interval in which to start the optimization ##' @param verbose logical indicating how much progress information should be printed. ##' @export glmer.nb <- function(..., interval = log(th)+c(-3,3), verbose=FALSE) { g0 <- glmer(..., family=poisson) th <- est_theta(g0) if(verbose) cat("th := est_theta(glmer(..)) =", format(th),"\n") g1 <- update(g0, family = negative.binomial(theta=th)) ## if (is.null(interval)) interval <- log(th)+c(-3,3) optTheta(g1, interval=interval, verbose=verbose) } ## do we want to facilitate profiling on theta?? ## save evaluations used in optimize() fit? ## ('memoise'?) ## Again, I think that a reference class object would be a better approach. lme4/R/bootMer.R0000644000176000001440000002225012232467515013106 0ustar ripleyusers.simpleCap <- function(x) { paste0(toupper(substr(x, 1,1)), substr(x, 2, 1000000L), collapse=" ") } ### bootMer() --- <==> (TODO: semi-*)parametric bootstrap ### ------------------------------------------------------- ## Doc: show how this is equivalent - but faster than ## boot(*, R = nsim, sim = "parametric", ran.gen = simulate(.,1,.), mle = x) ## --> return a "boot" object -- so we could use boot.ci() etc ## TODO: also allow "semi-parametric" model-based bootstrap: ## resampling the (centered!) residuals (for use.u=TRUE) or for use.u=FALSE, ## *both* the centered u's + centered residuals ## instead of using rnorm() ## BUT see: ## @article{morris_blups_2002, ## title = {The {BLUPs} are not "best" when it comes to bootstrapping}, ## volume = {56}, ## issn = {0167-7152}, ## url = {http://www.sciencedirect.com/science/article/pii/S016771520200041X}, ## doi = {10.1016/S0167-7152(02)00041-X}, ## journal = {Statistics \& Probability Letters}, ## author = {Morris, Jeffrey S}, ## year = {2002}, ## } ## for an indication of why this is not necessarily a good idea! ##' Model-based (Semi-)Parametric Bootstrap for Mixed Models ##' ##' Perform model-based (Semi-)parametric bootstrap for mixed models. ##' ##' The semi-parametric variant is not yet implemented, and we only ##' provide a method for \code{\link{lmer}} and \code{\link{glmer}} results. ##' ##' The working name for bootMer() was \dQuote{simulestimate()}, as it is an ##' extension of \code{\link{simulate}}, but we want to emphasize its ##' potential for valid inference. ##' ##' @param x fitted \code{*lmer()} model, see \code{\link{lmer}}, ##' \code{\link{glmer}}, etc. ##' @param FUN a \code{\link{function}(x)}, computating the \emph{statistic} of ##' interest, which must be a numeric vector, possibly named. ##' @param nsim number of simulations, positive integer; the bootstrap \eqn{B} ##' (or \eqn{R}). ##' @param seed optional argument to \code{\link{set.seed}}. ##' @param use.u logical, indicating whether the spherized random effects ##' should be simulated / bootstrapped as well. If \code{TRUE}, they are not ##' changed, and all inference is conditional on these values. If \code{FALSE}, ##' new normal deviates are drawn (see Details). ##' @param type character string specifying the type of bootstrap, ##' \code{"parametric"} or \code{"semiparametric"}; partial matching is allowed. ##' Note that the semiparametric bootstrap is currently an experimental feature, and therefore ##' may not be stable. ##' @param verbose logical indicating if progress should print output ##' @param .progress character string - type of progress bar to ##' display. Default is \code{"none"}; the function will look for a relevant \code{*ProgressBar} function, so \code{"txt"} will work in general; \code{"tk"} is available if the \code{tcltk} package is loaded; or \code{"win"} on Windows systems. ##' @param PBargs a list of additional arguments to the progress bar function (the package authors like \code{list(style=3)}). ##' @return an object of S3 \code{\link{class}} \code{"boot"}, compatible with ##' \pkg{boot} package's \code{\link[boot]{boot}()} result. ##' @seealso ##' \itemize{ ##' \item For inference, including confidence intervals, ##' \code{\link{profile-methods}}. ##' \item \code{\link[boot]{boot}()}, and then \code{\link[boot]{boot.ci}} from ##' package \pkg{boot}. ##' } ##' @details ##' \itemize{ ##' \item If \code{use.u} is \code{FALSE} and \code{type} is \code{"parametric"}, each simulation generates ##' new values of both the \dQuote{\emph{spherized}} random ##' effects \eqn{u} and the i.i.d. errors \eqn{\epsilon}, using ##' \code{\link{rnorm}()} with parameters corresponding to the fitted model ##' \code{x}. ##' \item If \code{use.u} is \code{TRUE} and \code{type=="parametric"}, only the i.i.d. errors ##' (or, for GLMMs, response values drawn from the appropriate distributions) ##' are resampled, with the values of \eqn{u} staying fixed at their ##' estimated values. ##' \item If \code{use.u} is \code{TRUE} and \code{type=="semiparametric"}, ##' the i.i.d. errors are sampled from the distribution of (response) residuals. ##' (For GLMMs, the resulting sample will no longer have the same ##' properties as the original sample, and the method may not make sense; ##' a warning is generated.) ##' Note that the semiparametric bootstrap is currently an experimental feature, and therefore ##' may not be stable. ##' \item The case where \code{use.u} is \code{FALSE} and \code{type=="semiparametric"} is not implemented; Morris (2002) suggests that resampling from the estimated values of \eqn{u} is not good practice. ##' } ##' @references ##' Davison, A.C. and Hinkley, D.V. (1997) ##' \emph{Bootstrap Methods and Their Application}. Cambridge University Press. ##' ##' Morris, J. S. (2002). ##' The BLUPs Are Not \sQuote{best} When It Comes to Bootstrapping. ##' \emph{Statistics & Probability Letters} \bold{56}(4): 425--430. ##' doi:10.1016/S0167-7152(02)00041-X. ##' @keywords models htest ##' @examples ##' fm01ML <- lmer(Yield ~ 1|Batch, Dyestuff, REML = FALSE) ##' ## see ?"profile-methods" ##' mySumm <- function(.) { s <- sigma(.) ##' c(beta =getME(., "beta"), sigma = s, sig01 = unname(s * getME(., "theta"))) } ##' (t0 <- mySumm(fm01ML)) # just three parameters ##' ## alternatively: ##' mySumm2 <- function(.) { ##' c(beta=fixef(.),sigma=sigma(.),sig01=unlist(VarCorr(.))) ##' } ##' ##' set.seed(101) ##' ## 3.8s (on a 5600 MIPS 64bit fast(year 2009) desktop "AMD Phenom(tm) II X4 925"): ##' system.time( boo01 <- bootMer(fm01ML, mySumm, nsim = 100) ) ##' ##' ## to "look" at it ##' require("boot") ## a recommended package, i.e. *must* be there ##' boo01 ##' ## note large estimated bias for sig01 ##' ## (~30% low, decreases _slightly_ for nsim = 1000) ##' ##' ## extract the bootstrapped values as a data frame ... ##' head(as.data.frame(boo01)) ##' ##' ## ------ Bootstrap-based confidence intervals ------------ ##' ##' ## intercept ##' (bCI.1 <- boot.ci(boo01, index=1, type=c("norm", "basic", "perc")))# beta ##' ##' ## Residual standard deviation - original scale: ##' (bCI.2 <- boot.ci(boo01, index=2, type=c("norm", "basic", "perc"))) ##' ## Residual SD - transform to log scale: ##' (bCI.2l <- boot.ci(boo01, index=2, type=c("norm", "basic", "perc"), ##' h = log, hdot = function(.) 1/., hinv = exp)) ##' ##' ## Among-batch variance: ##' (bCI.3 <- boot.ci(boo01, index=3, type=c("norm", "basic", "perc")))# sig01 ##' ##' ## Graphical examination: ##' plot(boo01,index=3) ##' ##' ## Check stored values from a longer (1000-replicate) run: ##' load(system.file("testdata","boo01L.RData",package="lme4")) ##' plot(boo01L,index=3) ##' ##' @export bootMer <- function(x, FUN, nsim = 1, seed = NULL, use.u = FALSE, type=c("parametric","semiparametric"), verbose = FALSE, .progress="none", PBargs=list()) { stopifnot((nsim <- as.integer(nsim[1])) > 0) if (.progress!="none") { ## progress bar pbfun <- get(paste0(.progress,"ProgressBar")) setpbfun <- get(paste0("set",.simpleCap(.progress),"ProgressBar")) pb <- do.call(pbfun,PBargs) } FUN <- match.fun(FUN) type <- match.arg(type) if(!is.null(seed)) set.seed(seed) else if(!exists(".Random.seed", envir = .GlobalEnv)) runif(1) # initialize the RNG if necessary mc <- match.call() t0 <- FUN(x) if (!is.numeric(t0)) stop("bootMer currently only handles functions that return numeric vectors") mle <- list(beta = getME(x,"beta"), theta = getME(x,"theta")) if (isLMM(x)) mle <- c(mle,list(sigma = sigma(x))) ## FIXME: what about GLMMs with scale parameters?? ## FIXME: remove prefix when incorporated in package if (type=="parametric") { ss <- simulate(x, nsim=nsim, use.u=use.u) } else { if (use.u) { if (isGLMM(x)) warning("semiparametric bootstrapping is questionable for GLMMs") ss <- replicate(nsim,fitted(x)+sample(residuals(x,"response")), simplify=FALSE) } else { stop("semiparametric bootstrapping with use.u=FALSE not yet implemented") } } t.star <- matrix(t0, nrow = length(t0), ncol = nsim) for(i in 1:nsim) { if (.progress!="none") { setpbfun(pb,i/nsim) } foo <- tryCatch(FUN(refit(x,ss[[i]])), error=function(e)e) if(verbose) { cat(sprintf("%5d :",i)); str(foo) } t.star[,i] <- if (inherits(foo, "error")) NA else foo } if (.progress!="none") { close(pb) } rownames(t.star) <- names(t0) if ((numFail <- sum(apply(is.na(t.star),2,all)))>0) { warning("some bootstrap runs failed (",numFail,"/",nsim,")") } ## boot() ends with the equivalent of ## structure(list(t0 = t0, t = t.star, R = R, data = data, seed = seed, ## statistic = statistic, sim = sim, call = call, ## ran.gen = ran.gen, mle = mle), ## class = "boot") structure(list(t0 = t0, t = t(t.star), R = nsim, data = x@frame, seed = .Random.seed, statistic = FUN, sim = "parametric", call = mc, ## these two are dummies ran.gen = "simulate(, 1, *)", mle = mle), class = "boot") } ## {bootMer} ##' @S3method as.data.frame boot as.data.frame.boot <- function(x,...) { as.data.frame(x$t) } lme4/R/optimizer.R0000644000176000001440000001112512204271665013516 0ustar ripleyusers##' @rdname NelderMead ##' @title Nelder-Mead optimization of parameters, possibly with box constraints ##' @param fn a function of a single numeric vector argument returning a numeric scalar ##' @param par numeric vector of starting values for the parameters. ##' @param lower numeric vector of lower bounds (elements may be \code{-Inf}). ##' @param upper numeric vector of upper bounds (elements may be \code{Inf}). ##' @param control a named list of control settings. Possible settings are ##' \describe{ ##' \item{iprint}{numeric scalar - frequency of printing evaluation information. ##' Defaults to 0 indicating no printing.} ##' \item{maxfun}{numeric scalar - maximum number of function evaluations allowed (default:10000).} ##' \item{FtolAbs}{numeric scalar - absolute tolerance on change in function values (default: 1e-5)} ##' \item{FtolRel}{numeric scalar - relative tolerance on change in function values (default:1e-15)} ##' \item{XtolRel}{numeric scalar - relative tolerance on change in parameter values (default: 1e-7)} ##' \item{MinfMax}{numeric scalar - maximum value of the minimum (default: .Machine$double.xmin)} ##' \item{xst}{numeric vector of initial step sizes to establish the simplex - ##' all elements must be non-zero (default: rep(0.02,length(par)))} ##' \item{xt}{numeric vector of tolerances on the parameters (default: xst*5e-4)} ##' \item{verbose}{numeric value: 0=no printing, 1=print every 20 evaluations, ##' 2=print every 10 evalutions, 3=print every evaluation. Sets \sQuote{iprint}, ##' if specified, but does not override it.} ##' } ##' ##' @return a list with 4 components ##' \item{fval}{numeric scalar - the minimum function value achieved} ##' \item{par}{numeric vector - the value of \code{x} providing the minimum} ##' \item{ierr}{integer scalar - error code (see below)} ##' \item{control}{list - the list of control settings after substituting for defaults} ##' @note ##' Return error codes (\code{ierr}): ##' \describe{ ##' \item{-4}{\code{nm_evals}: maximum evaluations reached} ##' \item{-3}{\code{nm_forced}: ?} ##' \item{-2}{\code{nm_nofeasible}: cannot generate a feasible simplex} ##' \item{-1}{\code{nm_x0notfeasible}: initial x is not feasible (?)} ##' } ##' @export Nelder_Mead <- function(fn, par, lower=rep.int(-Inf, n), upper=rep.int(Inf, n), control=list()) { n <- length(par) if (is.null(xst <- control[["xst"]])) xst <- rep.int(0.02,n) if (is.null(xt <- control[["xt"]])) xt <- xst*5e-4 control[["xst"]] <- control[["xt"]] <- NULL ## mapping between simpler 'verbose' setting (0=no printing, 1=20, 2=10, 3=1) ## and internal 'iprint' control (frequency of printing) if (is.null(verbose <- control[["verbose"]])) verbose <- 0 control[["verbose"]] <- NULL if (is.null(control[["iprint"]])) { control[["iprint"]] <- switch(as.character(min(as.numeric(verbose),3L)), "0"=0, "1"=20,"2"=10,"3"=1) } stopifnot(is.function(fn), length(formals(fn)) == 1L, (n <- length(par <- as.numeric(par))) == length(lower <- as.numeric(lower)), length(upper <- as.numeric(upper)) == n, length(xst <- as.numeric(xst)) == n, all(xst != 0), length(xt <- as.numeric(xt)) == n) nM <- NelderMead$new(lower=lower, upper=upper, x0=par, xst=xst, xt=xt) cc <- do.call(function(iprint=0L, maxfun=10000L, FtolAbs=1e-5, FtolRel=1e-15, XtolRel=1e-7, MinfMax=-.Machine$double.xmax, ...) { if (length(list(...))>0) warning("unused control arguments ignored") list(iprint=iprint, maxfun=maxfun, FtolAbs=FtolAbs, FtolRel=FtolRel, XtolRel=XtolRel, MinfMax=MinfMax) }, control) nM$setFtolAbs(cc$FtolAbs) nM$setFtolRel(cc$FtolRel) nM$setIprint(cc$iprint) nM$setMaxeval(cc$maxfun) nM$setMinfMax(cc$MinfMax) while ((nMres <- nM$newf(fn(nM$xeval()))) == 0L) {} cmsg <- "reached max evaluations" if (nMres==-4) { ## map max evals from error to warning cmsg <- warning(sprintf("failure to converge in %d evaluations",cc$maxfun)) nMres <- 4 } msgvec <- c("nm_forced","cannot generate a feasible simplex","initial x is not feasible", "active","minf_max","fcvg","xcvg", ## FIXME: names (see NelderMead_newf in external.cpp) cmsg) if (nMres<0) stop(msgvec[nMres+4]) cc <- c(cc,xst=xst,xt=xt) list(fval=nM$value(), par=nM$xpos(), convergence=pmin(nMres,0), message=msgvec[nMres+4], control=cc) } lme4/R/hooks.r0000644000176000001440000000011112232467515012652 0ustar ripleyusers.onUnload <- function(libpath) library.dynam.unload("lme4", libpath) lme4/R/AllGeneric.R0000644000176000001440000001146312156422372013505 0ustar ripleyusers## utilities, these *exported*: ##' @export getL setGeneric("getL", function(x) standardGeneric("getL")) fixed.effects <- function(object, ...) { ## fixed.effects was an alternative name for fixef .Deprecated("fixef") mCall = match.call() mCall[[1]] = as.name("fixef") eval(mCall, parent.frame()) } random.effects <- function(object, ...) { ## random.effects was an alternative name for ranef .Deprecated("ranef") mCall = match.call() mCall[[1]] = as.name("ranef") eval(mCall, parent.frame()) } ## Create a Markov chain Monte Carlo sample from the posterior ## distribution of the parameters ## ## ## @title Create an MCMC sample ## @param object a fitted model object ## @param n number of samples to generate. Defaults to 1; for real use values of 200-1000 are more typical ## @param verbose should verbose output be given? ## @param ... additional, optional arguments (not used) ## @return a Markov chain Monte Carlo sample as a matrix mcmcsamp <- function(object, n = 1L, verbose = FALSE, ...) UseMethod("mcmcsamp") ##' Extract the residual standard error from a fitted model. ##' ##' This is a generic function. At present the only methods are for mixed-effects ##' models of class \code{\linkS4class{merMod}}. ##' @title Extract residual standard error ##' @param object a fitted model. ##' @param ... additional, optional arguments. (None are used in the merMod method) ##' @return the residual standard error as a scalar ##' @export sigma <- function(object, ...) UseMethod("sigma") ##' Check characteristics of models: whether a model fit corresponds to a linear (LMM), generalized linear (GLMM), or ##' nonlinear (NLMM) mixed model, and whether a linear mixed model has been fitted by REML or not (\code{isREML(x)} ##' is always \code{FALSE} for GLMMs and NLMMs). ##' ##' These are generic functions. At present the only methods are for mixed-effects ##' models of class \code{\linkS4class{merMod}}. ##' @title Check characteristics of models ##' @param x a fitted model. ##' @param ... additional, optional arguments. (None are used in the merMod methods) ##' @return a logical value ##' @seealso getME ##' @examples ##' fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) ##' gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), ##' data = cbpp, family = binomial) ##' nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree, ##' Orange, start = c(Asym = 200, xmid = 725, scal = 350)) ##' ##' isLMM(fm1) ##' isGLMM(gm1) ##' ## check all : ##' is.MM <- function(x) c(LMM = isLMM(x), GLMM= isGLMM(x), NLMM= isNLMM(x)) ##' stopifnot(cbind(is.MM(fm1), is.MM(gm1), is.MM(nm1)) ##' == diag(rep(TRUE,3))) ##' @export isREML <- function(x, ...) UseMethod("isREML") ##' @rdname isREML ##' @export isLMM <- function(x, ...) UseMethod("isLMM") ##' @rdname isREML ##' @export isNLMM <- function(x, ...) UseMethod("isNLMM") ##' @rdname isREML ##' @export isGLMM <- function(x, ...) UseMethod("isGLMM") ##' Refit a model using the maximum likelihood criterion ##' ##' This function is primarily used to get a maximum likelihood fit of ##' a linear mixed-effects model for an \code{\link{anova}} comparison. ##' ##' @title Refit a model by maximum likelihood criterion ##' @param x a fitted model, usually of class \code{"\linkS4class{lmerMod}"}, ##' to be refit according to the maximum likelihood criterion ##' @param ... optional additional parameters. None are used at present. ##' @return an object like \code{x} but fit by maximum likelihood ##' @export refitML <- function(x, ...) UseMethod("refitML") ##' Refit a model with a different response vector ##' ##' Refit a model after modifying the response vector. This could be done using ##' an \code{\link{update}} method but this approach should be faster because ##' it bypasses the creation of the model representation and goes directly to ##' the optimization step. ##' @title Refit a model by maximum likelihood criterion ##' @param object a fitted model, usually of class \code{"\linkS4class{lmerMod}"}, ##' to be refit with a new response ##' @param newresp a numeric vector providing the new response. Must be ##' of the same length as the original response. ##' @param ... optional additional parameters. None are used at present. ##' @return an object like \code{x} but fit by maximum likelihood ##' @examples ##' ## using refit() to fit each column in a matrix of responses ##' set.seed(101) ##' Y <- matrix(rnorm(1000),ncol=10) ##' res <- list() ##' d <- data.frame(y=Y[,1],x=rnorm(100),f=rep(1:10,10)) ##' fit1 <- lmer(y~x+(1|f),data=d) ##' res <- c(fit1,lapply(as.data.frame(Y[,-1]), ##' refit,object=fit1)) ##' @export refit <- function(object, newresp, ...) UseMethod("refit") if (FALSE) { setGeneric("HPDinterval", function(object, prob = 0.95, ...) standardGeneric("HPDinterval")) } lme4/R/lmer.R0000644000176000001440000027723012273465633012454 0ustar ripleyusers## NB: doc in ../man/*.Rd ***not*** auto generated ## FIXME: need to document S3 methods better (can we pull from r-forge version?) ##' ##' Fit a linear mixed model (LMM) ##' ##' @title Fit Linear Mixed-Effects Models ##' @concept LMM ##' @aliases lmer ##' @param formula a two-sided linear formula object describing both the fixed-effects and ##' fixed-effects part of the model, with the response on the left of a ##' \code{~} operator and the terms, separated by \code{+} operators, on ##' the right. Random-effects terms are distinguished by vertical bars ##' (\code{"|"}) separating expressions for design matrices from ##' grouping factors. ##' @param data an optional data frame containing the variables named in ##' \code{formula}. By default the variables are taken from the environment ##' from which \code{lmer} is called. While \code{data} is optional, ##' the package authors \emph{strongly} recommend its use, ##' especially when later applying methods such as ##' \code{update} and \code{drop1} to the fitted model ##' (\emph{such methods are not guaranteed to work properly if \code{data} is omitted}). ##' If \code{data} is omitted, variables will be taken from the environment ##' of \code{formula} (if specified as a formula) or from the parent frame ##' (if specified as a character vector). ##' @param REML logical scalar - Should the estimates be chosen to optimize ##' the REML criterion (as opposed to the log-likelihood)? ##' @param control a list (of correct class, resulting from ##' \code{\link{lmerControl}()} or \code{\link{glmerControl}()} respectively) ##' containing control parameters, including the nonlinear optimizer to be ##' used and parameters to be passed through to the nonlinear optimizer, see ##' the \code{*lmerControl} documentation for details. ##' @param start a named list of starting values for the parameters in the ##' model. For \code{lmer} this can be a numeric vector or a list with one ##' component named \code{"theta"}. ##' @param verbose integer scalar. If \code{> 0} verbose output is generated ##' during the optimization of the parameter estimates. If \code{> 1} verbose ##' output is generated during the individual PIRLS steps. ##' @param subset an optional expression indicating the subset of the rows of ##' \code{data} that should be used in the fit. This can be a logical ##' vector, or a numeric vector indicating which observation numbers are ##' to be included, or a character vector of the row names to be ##' included. All observations are included by default. ##' @param weights an optional vector of \sQuote{prior weights} to be used in the ##' fitting process. Should be \code{NULL} or a numeric vector. ##' @param na.action a function that indicates what should happen when the ##' data contain \code{NA}s. The default action (\code{na.omit}, ##' inherited from the 'factory fresh' value of \code{getOption("na.action")}) ##' strips any observations with any missing values in any variables. ##' @param offset this can be used to specify an \emph{a priori} known component ##' to be included in the linear predictor during fitting. This should be ##' \code{NULL} or a numeric vector of length equal to the number of cases. ##' One or more \code{\link{offset}} terms can be included in the formula ##' instead or as well, and if more than one is specified their sum is used. ##' See \code{\link{model.offset}}. ##' @param contrasts an optional list. See the \code{contrasts.arg} of ##' \code{model.matrix.default}. ##' @param devFunOnly logical - return only the deviance evaluation function. Note that ##' because the deviance function operates on variables stored in its environment, ##' it may not return \emph{exactly} the same values on subsequent calls (but the results should always be within machine tolerance). ##' @param \dots other potential arguments. A \code{method} argument was used ##' in earlier versions of the package. Its functionality has been replaced by ##' the \code{REML} argument. ##' @return An object of class \code{merMod}, for which many ##' methods are available (e.g. \code{methods(class="merMod")}) ##' @seealso \code{\link[stats]{lm}} ##' @keywords models ##' @details ##' \itemize{ ##' \item{If the \code{formula} argument is specified as a character vector, ##' the function will attempt to coerce it to a formula. However, this is ##' not recommended (users who want to construct formulas by pasting together ##' components are advised to use \code{\link{as.formula}} or \code{\link{reformulate}}); model fits will ##' work but subsequent methods such as \code{\link{drop1}}, \code{\link{update}} ##' may fail.} ##' \item{Unlike some simpler modeling frameworks such as \code{\link{lm}} ##' and \code{\link{glm}} which automatically detect perfectly collinear ##' predictor variables, \code{[gn]lmer} cannot handle design matrices of ##' less than full rank. For example, in cases of models with interactions ##' that have unobserved combinations of levels, it is up to the user to ##' define a new variable (for example creating ##' \code{ab} within the data from the results of \code{interaction(a,b,drop=TRUE)}). ##' } ##' \item{the deviance function returned when \code{devFunOnly} is \code{TRUE} ##' takes a single numeric vector argument, representing the \code{theta} vector. ##' This vector defines the scaled variance-covariance matrices of the random effects, ##' in the Cholesky parameterization. For models with only simple (intercept-only) random effects, ##' \code{theta} is a vector of the standard deviations of the random effects. For more ##' complex or multiple random effects, running \code{getME(.,"theta")} to ##' retrieve the \code{theta} vector for a fitted model and examining the ##' names of the vector is probably the easiest way to determine the correspondence ##' between the elements of the \code{theta} vector and elements of the lower ##' triangles of the Cholesky factors of the random effects.} ##' } ##' @examples ##' ## linear mixed models - reference values from older code ##' (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) ##' fm1_ML <- update(fm1,REML=FALSE) ##' (fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) ##' anova(fm1, fm2) ##' @export ##' @importFrom minqa bobyqa lmer <- function(formula, data=NULL, REML = TRUE, control = lmerControl(), start = NULL, verbose = 0L, subset, weights, na.action, offset, contrasts = NULL, devFunOnly=FALSE, ...) { mc <- mcout <- match.call() missCtrl <- missing(control) ## see functions in modular.R for the body ... if (!missCtrl && !inherits(control, "lmerControl")) { if(!is.list(control)) stop("'control' is not a list; use lmerControl()") ## back-compatibility kluge warning("passing control as list is deprecated: please use lmerControl() instead", immediate.=TRUE) control <- do.call(lmerControl, control) } if (!is.null(list(...)[["family"]])) { warning("calling lmer with 'family' is deprecated; please use glmer() instead") mc[[1]] <- quote(lme4::glmer) if(missCtrl) mc$control <- glmerControl() return(eval(mc, parent.frame(1L))) } mc$control <- control ## update for back-compatibility kluge ## https://github.com/lme4/lme4/issues/50 ## parse data and formula mc[[1]] <- quote(lme4::lFormula) lmod <- eval(mc, parent.frame(1L)) ## parse data and formula mcout$formula <- lmod$formula lmod$formula <- NULL ## create deviance function for covariance parameters (theta) devfun <- do.call(mkLmerDevfun, c(lmod, list(start=start,verbose=verbose,control=control))) if (devFunOnly) return(devfun) ## optimize deviance function over covariance parameters opt <- optimizeLmer(devfun, optimizer=control$optimizer, restart_edge=control$restart_edge, control=control$optCtrl, verbose=verbose, start=start) mkMerMod(environment(devfun), opt, lmod$reTrms, fr = lmod$fr, mcout) ## prepare output }## { lmer } ##' Fit a generalized linear mixed model (GLMM) ##' ##' Fit a generalized linear mixed model, which incorporates both fixed-effects ##' parameters and random effects in a linear predictor, via maximum ##' likelihood. The linear predictor is related to the conditional ##' mean of the response through the inverse link function defined in ##' the GLM \code{family}. ##' ##' The expression for the likelihood of a mixed-effects model is an integral over ##' the random effects space. For a linear mixed-effects model (LMM), as fit by ##' \code{\link{lmer}}, this integral can be evaluated exactly. For a ##' GLMM the integral must be approximated. The most reliable ##' approximation for GLMMs with a single grouping factor for the ##' random effects is adaptive Gauss-Hermite quadrature. The ##' \code{nAGQ} argument controls the number of nodes in the ##' quadrature formula. A model with a single, scalar random-effects ##' term could reasonably use up to 25 quadrature points per scalar ##' integral. ##' ##' With vector-valued random effects the complexity of the ##' Gauss-Hermite quadrature formulas increases dramatically with the ##' dimension. For a 3-dimensional vector-valued random effect ##' \code{nAGQ=5} requires 93 evaluations of the GLM deviance per ##' evaluation of the approximate GLMM deviance. For 20-dimensional ## vector-valued random effects, \code{nAGQ=2} requires 41 ##' evaluations of the GLM deviance per evaluation of the approximate ##' GLMM deviance. ##' ##' The default approximation is the Laplace approximation, ##' corresponding to \code{nAGQ=1}. ##' ##' @title Fit Generalized Linear Mixed-Effects Models ##' @concept GLMM ##' @param family a GLM family, see \code{\link[stats]{glm}} and ##' \code{\link[stats]{family}}. ##' @param nAGQ integer scalar - the number of points per axis for evaluating ##' the adaptive Gauss-Hermite approximation to the log-likelihood. ##' Defaults to 1, corresponding to the Laplace ##' approximation. Values greater than 1 produce greater accuracy in ##' the evaluation of the log-likelihood at the expense of speed. A value ##' of zero uses a faster but less exact form of parameter estimation for ##' GLMMs by optimizing the random effects and the fixed-effects coefficients ##' in the penalized iteratively reweighted least squares step. ##' @param start a named list of starting values for the parameters in the ##' model, or a numeric vector. A numeric \code{start} argument ##' will be used as the starting value of \code{theta}. If \code{start} ##' is a list, the \code{theta} element (a numeric vector) is used ##' as the starting value for the first optimization step (default=1 ##' for diagonal elements and 0 for off-diagonal elements of the ##' lower Cholesky factor); the fitted value of \code{theta} from ##' the first step, plus \code{start[["fixef"]]}, ##' are used as starting values for the second optimization step. ##' If \code{start} has both \code{fixef} and \code{theta} ##' elements, the first optimization step is skipped. For more details ##' or finer control of optimization, see \code{\link{modular}}. ##' @param mustart optional starting values on the scale of the conditional mean, ##' as in \code{\link[stats]{glm}}; see there for details. ##' @param etastart optional starting values on the scale of the unbounded ##' predictor as in \code{\link[stats]{glm}}; see there for details. ##' @param \dots other potential arguments. A \code{method} argument was used ##' in earlier versions of the package. Its functionality has been replaced by ##' the \code{nAGQ} argument. ##' @inheritParams lmer ##' @return An object of class \code{glmerMod}, for which many ##' methods are available (e.g. \code{methods(class="glmerMod")}) ##' @seealso \code{\link{lmer}} (for details on formulas and parameterization); \code{\link[stats]{glm}} ##' @keywords models ##' @examples ##' ## generalized linear mixed model ##' library(lattice) ##' xyplot(incidence/size ~ period|herd, cbpp, type=c('g','p','l'), ##' layout=c(3,5), index.cond = function(x,y)max(y)) ##' (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), ##' data = cbpp, family = binomial)) ##' ## using nAGQ=0 only gets close to the optimum ##' (gm1a <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), ##' cbpp, binomial, nAGQ = 0)) ##' ## using nAGQ = 9 provides a better evaluation of the deviance ##' ## Currently the internal calculations use the sum of deviance residuals, ##' ## which is not directly comparable with the nAGQ=0 or nAGQ=1 result. ##' (gm1a <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), ##' cbpp, binomial, nAGQ = 9)) ##' ##' ## GLMM with individual-level variability (accounting for overdispersion) ##' ## For this data set the model is the same as one allowing for a period:herd ##' ## interaction, which the plot indicates could be needed. ##' cbpp$obs <- 1:nrow(cbpp) ##' (gm2 <- glmer(cbind(incidence, size - incidence) ~ period + ##' (1 | herd) + (1|obs), ##' family = binomial, data = cbpp)) ##' anova(gm1,gm2) ##' ##' ## glmer and glm log-likelihoods are consistent ##' gm1Devfun <- update(gm1,devFunOnly=TRUE) ##' gm0 <- glm(cbind(incidence, size - incidence) ~ period, ##' family = binomial, data = cbpp) ##' ## evaluate GLMM deviance at RE variance=theta=0, beta=(GLM coeffs) ##' gm1Dev0 <- gm1Devfun(c(0,coef(gm0))) ##' ## compare ##' stopifnot(all.equal(gm1Dev0,c(-2*logLik(gm0)))) ##' ##' @export glmer <- function(formula, data=NULL, family = gaussian, control = glmerControl(), start = NULL, verbose = 0L, nAGQ = 1L, subset, weights, na.action, offset, contrasts = NULL, mustart, etastart, devFunOnly = FALSE, ...) { if (!inherits(control, "glmerControl")) { if(!is.list(control)) stop("'control' is not a list; use glmerControl()") ## back-compatibility kluge msg <- "Use control=glmerControl(..) instead of passing a list" if(length(cl <- class(control))) msg <- paste(msg, "of class", dQuote(cl[1])) warning(msg, immediate.=TRUE) control <- do.call(glmerControl, control) } mc <- mcout <- match.call() ## family-checking code duplicated here and in glFormula (for now) since ## we really need to redirect at this point; eventually deprecate formally ## and clean up if (is.character(family)) family <- get(family, mode = "function", envir = parent.frame(2)) if( is.function(family)) family <- family() if (isTRUE(all.equal(family, gaussian()))) { ## redirect to lmer (with warning) warning("calling glmer() with family=gaussian (identity link) as a shortcut to lmer() is deprecated;", " please call lmer() directly") mc[[1]] <- quote(lme4::lmer) mc["family"] <- NULL # to avoid an infinite loop return(eval(mc, parent.frame())) } ## see https://github.com/lme4/lme4/issues/50 ## parse the formula and data mc[[1]] <- quote(lme4::glFormula) glmod <- eval(mc, parent.frame(1L)) mcout$formula <- glmod$formula glmod$formula <- NULL ## create deviance function for covariance parameters (theta) devfun <- do.call(mkGlmerDevfun, c(glmod, list(verbose=verbose, control=control, nAGQ = 0))) if (nAGQ==0 && devFunOnly) return(devfun) ## optimize deviance function over covariance parameters if (is.list(start) && !is.null(start$fixef)) if (nAGQ==0) stop("should not specify both start$fixef and nAGQ==0") opt <- optimizeGlmer(devfun, optimizer = control$optimizer[[1]], restart_edge=control$restart_edge, control = control$optCtrl, start=start, nAGQ = 0, verbose=verbose) if(nAGQ > 0L) { start <- updateStart(start,theta=opt$par) # update deviance function to include fixed effects as inputs devfun <- updateGlmerDevfun(devfun, glmod$reTrms, nAGQ = nAGQ) if (devFunOnly) return(devfun) # reoptimize deviance function over covariance parameters and fixed effects opt <- optimizeGlmer(devfun, optimizer = control$optimizer[[2]], restart_edge=control$restart_edge, control = control$optCtrl, start=start, nAGQ=nAGQ, verbose = verbose, stage=2) } # prepare output mkMerMod(environment(devfun), opt, glmod$reTrms, fr = glmod$fr, mcout) }## {glmer} ##' Fit a nonlinear mixed-effects model ##' ##' Fit nonlinear mixed-effects models, such as those used in ##' population pharmacokinetics. ##' @title Fit Nonlinear Mixed-Effects Models ##' @param formula a nonlinear mixed model formula (see detailed documentation) ##' @param start starting estimates for the nonlinear model ##' parameters, as a named numeric vector or as a list with components ##' \describe{ ##' \item{nlpars}{required numeric vector of starting values for the ##' nonlinear model parameters} ##' \item{theta}{optional numeric vector of starting values for the ##' covariance parameters} ##' } ##' @param \dots other potential arguments. A \code{method} argument was used ##' in earlier versions of the package. Its functionality has been replaced by ##' the \code{nAGQ} argument. ##' @note Adaptive Gauss-Hermite quadrature (\code{nAGQ}>1) is not currently implemented for \code{nlmer}. ##' @inheritParams glmer ##' @keywords models ##' @examples ##' ## nonlinear mixed models --- 3-part formulas --- ##' ##' (nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree, ##' Orange, start = c(Asym = 200, xmid = 725, scal = 350))) ##' (nm1a <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree, ##' Orange, start = c(Asym = 200, xmid = 725, scal = 350), ##' nAGQ = 0L)) ##' @export nlmer <- function(formula, data=NULL, control = nlmerControl(), start = NULL, verbose = 0L, nAGQ = 1L, subset, weights, na.action, offset, contrasts = NULL, devFunOnly = FALSE, ...) { vals <- nlformula(mc <- match.call()) p <- ncol(X <- vals$X) if ((rankX <- rankMatrix(X)) < p) stop(gettextf("rank of X = %d < ncol(X) = %d", rankX, p)) rho <- list2env(list(verbose=verbose, tolPwrss=0.001, # this is reset to the tolPwrss argument's value later resp=vals$resp, lower=vals$reTrms$lower), parent=parent.frame()) rho$pp <- do.call(merPredD$new, c(vals$reTrms[c("Zt","theta","Lambdat","Lind")], list(X=X, n=length(vals$respMod$mu), Xwts=vals$respMod$sqrtXwt, beta0=qr.coef(qr(X), unlist(lapply(vals$pnames, get, envir = rho$resp$nlenv)))))) rho$u0 <- rho$pp$u0 rho$beta0 <- rho$pp$beta0 devfun <- mkdevfun(rho, 0L, verbose, control) # deviance as a function of theta only if (devFunOnly && !nAGQ) return(devfun) devfun(rho$pp$theta) # initial coarse evaluation to get u0 and beta0 rho$u0 <- rho$pp$u0 rho$beta0 <- rho$pp$beta0 rho$tolPwrss <- control$tolPwrss # Reset control parameter (the initial optimization is coarse) opt <- optwrap(control$optimizer[[1]], devfun, rho$pp$theta, rho$lower, control=control$optCtrl, adj=FALSE) rho$control <- attr(opt,"control") if (nAGQ > 0L) { rho$lower <- c(rho$lower, rep.int(-Inf, length(rho$beta0))) rho$u0 <- rho$pp$u0 rho$beta0 <- rho$pp$beta0 rho$dpars <- seq_along(rho$pp$theta) if (nAGQ > 1L) { if (length(vals$reTrms$flist) != 1L || length(vals$reTrms$cnms[[1]]) != 1L) stop("nAGQ > 1 is only available for models with a single, scalar random-effects term") rho$fac <- vals$reTrms$flist[[1]] } devfun <- mkdevfun(rho, nAGQ, verbose, control) if (devFunOnly) return(devfun) opt <- optwrap(control$optimizer[[2]], devfun, par=c(rho$pp$theta, rho$beta0), lower=rho$lower, control=control$optCtrl, adj=TRUE, verbose=verbose) } mkMerMod(environment(devfun), opt, vals$reTrms, vals$frame, mc) }## {nlmer} ## R 3.1.0 devel [2013-08-05]: This does not help yet if(getRversion() >= "3.1.0") utils::suppressForeignCheck("nlmerAGQ") if(getRversion() < "3.1.0") dontCheck <- identity ##' Create a deviance evaluation function from a predictor and a response module ##' ##' From an merMod object create an R function that takes a single argument, ##' which is the new parameter value, and returns the deviance. ##' ##' The function returned by \code{mkdevfun} evaluates the deviance of the model ##' represented by the predictor module, \code{pp}, and the response module, ##' \code{resp}. ##' ##' For \code{\link{lmer}} model objects the argument of the resulting function ##' is the variance component parameter, \code{theta}, with lower bound. For ##' \code{glmer} or \code{nlmer} model objects with \code{nAGQ = 0} the argument ##' is also \code{theta}. However, when nAGQ > 0 the argument is \code{c(theta, ##' beta)}. ##' ##' @param rho an environment containing \code{pp}, a prediction module, ##' typically of class \code{\linkS4class{merPredD}} and \code{resp}, a response ##' module, e.g., of class \code{\linkS4class{lmerResp}}. ##' @param nAGQ scalar integer - the number of adaptive Gauss-Hermite quadrature ##' points. A value of 0 indicates that both the fixed-effects parameters ##' and the random effects are optimized by the iteratively reweighted least ##' squares algorithm. ##' @param verbose Logical: print verbose output? ##' @param control list of control parameters, a subset of those specified ##' by \code{\link{lmerControl}} (\code{tolPwrss} and \code{compDev} for GLMMs, ##' \code{tolPwrss} for NLMMs) ##' @return A function of one numeric argument. ##' @seealso \code{\link{lmer}}, \code{\link{glmer}} and \code{\link{nlmer}} ##' @keywords models ##' @examples ##' ##' (dd <- lmer(Yield ~ 1|Batch, Dyestuff, devFunOnly=TRUE)) ##' dd(0.8) ##' minqa::bobyqa(1, dd, 0) mkdevfun <- function(rho, nAGQ=1L, verbose=0, control=list()) { ## FIXME: should nAGQ be automatically embedded in rho? stopifnot(is.environment(rho), is(rho$resp, "lmResp")) ## silence R CMD check warnings *locally* in this function ## (clearly preferred to using globalVariables() !] fac <- pp <- resp <- lp0 <- compDev <- dpars <- baseOffset <- tolPwrss <- pwrssUpdate <- ## <-- even though it's a function below GQmat <- nlmerAGQ <- NULL ## The deviance function (to be returned): ff <- if (is(rho$resp, "lmerResp")) { rho$lmer_Deviance <- lmer_Deviance function(theta) .Call(lmer_Deviance, pp$ptr(), resp$ptr(), as.double(theta)) } else if (is(rho$resp, "glmResp")) { ## control values will override rho values *if present* if (!is.null(tp <- control$tolPwrss)) rho$tolPwrss <- tp if (!is.null(cd <- control$compDev)) rho$compDev <- cd if (nAGQ == 0L) function(theta) { resp$updateMu(lp0) pp$setTheta(theta) p <- pwrssUpdate(pp, resp, tolPwrss, GHrule(0L), compDev, verbose) resp$updateWts() p } else function(pars) { ## pp$setDelu(rep(0, length(pp$delu))) resp$setOffset(baseOffset) resp$updateMu(lp0) pp$setTheta(as.double(pars[dpars])) # theta is first part of pars spars <- as.numeric(pars[-dpars]) offset <- if (length(spars)==0) baseOffset else baseOffset + pp$X %*% spars resp$setOffset(offset) p <- pwrssUpdate(pp, resp, tolPwrss, GQmat, compDev, fac, verbose) resp$updateWts() p } } else if (is(rho$resp, "nlsResp")) { if (nAGQ < 2L) { rho$nlmerLaplace <- nlmerLaplace rho$tolPwrss <- control$tolPwrss switch(nAGQ + 1L, function(theta) .Call(nlmerLaplace, pp$ptr(), resp$ptr(), as.double(theta), as.double(u0), beta0, verbose, FALSE, tolPwrss), function(pars) .Call(nlmerLaplace, pp$ptr(), resp$ptr(), pars[dpars], u0, pars[-dpars], verbose, TRUE, tolPwrss)) } else { stop("AGQ>1 not yet implemented for nlmer models") rho$nlmerAGQ <- nlmerAGQ rho$GQmat <- GHrule(nAGQ) ## function(pars) { ## .Call(nlmerAGQ, ## <- dontCheck(nlmerAGQ) should work according to docs but does not ## pp$ptr(), resp$ptr(), fac, GQmat, pars[dpars], ## u0, pars[-dpars], tolPwrss) ##} } } else stop("code not yet written") environment(ff) <- rho ff } ## Determine a step factor that will reduce the pwrss ## ## The penalized, weighted residual sum of squares (pwrss) is the sum ## of the weighted residual sum of squares from the resp module and ## the squared length of u from the predictor module. The predictor module ## contains a base value and an increment for the coefficients. ## @title Determine a step factor ## @param pp predictor module ## @param resp response module ## @param verbose logical value determining verbose output ## @return NULL if successful ## @note Typically all this is done in the C++ code. ## The R code is for debugging and comparisons of ## results. ## stepFac <- function(pp, resp, verbose, maxSteps = 10) { ## stopifnot(is.numeric(maxSteps), maxSteps >= 2) ## pwrss0 <- resp$wrss() + pp$sqrL(0) ## for (fac in 2^(-(0:maxSteps))) { ## wrss <- resp$updateMu(pp$linPred(fac)) ## pwrss1 <- wrss + pp$sqrL(fac) ## if (verbose > 3L) ## cat(sprintf("pwrss0=%10g, diff=%10g, fac=%6.4f\n", ## pwrss0, pwrss0 - pwrss1, fac)) ## if (pwrss1 <= pwrss0) { ## pp$installPars(fac) ## return(NULL) ## } ## } ## stop("step factor reduced below ",signif(2^(-maxSteps),2)," without reducing pwrss") ## } RglmerWrkIter <- function(pp, resp, uOnly=FALSE) { pp$updateXwts(resp$sqrtWrkWt()) pp$updateDecomp() pp$updateRes(resp$wtWrkResp()) if (uOnly) pp$solveU() else pp$solve() resp$updateMu(pp$linPred(1)) # full increment resp$resDev() + pp$sqrL(1) } glmerPwrssUpdate <- function(pp, resp, tol, GQmat, compDev=TRUE, grpFac=NULL, verbose=0) { nAGQ <- nrow(GQmat) if (compDev) { if (nAGQ < 2L) return(.Call(glmerLaplace, pp$ptr(), resp$ptr(), nAGQ, tol, verbose)) return(.Call(glmerAGQ, pp$ptr(), resp$ptr(), tol, GQmat, grpFac, verbose)) } oldpdev <- .Machine$double.xmax uOnly <- nAGQ == 0L i <- 0 repeat { ## oldu <- pp$delu ## olddelb <- pp$delb pdev <- RglmerWrkIter(pp, resp, uOnly=uOnly) if (verbose>2) cat(i,": ",pdev,"\n",sep="") ## check convergence first so small increases don't trigger errors if (is.na(pdev)) stop("encountered NA in PWRSS update") if (abs((oldpdev - pdev) / pdev) < tol) break ## if (pdev > oldpdev) { ## ## try step-halving ## ## browser() ## k <- 0 ## while (k < 10 && pdev > oldpdev) { ## pp$setDelu((oldu + pp$delu)/2.) ## if (!uOnly) pp$setDelb((olddelb + pp$delb)/2.) ## pdev <- RglmerWrkIter(pp, resp, uOnly=uOnly) ## k <- k+1 ## } ## } if (pdev>oldpdev) stop("PIRLS update failed") oldpdev <- pdev i <- i+1 } resp$Laplace(pp$ldL2(), 0., pp$sqrL(1)) ## FIXME: should 0. be pp$ldRX2 ? } ## create a deviance evaluation function that uses the sigma parameters ## df2 <- function(dd) { ## stopifnot(is.function(dd), ## length(formals(dd)) == 1L, ## is((rem <- (rho <- environment(dd))$rem), "Rcpp_reModule"), ## is((fem <- rho$fem), "Rcpp_deFeMod"), ## is((resp <- rho$resp), "Rcpp_lmerResp"), ## all((lower <- rem$lower) == 0)) ## Lind <- rem$Lind ## n <- length(resp$y) ## function(pars) { ## sigma <- pars[1] ## sigsq <- sigma * sigma ## sigmas <- pars[-1] ## theta <- sigmas/sigma ## rem$theta <- theta ## resp$updateMu(numeric(n)) ## solveBetaU(rem, fem, resp$sqrtXwt, resp$wtres) ## resp$updateMu(rem$linPred1(1) + fem$linPred1(1)) ## n * log(2*pi*sigsq) + (resp$wrss + rem$sqrLenU)/sigsq + rem$ldL2 ## } ## } ## bootMer() ---> now in ./bootMer.R ## Methods for the merMod class ## Anova for merMod objects ## ## @title anova() for merMod objects ## @param a merMod object ## @param ... further such objects ## @return an "anova" data frame; the traditional (S3) result of anova() anovaLmer <- function(object, ...) { mCall <- match.call(expand.dots = TRUE) dots <- list(...) .sapply <- function(L, FUN, ...) unlist(lapply(L, FUN, ...)) modp <- as.logical(.sapply(dots, is, "merMod")) | as.logical(.sapply(dots, is, "lm")) if (any(modp)) { # multiple models - form table opts <- dots[!modp] mods <- c(list(object), dots[modp]) ## model names mNms <- .sapply(as.list(mCall)[c(FALSE, TRUE, modp)], deparse) ## HACK to try to identify model names in situations such as ## 'do.call(anova,list(model1,model2))' where the model names ## are lost in the call stack ... this doesn't quite work but might ## be useful for future attempts? ## maxdepth <- -2 ## depth <- -1 ## while (depth>=maxdepth & ## all(grepl("S4 object of class structure",mNms))) { ## xCall <- match.call(call=sys.call(depth)) ## mNms <- .sapply(as.list(xCall)[c(FALSE, TRUE, modp)], deparse) ## depth <- depth-1 ## } ## if (depthChisq)" = pchisq(chisq, dfChisq, lower.tail = FALSE), row.names = names(mods), check.names = FALSE) class(val) <- c("anova", class(val)) attr(val, "heading") <- c(header, "Models:", paste(rep(names(mods), times = unlist(lapply(lapply(lapply(calls, "[[", "formula"), deparse), length))), unlist(lapply(lapply(calls, "[[", "formula"), deparse)), sep = ": ")) return(val) } else { ## ------ single model --------------------- dc <- getME(object, "devcomp") p <- dc$dims["p"] X <- getME(object, "X") asgn <- attr(X, "assign") stopifnot(length(asgn) == (p <- dc$dims["p"])) ss <- as.vector(object@pp$RX() %*% object@beta)^2 names(ss) <- colnames(X) terms <- terms(object) nmeffects <- attr(terms, "term.labels") if ("(Intercept)" %in% names(ss)) nmeffects <- c("(Intercept)", nmeffects) ss <- unlist(lapply(split(ss, asgn), sum)) stopifnot(length(ss) == length(nmeffects)) df <- vapply(split(asgn, asgn), length, 1L) ## dfr <- unlist(lapply(split(dfr, asgn), function(x) x[1])) ms <- ss/df f <- ms/(sigma(object)^2) ## P <- pf(f, df, dfr, lower.tail = FALSE) ## table <- data.frame(df, ss, ms, dfr, f, P) table <- data.frame(df, ss, ms, f) dimnames(table) <- list(nmeffects, ## c("Df", "Sum Sq", "Mean Sq", "Denom", "F value", "Pr(>F)")) c("Df", "Sum Sq", "Mean Sq", "F value")) if ("(Intercept)" %in% nmeffects) table <- table[-match("(Intercept)", nmeffects), ] attr(table, "heading") <- "Analysis of Variance Table" class(table) <- c("anova", "data.frame") table } }## {anovaLmer} ##' @importFrom stats anova ##' @S3method anova merMod anova.merMod <- anovaLmer ##' @S3method as.function merMod as.function.merMod <- function(x, ...) { rho <- list2env(list(resp=x@resp$copy(), pp=x@pp$copy(), beta0=x@beta, u0=x@u), parent=as.environment("package:lme4")) ## FIXME: extract verbose and control mkdevfun(rho, getME(x, "devcomp")$dims["nAGQ"]) } ## coef() method for all kinds of "mer", "*merMod", ... objects ## ------ should work with fixef() + ranef() alone coefMer <- function(object, ...) { if (length(list(...))) warning('arguments named "', paste(names(list(...)), collapse = ", "), '" ignored') fef <- data.frame(rbind(fixef(object)), check.names = FALSE) ref <- ranef(object) ## check for variables in RE but missing from FE, fill in zeros in FE accordingly refnames <- unlist(lapply(ref,colnames)) nmiss <- length(missnames <- setdiff(refnames,names(fef))) if (nmiss >0) { fillvars <- setNames(data.frame(rbind(rep(0,nmiss))),missnames) fef <- cbind(fillvars,fef) } val <- lapply(ref, function(x) fef[rep.int(1L, nrow(x)),,drop = FALSE]) for (i in seq(a = val)) { refi <- ref[[i]] row.names(val[[i]]) <- row.names(refi) nmsi <- colnames(refi) if (!all(nmsi %in% names(fef))) stop("unable to align random and fixed effects") for (nm in nmsi) val[[i]][[nm]] <- val[[i]][[nm]] + refi[,nm] } class(val) <- "coef.mer" val } ## {coefMer} ##' @importFrom stats coef ##' @S3method coef merMod coef.merMod <- coefMer ## FIXME: should these values (i.e. ML criterion for REML models ## and vice versa) be computed and stored in the object in the first place? ##' @importFrom stats deviance ##' @S3method deviance merMod deviance.merMod <- function(object, REML = NULL, ...) { ## cf. (1) lmerResp::Laplace in respModule.cpp ## (2) section 5.6 of lMMwR, listing lines 34-42 if (isTRUE(REML) && !isLMM(object)) stop("can't compute REML deviance for a non-LMM") cmp <- object@devcomp$cmp if (is.null(REML) || is.na(REML[1])) REML <- isREML(object) if (REML) { if (isREML(object)) { cmp["REML"] } else { ## adjust ML results to REML lnum <- log(2*pi*(cmp["pwrss"])) n <- object@devcomp$dims["n"] nmp <- n-length(object@beta) unname(cmp["ldL2"]+cmp["ldRX2"]+nmp*(1.+lnum-log(nmp))) } } else { if (!isREML(object)) { cmp[["dev"]] } else { ## adjust REML results to ML n <- object@devcomp$dims["n"] lnum <- log(2*pi*(cmp["pwrss"])) unname(cmp["ldL2"]+n*(1+lnum-log(n))) } } } ## copied from stats:::safe_pchisq safe_pchisq <- function (q, df, ...) { df[df <= 0] <- NA pchisq(q = q, df = df, ...) } ##' @importFrom stats drop1 ##' @S3method drop1 merMod drop1.merMod <- function(object, scope, scale = 0, test = c("none", "Chisq"), k = 2, trace = FALSE, evalhack="formulaenv", ...) { ## FIXME: incorporate na.predict() stuff? tl <- attr(terms(object), "term.labels") if(missing(scope)) scope <- drop.scope(object) else { if(!is.character(scope)) { scope <- attr(terms(getFixedFormula(update.formula(object, scope))), "term.labels") } if(!all(match(scope, tl, 0L) > 0L)) stop("scope is not a subset of term labels") } ns <- length(scope) ans <- matrix(nrow = ns + 1L, ncol = 2L, dimnames = list(c("", scope), c("df", "AIC"))) ans[1, ] <- extractAIC(object, scale, k = k, ...) n0 <- nobs(object, use.fallback = TRUE) env <- environment(formula(object)) # perhaps here is where trouble begins?? for(i in seq_along(scope)) { ## was seq(ns), failed on empty scope tt <- scope[i] if(trace > 1) { cat("trying -", tt, "\n", sep='') utils::flush.console() } ## FIXME: make this more robust, somehow? ## three choices explored so far: ## (1) evaluate nfit in parent frame: tests in inst/tests/test-formulaEval.R ## will fail on lapply(m_data_List,drop1) ## (formula environment contains r,x,y,z but not d) ## (2) evaluate nfit in frame of formula: tests will fail when data specified and formula is character ## (3) update with data=NULL: fails when ... ## if (evalhack %in% c("parent","formulaenv")) { nfit <- update(object, as.formula(paste("~ . -", tt)), evaluate = FALSE) ## nfit <- eval(nfit, envir = env) # was eval.parent(nfit) if (evalhack=="parent") { nfit <- eval.parent(nfit) } else if (evalhack=="formulaenv") { nfit <- eval(nfit,envir=env) } } else { nfit <- update(object, as.formula(paste("~ . -", tt)),data=NULL, evaluate = FALSE) nfit <- eval(nfit,envir=env) } ans[i+1, ] <- extractAIC(nfit, scale, k = k, ...) nnew <- nobs(nfit, use.fallback = TRUE) if(all(is.finite(c(n0, nnew))) && nnew != n0) stop("number of rows in use has changed: remove missing values?") } dfs <- ans[1L , 1L] - ans[, 1L] dfs[1L] <- NA aod <- data.frame(Df = dfs, AIC = ans[,2]) test <- match.arg(test) if(test == "Chisq") { ## reconstruct deviance from AIC (ugh) dev <- ans[, 2L] - k*ans[, 1L] dev <- dev - dev[1L] ; dev[1L] <- NA nas <- !is.na(dev) P <- dev ## BMB: hack to extract safe_pchisq P[nas] <- safe_pchisq(dev[nas], dfs[nas], lower.tail = FALSE) aod[, c("LRT", "Pr(Chi)")] <- list(dev, P) } else if (test == "F") { ## FIXME: allow this if denominator df are specified externally? stop("F test STUB -- unfinished maybe forever") dev <- ans[, 2L] - k*ans[, 1L] dev <- dev - dev[1L] ; dev[1L] <- NA nas <- !is.na(dev) P <- dev ## BMB: hack to extract safe_pchisq P[nas] <- safe_pchisq(dev[nas], dfs[nas], lower.tail = FALSE) aod[, c("LRT", "Pr(F)")] <- list(dev, P) } head <- c("Single term deletions", "\nModel:", deparse(formula(object)), if(scale > 0) paste("\nscale: ", format(scale), "\n")) class(aod) <- c("anova", "data.frame") attr(aod, "heading") <- head aod } ##' @importFrom stats extractAIC ##' @S3method extractAIC merMod extractAIC.merMod <- function(fit, scale = 0, k = 2, ...) { L <- logLik(refitML(fit)) edf <- attr(L,"df") c(edf,-2*L + k*edf) } ##' @importFrom stats family ##' @S3method family merMod family.merMod <- function(object, ...) family(object@resp, ...) ##' @S3method family glmResp family.glmResp <- function(object, ...) object$family ##' @S3method family lmResp family.lmResp <- function(object, ...) gaussian() ##' @S3method family nlsResp family.nlsResp <- function(object, ...) gaussian() ##' @importFrom stats fitted ##' @S3method fitted merMod fitted.merMod <- function(object, ...) object@resp$mu ##' Extract the fixed-effects estimates ##' ##' Extract the estimates of the fixed-effects parameters from a fitted model. ##' @name fixef ##' @title Extract fixed-effects estimates ##' @aliases fixef fixed.effects fixef.merMod ##' @docType methods ##' @param object any fitted model object from which fixed effects estimates can ##' be extracted. ##' @param \dots optional additional arguments. Currently none are used in any ##' methods. ##' @return a named, numeric vector of fixed-effects estimates. ##' @keywords models ##' @examples ##' fixef(lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) ##' @importFrom nlme fixef ##' @export fixef ##' @method fixef merMod ##' @export fixef.merMod <- function(object, ...) structure(object@beta, names = dimnames(object@pp$X)[[2]]) getFixedFormula <- function(form) { form[[3]] <- if (is.null(nb <- nobars(form[[3]]))) 1 else nb form } ##' @importFrom stats formula ##' @S3method formula merMod formula.merMod <- function(x, fixed.only=FALSE, ...) { if (is.null(form <- attr(x@frame,"formula"))) { if (!grepl("lmer$",deparse(getCall(x)[[1]]))) stop("can't find formula stored in model frame or call") form <- as.formula(formula(getCall(x),...)) } if (fixed.only) { form <- getFixedFormula(form) } form } ##' @S3method isREML merMod isREML.merMod <- function(x, ...) as.logical(x@devcomp$dims["REML"]) ##' @S3method isGLMM merMod isGLMM.merMod <- function(x,...) { as.logical(x@devcomp$dims[["GLMM"]]) ## or: is(x@resp,"glmResp") } ##' @S3method isNLMM merMod isNLMM.merMod <- function(x,...) { as.logical(x@devcomp$dims[["NLMM"]]) ## or: is(x@resp,"nlsResp") } ##' @S3method isLMM merMod isLMM.merMod <- function(x,...) { !isGLMM(x) && !isNLMM(x) ## or: is(x@resp,"lmerResp") ? } ##' @importFrom stats logLik ##' @S3method logLik merMod logLik.merMod <- function(object, REML = NULL, ...) { if (is.null(REML) || is.na(REML[1])) REML <- isREML(object) val <- -deviance(object, REML = REML)/2 dc <- object@devcomp dims <- dc$dims nobs <- nrow(object@frame) ## FIXME use nobs() ? structure(val, nobs = nobs, nall = nobs, df = length(object@beta) + length(object@theta) + dims[["useSc"]], class = "logLik") } stripwhite <- function(x) gsub("(^ +| +$)","",x) ##' @importFrom stats logLik ##' @S3method model.frame merMod model.frame.merMod <- function(formula, fixed.only=FALSE, ...) { fr <- formula@frame if (fixed.only) { ff <- formula(formula,fixed.only=TRUE) ## thanks to Thomas Leeper and Roman LuÅ¡trik, Stack Overflow vars <- rownames(attr(terms.formula(ff), "factors")) fr <- fr[vars] } fr } ##' @importFrom stats model.matrix ##' @S3method model.matrix merMod model.matrix.merMod <- function(object, ...) object@pp$X ##' @importFrom stats nobs ##' @S3method nobs merMod nobs.merMod <- function(object, ...) nrow(object@frame) ##' @importFrom nlme ranef ##' @export ranef NULL ##' Extract the modes of the random effects ##' ##' A generic function to extract the conditional modes of the random effects ##' from a fitted model object. For linear mixed models the conditional modes ##' of the random effects are also the conditional means. ##' ##' If grouping factor i has k levels and j random effects per level the ith ##' component of the list returned by \code{ranef} is a data frame with k rows ##' and j columns. If \code{condVar} is \code{TRUE} the \code{"postVar"} ##' attribute is an array of dimension j by j by k. The kth face of this array ##' is a positive definite symmetric j by j matrix. If there is only one ##' grouping factor in the model the variance-covariance matrix for the entire ##' random effects vector, conditional on the estimates of the model parameters ##' and on the data will be block diagonal and this j by j matrix is the kth ##' diagonal block. With multiple grouping factors the faces of the ##' \code{"postVar"} attributes are still the diagonal blocks of this ##' conditional variance-covariance matrix but the matrix itself is no longer ##' block diagonal. ##' @name ranef ##' @aliases ranef ranef.merMod ##' @param object an object of a class of fitted models with random effects, ##' typically an \code{"\linkS4class{merMod}"} object. ##' @param condVar an optional logical argument indicating if the conditional ##' variance-covariance matrices of the random effects should be added as an attribute. ##' @param postVar a (deprecated) synonym for \code{condVar} ##' @param drop an optional logical argument indicating components of the return ##' value that would be data frames with a single column, usually a column ##' called \sQuote{\code{(Intercept)}}, should be returned as named vectors. ##' @param whichel an optional character vector of names of grouping factors for ##' which the random effects should be returned. Defaults to all the grouping ##' factors. ##' @param \dots some methods for this generic function require additional ##' arguments. ##' @return A list of data frames, one for each grouping factor for the random ##' effects. The number of rows in the data frame is the number of levels of ##' the grouping factor. The number of columns is the dimension of the random ##' effect associated with each level of the factor. ##' ##' If \code{condVar} is \code{TRUE} each of the data frames has an attribute ##' called \code{"postVar"} which is a three-dimensional array with symmetric ##' faces. ##' ##' When \code{drop} is \code{TRUE} any components that would be data frames of ##' a single column are converted to named numeric vectors. ##' @note To produce a \dQuote{caterpillar plot} of the random effects apply ##' \code{\link[lattice:xyplot]{dotplot}} to the result of a call to ##' \code{ranef} with \code{condVar = TRUE}. ##' @examples ##' fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) ##' fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy) ##' fm3 <- lmer(diameter ~ (1|plate) + (1|sample), Penicillin) ##' ranef(fm1) ##' str(rr1 <- ranef(fm1, condVar = TRUE)) ##' dotplot(rr1) ## default ##' ## specify free scales in order to make Day effects more visible ##' dotplot(rr1,scales = list(x = list(relation = 'free')))[["Subject"]] ##' if(FALSE) { ##-- condVar=TRUE is not yet implemented for multiple terms -- FIXME ##' str(ranef(fm2, condVar = TRUE)) ##' } ##' op <- options(digits = 4) ##' ranef(fm3, drop = TRUE) ##' options(op) ##' @keywords models methods ##' @method ranef merMod ##' @export ranef.merMod <- function(object, condVar = FALSE, drop = FALSE, whichel = names(ans), postVar = FALSE, ...) { if (!missing(postVar) && missing(condVar)) { warning(sQuote("postVar")," is deprecated: please use ", sQuote("condVar")," instead") condVar <- postVar } ans <- object@pp$b(1.) if (!is.null(object@flist)) { ## evaluate the list of matrices levs <- lapply(fl <- object@flist, levels) asgn <- attr(fl, "assign") cnms <- object@cnms nc <- vapply(cnms, length, 1L) nb <- nc * (nl <- vapply(levs, length, 1L)[asgn]) nbseq <- rep.int(seq_along(nb), nb) ml <- split(ans, nbseq) for (i in seq_along(ml)) ml[[i]] <- matrix(ml[[i]], ncol = nc[i], byrow = TRUE, dimnames = list(NULL, cnms[[i]])) ## create a list of data frames corresponding to factors ans <- lapply(seq_along(fl), function(i) data.frame(do.call(cbind, ml[asgn == i]), row.names = levs[[i]], check.names = FALSE)) names(ans) <- names(fl) # process whichel stopifnot(is(whichel, "character")) whchL <- names(ans) %in% whichel ans <- ans[whchL] if (condVar) { sigsqr <- sigma(object)^2 vv <- .Call(merPredDcondVar, object@pp$ptr(), as.environment(rePos$new(object))) for (i in names(ans)) ## seq_along(ans)) attr(ans[[i]], "postVar") <- vv[[i]] * sigsqr } if (drop) ans <- lapply(ans, function(el) { if (ncol(el) > 1) return(el) pv <- drop(attr(el, "postVar")) el <- drop(as.matrix(el)) if (!is.null(pv)) attr(el, "postVar") <- pv el }) class(ans) <- "ranef.mer" } ans }## ranef.merMod ##' @method refit merMod ##' @rdname refit ##' @export refit.merMod <- function(object, newresp=NULL, rename.response=FALSE, ...) { if (length(list(...))>0) warning("additional arguments to refit.merMod ignored") newrespSub <- substitute(newresp) ## for backward compatibility/functioning of refit(fit,simulate(fit)) if (is.list(newresp)) { if (length(newresp)==1) { newresp <- newresp[[1]] } else { stop("refit not implemented for lists with length>1: ", "consider ",sQuote("lapply(object,refit)")) } } rr <- object@resp$copy() if (!is.null(newresp)) { ## update call and model frame with new response rcol <- attr(attr(mf <- model.frame(object),"terms"),"response") if (rename.response) { attr(object@frame,"formula")[[2]] <- object@call$formula[[2]] <- newrespSub names(object@frame)[rcol] <- deparse(newrespSub) } if (!is.null(na.act <- attr(object@frame,"na.action"))) { ## will only get here if na.action is 'na.omit' or 'na.exclude' if (is.matrix(newresp)) { newresp <- newresp[-na.act,] } else newresp <- newresp[-na.act] } object@frame[,rcol] <- newresp if (isGLMM(object) && rr$family$family=="binomial") { ## re-do conversion of two-column matrix and factor ## responses to proportion/weights format if (is.matrix(newresp) && ncol(newresp)==2) { ntot <- rowSums(newresp) ## FIXME: test what happens for (0,0) rows newresp <- newresp[,1]/ntot rr$setWeights(ntot) } if (is.factor(newresp)) { ## FIXME: would be better to do this consistently with ## whatever machinery is used in glm/glm.fit/glmer ... ?? newresp <- as.numeric(newresp)-1 } } stopifnot(length(newresp <- as.numeric(as.vector(newresp))) == length(rr$y)) rr$setResp(newresp) } pp <- object@pp$copy() dc <- object@devcomp nAGQ <- dc$dims["nAGQ"] nth <- dc$dims["nth"] verbose <- list(...)$verbose if (is.null(verbose)) verbose <- 0L devlist <- list(pp=pp, resp=rr, u0=pp$u0, verbose=verbose, dpars=seq_len(nth)) if (isGLMM(object)) { baseOffset <- object@resp$offset devlist <- c(list(tolPwrss=unname(dc$cmp["tolPwrss"]), compDev=unname(dc$dims["compDev"]), nAGQ=unname(nAGQ), lp0=object@resp$eta - baseOffset, baseOffset=baseOffset, pwrssUpdate=glmerPwrssUpdate, ## save GQmat in the object and use that instead of nAGQ GQmat=GHrule(nAGQ)), devlist) } ff <- mkdevfun(list2env(devlist),nAGQ=nAGQ, verbose) xst <- rep.int(0.1, nth) x0 <- pp$theta lower <- object@lower if (!is.na(nAGQ) && nAGQ > 0L) { xst <- c(xst, sqrt(diag(pp$unsc()))) x0 <- c(x0, unname(fixef(object))) lower <- c(lower, rep(-Inf,length(x0)-length(lower))) } control <- object@optinfo$control newControl <- list(...)$control if (!is.null(newControl)) { for (i in names(newControl)) { control[[i]] <- newControl[[i]] } } ## control <- c(control,list(xst=0.2*xst, xt=xst*0.0001)) opt <- optwrap(object@optinfo$optimizer, ff, x0, lower=lower, control=control) if (isGLMM(object)) rr$setOffset(baseOffset) mkMerMod(environment(ff), opt, list(flist=object@flist, cnms=object@cnms, Gp=object@Gp, lower=object@lower), object@frame, getCall(object)) } ##-- BUG in roxygen2: If we use @S3method instead of @method, ##-- the \usage{ ... } will have ##-- refitML.merMod(..) instead of \method{refitML}{mermod}(..) ##' @param optimizer a string indicating the optimizer to be used. ##' @method refitML merMod ##' @rdname refitML ##' @export refitML.merMod <- function (x, optimizer="bobyqa", ...) { ## FIXME: optimizer is set to 'bobyqa' for back-compatibility, but that's not ## consistent with lmer (default NM). Should be based on internally stored 'optimizer' value if (!isREML(x)) return(x) stopifnot(is(rr <- x@resp, "lmerResp")) rho <- new.env(parent=parent.env(environment())) rho$resp <- new(class(rr), y=rr$y, offset=rr$offset, weights=rr$weights, REML=0L) xpp <- x@pp$copy() rho$pp <- new(class(xpp), X=xpp$X, Zt=xpp$Zt, Lambdat=xpp$Lambdat, Lind=xpp$Lind, theta=xpp$theta, n=nrow(xpp$X)) devfun <- mkdevfun(rho, 0L) opt <- optwrap(optimizer, devfun, x@theta, lower=x@lower) ## opt <- bobyqa(x@theta, devfun, x@lower) n <- length(rr$y) pp <- rho$pp p <- ncol(pp$X) dims <- c(N=n, n=n, nmp=n-p, nth=length(pp$theta), p=p, q=nrow(pp$Zt), nAGQ=NA_integer_, useSc=1L, reTrms=length(x@cnms), spFe=0L, REML=0L, GLMM=0L, NLMM=0L) wrss <- rho$resp$wrss() ussq <- pp$sqrL(1) pwrss <- wrss + ussq cmp <- c(ldL2=pp$ldL2(), ldRX2=pp$ldRX2(), wrss=wrss, ussq=ussq, pwrss=pwrss, drsum=NA, dev=opt$fval, REML=NA, sigmaML=sqrt(pwrss/n), sigmaREML=sqrt(pwrss/(n-p))) ### FIXME: Should modify the call slot to set REML=FALSE. It is ### tricky to do so without causing the call to be evaluated new("lmerMod", call=x@call, frame=x@frame, flist=x@flist, cnms=x@cnms, theta=pp$theta, beta=pp$delb, u=pp$delu, lower=x@lower, devcomp=list(cmp=cmp, dims=dims), pp=pp, resp=rho$resp) } ##' residuals of merMod objects ##' @importFrom stats residuals ##' @S3method residuals merMod ##' @method residuals merMod ##' @param object a fitted [g]lmer (\code{merMod}) object ##' @param type type of residuals ##' @param scaled scale residuals by residual standard deviation (=scale parameter)? ##' @param \dots additional arguments (ignored: for method compatibility) ##' @details ##' \itemize{ ##' \item The default residual type ##' varies between \code{lmerMod} and \code{glmerMod} objects: they try to ##' mimic \code{\link{residuals.lm}} and \code{\link{residuals.glm}} respectively. ##' In particular, the default \code{type} is \code{"response"}, ##' i.e. (observed-fitted) for \code{lmerMod} objects ##' vs. \code{"deviance"} for \code{glmerMod} objects. \code{type="partial"} ##' is not yet implemented for either type. ##' \item Note that the meaning of \code{"pearson"} residuals differs between ##' \code{\link{residuals.lm}} and \code{\link{residuals.lme}}. The former ##' returns values scaled by the square root of user-specified weights (if any), ##' but \emph{not} by the residual standard deviation, ##' while the latter returns values scaled by the estimated standard deviation ##' (which will include the effects of any variance structure specified in ##' the \code{weights} argument). To replicate \code{lme} behaviour, use ##' \code{type="pearson"}, \code{scaled=TRUE}. ##' } residuals.merMod <- function(object, type=if (isGLMM(object)) "deviance" else "response", scaled=FALSE, ...) { r <- residuals(object@resp, type,...) if (scaled) r <- r/sigma(object) if (!is.null(na.action <- attr(model.frame(object),"na.action"))) r <- naresid(na.action,r) r } ##' @rdname residuals.merMod ##' @S3method residuals lmResp ##' @method residuals lmResp residuals.lmResp <- function(object, type = c("working", "response", "deviance", "pearson", "partial"), ...) { y <- object$y r <- object$wtres mu <- object$mu switch(match.arg(type), working =, response = y-mu, deviance =, pearson = r, partial = .NotYetImplemented()) } ##' @rdname residuals.merMod ##' @S3method residuals glmResp ##' @method residuals glmResp residuals.glmResp <- function(object, type = c("deviance", "pearson", "working", "response", "partial"), ...) { type <- match.arg(type) y <- object$y mu <- object$mu switch(type, deviance = { d.res <- sqrt(object$devResid()) ifelse(y > mu, d.res, -d.res) }, pearson = object$wtres, working = object$wrkResids(), response = y - mu, partial = .NotYetImplemented()) } ##' @S3method sigma merMod sigma.merMod <- function(object, ...) { dc <- object@devcomp dd <- dc$dims if(dd[["useSc"]]) dc$cmp[[if(dd[["REML"]]) "sigmaREML" else "sigmaML"]] else 1. } ##' @importFrom stats simulate NULL ##' Simulate responses from the model represented by a fitted model object ##' ##' @title Simulate responses from a \code{\linkS4class{merMod}} object ##' @param object a fitted model object ##' @param nsim positive integer scalar - the number of responses to simulate ##' @param seed an optional seed to be used in \code{set.seed} immediately ##' before the simulation so as to generate a reproducible sample. ##' @param use.u (logical) if \code{TRUE}, generate a simulation conditional on the current ##' random-effects estimates; if \code{FALSE} generate new Normally distributed random-effects values ##' @param ... optional additional arguments, none are used at present ##' @examples ##' ## test whether fitted models are consistent with the ##' ## observed number of zeros in CBPP data set: ##' gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), ##' data = cbpp, family = binomial) ##' gg <- simulate(gm1,1000) ##' zeros <- sapply(gg,function(x) sum(x[,"incidence"]==0)) ##' plot(table(zeros)) ##' abline(v=sum(cbpp$incidence==0),col=2) ##' @method simulate merMod ##' @export simulate.merMod <- function(object, nsim = 1, seed = NULL, use.u = FALSE, ...) { stopifnot((nsim <- as.integer(nsim[1])) > 0, is(object, "merMod")) ## i.e. not yet for glmer etc: ## is(object@resp, "lmerResp")) if(!is.null(seed)) set.seed(seed) if(!exists(".Random.seed", envir = .GlobalEnv)) runif(1) # initialize the RNG if necessary RNGstate <- .Random.seed sigma <- sigma(object) n <- nrow(X <- getME(object, "X")) if (is.null(nm <- names(fitted(object)))) nm <- seq(n) # fixed-effect contribution etasim.fix <- as.vector(X %*% getME(object, "beta")) if (length(offset <- getME(object,"offset"))>0) { etasim.fix <- etasim.fix+offset } U <- getME(object, "Z") %*% getME(object, "Lambda") u <- if (use.u) { rep(getME(object, "u"), nsim)/sigma ## ??? u is 'spherized' but not scaled ??? } else { rnorm(ncol(U)*nsim) } etasim.reff <- ## UNSCALED random-effects contribution: as(U %*% matrix(u, ncol = nsim), "matrix") if (is(object@resp,"lmerResp")) { ## result will be matrix n x nsim : val <- etasim.fix + sigma * (etasim.reff + ## residual contribution: matrix(rnorm(n * nsim), ncol = nsim)) } else if (is(object@resp,"glmResp")) { ## GLMM ## n.b. DON'T scale random-effects (???) etasim <- etasim.fix+etasim.reff ## FIXME:: try to avoid @call ... family <- object@call$family if(is.symbol(family)) family <- as.character(family) if(is.character(family)) family <- get(family, mode = "function", envir = parent.frame(2)) if(is.function(family)) family <- family() if(is.language(family)) family <- eval(family) if(is.null(family$family)) stop("'family' not recognized") musim <- family$linkinv(etasim) ntot <- length(musim) ## FIXME: or could be dims["n"]? ## FIXME: is it possible to leverage family$simulate ... ??? val <- switch(family$family, poisson=rpois(ntot,lambda=musim), binomial={ w <- weights(object) Y <- rbinom(ntot,prob=musim,size=w) resp <- model.response(object@frame) if (!is.matrix(resp)) { ## bernoulli, or weights specified if (is.factor(resp)) { if (any(weights(object)!=1)) stop("non-uniform weights with factor response??") f <- factor(levels(resp)[Y+1],levels=levels(resp)) split(f, rep(seq_len(nsim), each = n)) } else { Y/w } } else { ## FIXME: should "N-size" (column 2) be named? ## copying structures from stats/R/family.R nresp <- nrow(resp) YY <- cbind(Y, w - Y) yy <- lapply(split(YY,gl(nsim,nresp,2*nsim*nresp)), matrix, ncol=2, dimnames=list(NULL,colnames(resp))) names(yy) <- paste("sim",seq_along(yy),sep="_") yy } }, stop("simulation not implemented for family", family$family)) } else { stop("simulate method for NLMMs not yet implemented") } ## from src/library/stats/R/lm.R if(!is.list(val)) { dim(val) <- c(n, nsim) val <- as.data.frame(val) } else class(val) <- "data.frame" names(val) <- paste("sim", seq_len(nsim), sep="_") row.names(val) <- nm attr(val, "seed") <- RNGstate val } ##' @importFrom stats terms ##' @S3method terms merMod terms.merMod <- function(x, fixed.only=TRUE, ...) { if (fixed.only) { tt <- terms.formula(formula(x,fixed.only=TRUE)) attr(tt,"predvars") <- attr(attr(x@frame,"terms"),"predvars.fixed") tt } else attr(x@frame,"terms") } ##' @importFrom stats update ##' @S3method update merMod update.merMod <- function(object, formula., ..., evaluate = TRUE) { if (is.null(call <- getCall(object))) stop("object should contain a 'call' component") extras <- match.call(expand.dots = FALSE)$... if (!missing(formula.)) call$formula <- update.formula(formula(object), formula.) if (length(extras) > 0) { existing <- !is.na(match(names(extras), names(call))) for (a in names(extras)[existing]) call[[a]] <- extras[[a]] if (any(!existing)) { call <- c(as.list(call), extras[!existing]) call <- as.call(call) } } if (evaluate) eval(call, parent.frame()) else call } ###----- Printing etc ---------------------------- methTitle <- function(object, dims = object@devcomp$dims) paste(switch(1L + dims[["GLMM"]] * 2L + dims[["NLMM"]], "Linear", "Nonlinear", "Generalized linear", "Generalized nonlinear"), "mixed model fit by", if(isREML(object)) "REML" else "maximum likelihood") famlink <- function(object, resp = object@resp) { if(is(resp, "glmResp")) resp$family[c("family", "link")] else list(family = NULL, link = NULL) } .prt.family <- function(famL) { if (!is.null(f <- famL$family)) { cat(" Family:", f) if (!(is.null(ll <- famL$link))) cat(" (", ll, ")") cat("\n") } } .prt.call <- function(call) { if (!is.null(cc <- call$formula)) cat("Formula:", deparse(cc),"\n") if (!is.null(cc <- call$data)) cat(" Data:", deparse(cc), "\n") if (!is.null(cc <- call$subset)) cat(" Subset:", deparse(asOneSidedFormula(cc)[[2]]),"\n") } getLlikAIC <- function(object, cmp = object@devcomp$cmp) { llik <- logLik(object) # returns NA for a REML fit - maybe change? AICstats <- { if(isREML(object)) cmp["REML"] # *no* likelihood stats here else { c(AIC = AIC(llik), BIC = BIC(llik), logLik = c(llik), deviance = deviance(object)) } } list(logLik=llik, AICtab = AICstats) } .prt.aictab <- function(aictab, digits=4) { t.4 <- round(aictab, digits) if (length(aictab) == 1 && names(aictab) == "REML") cat("REML criterion at convergence:", t.4, "\n") else print(t.4) } .prt.VC <- function(varcor, digits, comp, ...) { cat("Random effects:\n") fVC <- if(missing(comp)) formatVC(varcor, digits=digits) else formatVC(varcor, digits=digits, comp=comp) print(fVC, quote = FALSE, digits = digits, ...) } .prt.grps <- function(ngrps, nobs) { cat(sprintf("Number of obs: %d, groups: ", nobs)) cat(paste(paste(names(ngrps), ngrps, sep = ", "), collapse = "; ")) cat("\n") } ## This is modeled a bit after print.summary.lm : ## Prints *both* 'mer' and 'merenv' - as it uses summary(x) mainly ##' @S3method print summary.merMod print.summary.merMod <- function(x, digits = max(3, getOption("digits") - 3), correlation = NULL, symbolic.cor = FALSE, signif.stars = getOption("show.signif.stars"), ranef.comp = c("Variance", "Std.Dev."), ...) { cat(sprintf("%s ['%s']\n",x$methTitle, x$objClass)) .prt.family(x) ## not for patched branch? ## need residuals.merMod() rather than residuals(): ## summary.merMod has no residuals method ## cat("Scaled residuals:\n") ## print(residuals.merMod(x,type="pearson",scaled=TRUE)),digits=digits) .prt.call(x$call); cat("\n") .prt.aictab(x$AICtab, 4); cat("\n") .prt.VC(x$varcor, digits=digits, useScale= x$useScale, comp = ranef.comp, ...) .prt.grps(x$ngrps, nobs= x$devcomp$dims[["n"]]) p <- nrow(x$coefficients) if (p > 0) { cat("\nFixed effects:\n") printCoefmat(x$coefficients, zap.ind = 3, #, tst.ind = 4 digits = digits, signif.stars = signif.stars) if(is.null(correlation)) { # default correlation <- p <= 20 if(!correlation) { nam <- deparse(substitute(x)) if(length(nam) > 1 || nchar(nam) >= 32) nam <- "...." message(sprintf(paste("\nCorrelation matrix not shown by default, as p = %d > 20.", "Use print(%s, correlation=TRUE) or", " vcov(%s) if you need it\n", sep="\n"), p, nam, nam)) } } else if(!is.logical(correlation)) stop("'correlation' must be NULL or logical") if(correlation) { if(is.null(VC <- x$vcov)) VC <- vcov(x) corF <- VC@factors$correlation if (is.null(corF)) { cat("\nCorrelation of Fixed Effects is not available\n") } else { p <- ncol(corF) if (p > 1) { rn <- rownames(x$coefficients) rns <- abbreviate(rn, minlength=11) cat("\nCorrelation of Fixed Effects:\n") if (is.logical(symbolic.cor) && symbolic.cor) { corf <- as(corF, "matrix") dimnames(corf) <- list(rns, abbreviate(rn, minlength=1, strict=TRUE)) print(symnum(corf)) } else { corf <- matrix(format(round(corF@x, 3), nsmall = 3), ncol = p, dimnames = list(rns, abbreviate(rn, minlength=6))) corf[!lower.tri(corf)] <- "" print(corf[-1, -p, drop=FALSE], quote = FALSE) } ## !symbolic.cor } ## if (p > 1) } ## !is.null(corF) } ## if (correlation) } ## if (p>0) invisible(x) }## print.summary.merMod ##' @S3method print merMod print.merMod <- function(x, digits = max(3, getOption("digits") - 3), correlation = NULL, symbolic.cor = FALSE, signif.stars = getOption("show.signif.stars"), ranef.comp = "Std.Dev.", ...) { dims <- (devC <- x@devcomp)$dims methTit <- methTitle(x, dims=dims) cat(sprintf("%s ['%s']\n",methTit, class(x))) famL <- famlink(x, resp = x@resp) .prt.family(famL) .prt.call(x@call) useScale <- as.logical(dims[["useSc"]]) llAIC <- getLlikAIC(x) .prt.aictab(llAIC$AICtab, 4) varcor <- VarCorr(x) .prt.VC(varcor, digits=digits, comp = ranef.comp, ...) ngrps <- sapply(x@flist, function(x) length(levels(x))) .prt.grps(ngrps, nobs= dims[["n"]]) if(length(cf <- fixef(x)) >= 0) { cat("Fixed Effects:\n") print.default(format(cf, digits = digits), print.gap = 2L, quote = FALSE, ...) } else cat("No fixed effect coefficients\n") invisible(x) } ##' @exportMethod show setMethod("show", "merMod", function(object) print.merMod(object)) ##' Return the deviance component list ##' ##' A fitted model of class \code{\linkS4class{merMod}} has a \code{devcomp} ##' slot as described in the value section. ##' @title Extract the deviance component list ##' @param x a fitted model of class \code{\linkS4class{merMod}} ##' @return a list with components ##' \item{dims}{a named integer vector of various dimensions} ##' \item{cmp}{a named numeric vector of components of the deviance} ##' @export ##' @note This function is deprecated, use \code{getME(., "devcomp")} devcomp <- function(x) { .Deprecated("getME(., \"devcomp\")") stopifnot(is(x, "merMod")) x@devcomp } ##' @exportMethod getL setMethod("getL", "merMod", function(x) { .Deprecated("getME(., \"L\")") getME(x, "L") }) ##' Construct names of individual theta/sd:cor components ##' ##' @param object a fixed model ##' @param diag.only include only diagonal elements? ##' @param old (logical) give backward-compatible results? ##' @param prefix a character vector with two elements giving the prefix ##' for diagonal (e.g. "sd") and off-diagonal (e.g. "cor") elements ##' ## @export tnames <- function(object,diag.only=FALSE,old=TRUE,prefix=NULL) { if (old) { nc <- c(unlist(mapply(function(g,e) { mm <- outer(e,e,paste,sep=".") diag(mm) <- e mm <- if (diag.only) diag(mm) else mm[lower.tri(mm,diag=TRUE)] paste(g,mm,sep=".") }, names(object@cnms),object@cnms))) return(nc) } else { pfun <- function(g,e) { mm <- outer(e,e,paste,sep=".") mm[] <- paste(mm,g,sep="|") if (!is.null(prefix)) mm[] <- paste(prefix[2],mm,sep="_") diag(mm) <- paste(e,g,sep="|") if (!is.null(prefix)) diag(mm) <- paste(prefix[1],diag(mm),sep="_") mm <- if (diag.only) diag(mm) else mm[lower.tri(mm,diag=TRUE)] } nc <- c(unlist(mapply(pfun,names(object@cnms),object@cnms))) return(nc) } } ##' Extract or Get Generalize Components from a Fitted Mixed Effects Model ##' ##' Extract (or \dQuote{get}) \dQuote{components} -- in a generalized sense -- ##' from a fitted mixed-effects model, i.e. (in this version of the package) ##' from an object of class \code{"\linkS4class{merMod}"}. ##' ##' The goal is to provide \dQuote{everything a user may want} from a fitted ##' \code{"merMod"} object \emph{as far} as it is not available by methods, such ##' as \code{\link{fixef}}, \code{\link{ranef}}, \code{\link{vcov}}, etc. ##' ##' @aliases getME getL getL,merMod-method ##' @param object a fitted mixed-effects model of class ##' \code{"\linkS4class{merMod}"}, i.e. typically the result of ##' \code{\link{lmer}()}, \code{\link{glmer}()} or \code{\link{nlmer}()}. ##' @param name a character vector specifying the name(s) of the ##' \dQuote{component}. If \code{length(name)}>1, a named list of ##' components will be returned. Possible values are:\cr ##' \describe{ ##' \item{X}{fixed-effects model matrix} ##' \item{Z}{random-effects model matrix} ##' \item{Zt}{transpose of random-effects model matrix. Note that ##' the structure of \code{Zt} has changed since \code{lme4.0}; ##' to get a backward-compatible structure, use ##' \code{do.call(Matrix::rBind,getME(.,"Ztlist"))}} ##' \item{Ztlist}{list of components of the transpose of the random-effects model matrix, ##' separated by individual variance component} ##' \item{y}{response vector} ##' \item{mu}{conditional mean of the response} ##' \item{u}{conditional mode of the \dQuote{spherical} random effects variable} ##' \item{b}{conditional mode of the random effects variable} ##' \item{Gp}{groups pointer vector. A pointer to the beginning ##' of each group of random effects corresponding to the ##' random-effects terms, beginning with 0 and including ##' a final element giving the total number of random effects} ##' \item{Tp}{theta pointer vector. A pointer to the beginning ##' of the theta sub-vectors corresponding to the ##' random-effects terms, beginning with 0 and including ##' a final element giving the total number of random effects} ##' \item{L}{sparse Cholesky factor of the penalized random-effects model.} ##' \item{Lambda}{relative covariance factor of the random effects.} ##' \item{Lambdat}{transpose of the relative covariance factor of the random effects.} ##' \item{Lind}{index vector for inserting elements of \eqn{\theta}{theta} into the ##' nonzeros of \eqn{\Lambda}{Lambda}} ##' \item{A}{Scaled sparse model matrix (class ##' \code{"\link[Matrix:dgCMatrix-class]{dgCMatrix}"}) for ##' the unit, orthogonal random effects, \eqn{U}, ##' equal to \code{getME(.,"Zt") \%*\% getME(.,"Lambdat")}} ##' \item{RX}{Cholesky factor for the fixed-effects parameters} ##' \item{RZX}{cross-term in the full Cholesky factor} ##' \item{sigma}{residual standard error} ##' \item{flist}{a list of the grouping variables (factors) involved in the random effect terms} ##' \item{beta}{fixed-effects parameter estimates (identical to the result of \code{\link{fixef}}, but without names)} ##' \item{theta}{random-effects parameter estimates: these are parameterized as the relative Cholesky factors of each random effect term} ##' \item{ST}{a list of matrices giving the relative Cholesky factors for each random effect term} ##' \item{n_rtrms}{number of random-effects terms} ##' \item{n_rfacs}{number of distinct random-effects grouping factors} ##' \item{REML}{restricted maximum likelihood} ##' \item{is_REML}{same as the result of \code{\link{isREML}}} ##' \item{devcomp}{a list consisting of a named numeric vector, \dQuote{cmp}, and ##' a named integer vector, \dQuote{dims}, describing the fitted model} ##' \item{offset}{model offset} ##' \item{lower}{lower bounds on model parameters (random effects parameters only)} ##' } ##' @return Unspecified, as very much depending on the \code{\link{name}}. ##' @seealso \code{\link{getCall}()}, ##' More standard methods for mer objects, such as \code{\link{ranef}}, ##' \code{\link{fixef}}, \code{\link{vcov}}, etc.: ##' see \code{methods(class="merMod")} ##' @keywords utilities ##' @examples ##' ##' ## shows many methods you should consider *before* using getME(): ##' methods(class = "merMod") ##' ##' (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) ##' Z <- getME(fm1, "Z") ##' stopifnot(is(Z, "CsparseMatrix"), ##' c(180,36) == dim(Z), ##' all.equal(fixef(fm1), getME(fm1, "beta"), ##' check.attributes=FALSE, tol = 0)) ##' ##' ## All that can be accessed [potentially ..]: ##' (nmME <- eval(formals(getME)$name)) ##' \dontshow{ ##' ## internal consistency check ensuring that all work: ##' ## "try(.)" because some are not yet implemented: ##' str(parts <- sapply(nmME, function(nm) try(getME(fm1, nm)), ##' simplify=FALSE)) ##' }% dont.. ##' ##' @export getME <- function(object, name = c("X", "Z","Zt", "Ztlist", "y", "mu", "u", "b", "Gp", "Tp", "L", "Lambda", "Lambdat", "Lind", "A", "RX", "RZX", "sigma", "flist", "beta", "theta", "ST", "REML", "is_REML", "n_rtrms", "n_rfacs", "cnms", "devcomp", "offset", "lower")) { if(missing(name)) stop("'name' must not be missing") stopifnot(is(object,"merMod")) if (length(name <- as.character(name))>1) { names(name) <- name return(lapply(name, getME, object=object)) } name <- match.arg(name) rsp <- object@resp PR <- object@pp dc <- object@devcomp cmp <- dc $ cmp cnms <- object@cnms dims <- dc $ dims Tpfun <- function(cnms) { ltsize <- function(n) n*(n+1)/2 # lower triangle size cLen <- cumsum(ltsize(vapply(cnms,length, 1L))) setNames(c(0, cLen), c("beg__", names(cnms))) ## such that diff( Tp ) is well-named } switch(name, "X" = PR$X, ## ok ? - check -- use model.matrix() method instead? "Z" = t(PR$Zt), "Zt"= PR$Zt, "Ztlist" = { getInds <- function(i) { n <- diff(object@Gp)[i] ## number of elements in this block nt <- length(cnms[[i]]) ## number of REs inds <- lapply(seq(nt),seq,to=n,by=nt) ## pull out individual RE indices inds <- lapply(inds,function(x) x + object@Gp[i]) ## add group offset } inds <- do.call(c,lapply(seq_along(cnms),getInds)) setNames(lapply(inds,function(i) PR$Zt[i,]), tnames(object,diag.only=TRUE)) }, "y" = rsp$y, "mu"= rsp$mu, "u" = object@u, "b" = t(PR$Lambdat) %*% object@u, "L"= PR$ L(), "Lambda"= t(PR$ Lambdat), "Lambdat"= PR$ Lambdat, "A" = PR$Lambdat %*% PR$Zt, "Lind" = PR$ Lind, "RX" = structure(PR$RX(), dimnames = list(colnames(PR$X), colnames(PR$X))), ## maybe add names elsewhere? "RZX" = structure(PR$RZX, dimnames = list(NULL, colnames(PR$X))), ## maybe add names elsewhere? "sigma" = sigma(object), "Gp" = object@Gp, "Tp" = Tpfun(cnms) , "flist" = object@flist, "beta" = object@beta, "theta"= setNames(object@theta,tnames(object)), "ST"= setNames(vec2STlist(object@theta, n=sapply(cnms,length)), names(cnms)), "REML" = dims["REML"], "is_REML" = isREML(object), ## number of random-effects terms "n_rtrms" = length(cnms), ## number of random-effects grouping factors "n_rfacs" = length(object@flist), "cnms" = cnms, "devcomp" = dc, "offset" = rsp$offset, "lower" = object@lower, ## FIXME: current version gives lower bounds for theta parameters only -- these must be extended for [GN]LMMs -- give extended value including -Inf values for beta values? "..foo.." =# placeholder! stop(gettextf("'%s' is not implemented yet", sprintf("getME(*, \"%s\")", name))), ## otherwise stop(sprintf("Mixed-Effects extraction of '%s' is not available for class \"%s\"", name, class(object)))) }## {getME} ##' @importMethodsFrom Matrix t %*% crossprod diag tcrossprod ##' @importClassesFrom Matrix dgCMatrix dpoMatrix corMatrix NULL ## Extract the conditional variance-covariance matrix of the fixed-effects ## parameters ## ## @title Extract conditional covariance matrix of fixed effects ## @param sigma numeric scalar, the residual standard error ## @param unsc matrix of class \code{"\linkS4class{dpoMatrix}"}, the ## unscaled variance-covariance matrix ## @param nmsX character vector of column names of the model matrix ## @param correlation logical scalar, should the correlation matrix ## also be evaluated. ## @param ... additional, optional parameters. None are used at present. mkVcov <- function(sigma, unsc, nmsX, correlation = TRUE, ...) { V <- sigma^2 * unsc if(is.null(rr <- tryCatch(as(V, "dpoMatrix"), error = function(e) NULL))) stop("Computed variance-covariance matrix is not positive definite") dimnames(rr) <- list(nmsX, nmsX) if(correlation) rr@factors$correlation <- if(!is.na(sigma)) as(rr, "corMatrix") else rr # (is NA anyway) rr } ##' @importFrom stats vcov ##' @S3method vcov merMod vcov.merMod <- function(object, correlation = TRUE, sigm = sigma(object), ...) mkVcov(sigm, unsc = object@pp$unsc(), nmsX = colnames(object@pp$X), correlation=correlation, ...) ##' @importFrom stats vcov ##' @S3method vcov summary.merMod vcov.summary.merMod <- function(object, correlation = TRUE, ...) { if(is.null(object$vcov)) stop("logic error in summary of merMod object") object$vcov } ##' Make variance and correlation matrices from \code{theta} ##' ##' @param sc scale factor (residual standard deviation) ##' @param cnms component names ##' @param nc numeric vector: number of terms in each RE component ##' @param theta theta vector (lower-triangle of Cholesky factors) ##' @param nms component names (FIXME: nms/cnms redundant: nms=names(cnms)?) ##' @seealso \code{\link{VarCorr}} ##' @return A matrix ##' @export mkVarCorr <- function(sc, cnms, nc, theta, nms) { ncseq <- seq_along(nc) thl <- split(theta, rep.int(ncseq, (nc * (nc + 1))/2)) if(!all(nms == names(cnms))) ## the above FIXME warning("nms != names(cnms) -- whereas lme4-authors thought they were --\n", "Please report!", immediate.=TRUE) ans <- lapply(ncseq, function(i) { ## Li := \Lambda_i, the i-th block diagonal of \Lambda(\theta) Li <- diag(nrow = nc[i]) Li[lower.tri(Li, diag = TRUE)] <- thl[[i]] rownames(Li) <- cnms[[i]] ## val := \Sigma_i = \sigma^2 \Lambda_i \Lambda_i', the val <- tcrossprod(sc * Li) # variance-covariance stddev <- sqrt(diag(val)) corr <- t(val / stddev)/stddev diag(corr) <- 1 structure(val, stddev = stddev, correlation = corr) }) if(is.character(nms)) { ## FIXME: do we want this? Maybe not. ## Potential problem: the names of the elements of the VarCorr() list ## are not necessarily unique (e.g. fm2 from example("lmer") has *two* ## Subject terms, so the names are "Subject", "Subject". The print method ## for VarCorrs handles this just fine, but it's a little awkward if we ## want to dig out elements of the VarCorr list ... ??? if (anyDuplicated(nms)) nms <- make.names(nms, unique=TRUE) names(ans) <- nms } structure(ans, sc = sc) } ##' Extract variance and correlation components ##' ##' This function calculates the estimated variances, standard deviations, and ##' correlations between the random-effects terms in a mixed-effects model, of ##' class \code{\linkS4class{merMod}} (linear, generalized or nonlinear). The ##' within-group error variance and standard deviation are also calculated. ##' ##' @name VarCorr ##' @aliases VarCorr VarCorr.merMod ##' @param x a fitted model object, usually an object inheriting from class ##' \code{\linkS4class{merMod}}. ##' @param sigma an optional numeric value used as a multiplier for the standard ##' deviations. Default is \code{1}. ##' @param rdig an optional integer value specifying the number of digits used ##' to represent correlation estimates. Default is \code{3}. ##' @return a list of matrices, one for each random effects grouping term. ##' For each grouping term, the standard deviations and correlation matrices for each grouping term ##' are stored as attributes \code{"stddev"} and \code{"correlation"}, respectively, of the ##' variance-covariance matrix, and ##' the residual standard deviation is stored as attribute \code{"sc"} ##' (for \code{glmer} fits, this attribute stores the scale parameter of the model). ##' @author This is modeled after \code{\link[nlme]{VarCorr}} from package ##' \pkg{nlme}, by Jose Pinheiro and Douglas Bates. ##' @seealso \code{\link{lmer}}, \code{\link{nlmer}} ##' @examples ##' data(Orthodont, package="nlme") ##' fm1 <- lmer(distance ~ age + (age|Subject), data = Orthodont) ##' VarCorr(fm1) ##' @keywords models ##' @importFrom nlme VarCorr ##' @export VarCorr ##' @method VarCorr merMod ##' @export VarCorr.merMod <- function(x, sigma, rdig)# <- 3 args from nlme { ## FIXME:: would like to fix nlme to add ... ## FIXME:: add type=c("varcov","sdcorr","logs" ?) if (is.null(cnms <- x@cnms)) stop("VarCorr methods require reTrms, not just reModule") if(missing(sigma)) # "bug": fails via default 'sigma=sigma(x)' sigma <- lme4::sigma(x) ## FIXME: do we still need lme4:: ? nc <- vapply(cnms, length, 1L) # no. of columns per term structure(mkVarCorr(sigma, cnms=cnms, nc=nc, theta = x@theta, nms = {fl <- x@flist; names(fl)[attr(fl, "assign")]}), useSc = as.logical(x@devcomp$dims["useSc"]), class = "VarCorr.merMod") } if(FALSE)## *NOWHERE* used _FIXME_ ?? ## Compute standard errors of fixed effects from an merMod object ## ## @title Standard errors of fixed effects ## @param object "merMod" object, ## @param ... additional, optional arguments. None are used at present. ## @return numeric vector of length length(fixef(.)) unscaledVar <- function(object, ...) { stopifnot(is(object, "merMod")) sigma(object) * diag(object@pp$unsc()) } ##' @S3method print VarCorr.merMod print.VarCorr.merMod <- function(x, digits = max(3, getOption("digits") - 2), comp = "Std.Dev.", ...) print(formatVC(x, digits=digits, comp=comp), quote=FALSE, ...) ##' __NOT YET EXPORTED__ ##' "format()" the 'VarCorr' matrix of the random effects -- for ##' print()ing and show()ing ##' ##' @title Format the 'VarCorr' Matrix of Random Effects ##' @param varc a \code{\link{VarCorr}} (-like) matrix with attributes. ##' @param digits the number of significant digits. ##' @param comp character vector of length one or two indicating which ##' columns out of "Variance" and "Std.Dev." should be shown in the ##' formatted output. ##' @return a character matrix of formatted VarCorr entries from \code{varc}. formatVC <- function(varc, digits = max(3, getOption("digits") - 2), comp = "Std.Dev.") { c.nms <- c("Groups", "Name", "Variance", "Std.Dev.") avail.c <- c.nms[-(1:2)] if(any(is.na(mcc <- pmatch(comp, avail.c)))) stop("Illegal 'comp': ", comp[is.na(mcc)]) nc <- length(colnms <- c(c.nms[1:2], (use.c <- avail.c[mcc]))) if(length(use.c) == 0) stop("Must *either* show variances or standard deviations") useScale <- attr(varc, "useSc") recorr <- lapply(varc, attr, "correlation") reStdDev <- c(lapply(varc, attr, "stddev"), if(useScale) list(Residual = unname(attr(varc, "sc")))) reLens <- vapply(reStdDev, length, 1L) nr <- sum(reLens) reMat <- array('', c(nr, nc), list(rep.int('', nr), colnms)) reMat[1+cumsum(reLens)-reLens, "Groups"] <- names(reLens) reMat[,"Name"] <- c(unlist(lapply(varc, colnames)), if(useScale) "") if(any("Variance" == use.c)) reMat[,"Variance"] <- format(unlist(reStdDev)^2, digits = digits) if(any("Std.Dev." == use.c)) reMat[,"Std.Dev."] <- format(unlist(reStdDev), digits = digits) if (any(reLens > 1)) { maxlen <- max(reLens) corr <- do.call("rBind", lapply(recorr, function(x) { x <- as(x, "matrix") dig <- max(2, digits - 2) # use 'digits' ! cc <- format(round(x, dig), nsmall = dig) cc[!lower.tri(cc)] <- "" nr <- nrow(cc) if (nr >= maxlen) return(cc) cbind(cc, matrix("", nr, maxlen-nr)) }))[, -maxlen, drop = FALSE] if (nrow(corr) < nrow(reMat)) corr <- rbind(corr, matrix("", nrow(reMat) - nrow(corr), ncol(corr))) colnames(corr) <- c("Corr", rep.int("", max(0L, ncol(corr)-1L))) cbind(reMat, corr) } else reMat } ##' @S3method summary merMod summary.merMod <- function(object, ...) { resp <- object@resp devC <- object@devcomp dd <- devC$dims cmp <- devC$cmp useSc <- as.logical(dd[["useSc"]]) sig <- sigma(object) REML <- isREML(object) famL <- famlink(resp=resp) coefs <- cbind("Estimate" = fixef(object), "Std. Error" = sig * sqrt(diag(object@pp$unsc()))) if (nrow(coefs) > 0) { coefs <- cbind(coefs, (cf3 <- coefs[,1]/coefs[,2]), deparse.level=0) colnames(coefs)[3] <- paste(if(useSc) "t" else "z", "value") if (isGLMM(object)) coefs <- cbind(coefs, "Pr(>|z|)" = 2*pnorm(abs(cf3), lower.tail=FALSE)) } llAIC <- getLlikAIC(object) ## FIXME: You can't count on object@re@flist, ## nor compute VarCorr() unless is(re, "reTrms"): varcor <- VarCorr(object) # use S3 class for now structure(list(methTitle = methTitle(object, dims=dd), objClass = class(object), devcomp = devC, isLmer=is(resp, "lmerResp"), useScale=useSc, logLik=llAIC[["logLik"]], family=famL$fami, link=famL$link, ngrps=sapply(object@flist, function(x) length(levels(x))), coefficients=coefs, sigma=sig, vcov=vcov(object, correlation=TRUE, sigm=sig), varcor=varcor, # and use formatVC(.) for printing. AICtab = llAIC[["AICtab"]], call=object@call, residuals=residuals(object,"pearson") ), class = "summary.merMod") } ## TODO: refactor? ##' @S3method summary summary.merMod summary.summary.merMod <- function(object, varcov = TRUE, ...) { if(varcov && is.null(object$vcov)) object$vcov <- vcov(object, correlation=TRUE, sigm = object$sigma) object } ### Plots for the ranef.mer class ---------------------------------------- ##' @importFrom lattice dotplot ##' @S3method dotplot ranef.mer dotplot.ranef.mer <- function(x, data, ...) { prepanel.ci <- function(x, y, se, subscripts, ...) { if (is.null(se)) return(list()) x <- as.numeric(x) hw <- 1.96 * as.numeric(se[subscripts]) list(xlim = range(x - hw, x + hw, finite = TRUE)) } panel.ci <- function(x, y, se, subscripts, pch = 16, horizontal = TRUE, col = dot.symbol$col, lty = dot.line$lty, lwd = dot.line$lwd, col.line = dot.line$col, levels.fos = unique(y), groups = NULL, ...) { x <- as.numeric(x) y <- as.numeric(y) dot.line <- trellis.par.get("dot.line") dot.symbol <- trellis.par.get("dot.symbol") sup.symbol <- trellis.par.get("superpose.symbol") panel.abline(h = levels.fos, col = col.line, lty = lty, lwd = lwd) panel.abline(v = 0, col = col.line, lty = lty, lwd = lwd) if (!is.null(se)) { se <- as.numeric(se[subscripts]) panel.segments( x - 1.96 * se, y, x + 1.96 * se, y, col = 'black') } panel.xyplot(x, y, pch = pch, ...) } f <- function(x, ...) { ss <- stack(x) ss$ind <- factor(as.character(ss$ind), levels = colnames(x)) ss$.nn <- rep.int(reorder(factor(rownames(x)), x[[1]]), ncol(x)) se <- NULL if (!is.null(pv <- attr(x, "postVar"))) se <- unlist(lapply(1:(dim(pv)[1]), function(i) sqrt(pv[i, i, ]))) dotplot(.nn ~ values | ind, ss, se = se, prepanel = prepanel.ci, panel = panel.ci, xlab = NULL, ...) } lapply(x, f, ...) } ##' @importFrom graphics plot ##' @S3method plot ranef.mer plot.ranef.mer <- function(x, y, ...) { lapply(x, function(x) { cn <- lapply(colnames(x), as.name) switch(min(ncol(x), 3), qqmath(eval(substitute(~ x, list(x = cn[[1]]))), x, ...), xyplot(eval(substitute(y ~ x, list(y = cn[[1]], x = cn[[2]]))), x, ...), splom(~ x, ...)) }) } ##' @importFrom lattice qqmath ##' @S3method qqmath ranef.mer qqmath.ranef.mer <- function(x, data, ...) { prepanel.ci <- function(x, y, se, subscripts, ...) { x <- as.numeric(x) se <- as.numeric(se[subscripts]) hw <- 1.96 * se list(xlim = range(x - hw, x + hw, finite = TRUE)) } panel.ci <- function(x, y, se, subscripts, pch = 16, ...) { panel.grid(h = -1,v = -1) panel.abline(v = 0) x <- as.numeric(x) y <- as.numeric(y) se <- as.numeric(se[subscripts]) panel.segments(x - 1.96 * se, y, x + 1.96 * se, y, col = 'black') panel.xyplot(x, y, pch = pch, ...) } f <- function(x) { if (!is.null(pv <- attr(x, "postVar"))) { cols <- 1:(dim(pv)[1]) se <- unlist(lapply(cols, function(i) sqrt(pv[i, i, ]))) nr <- nrow(x) nc <- ncol(x) ord <- unlist(lapply(x, order)) + rep((0:(nc - 1)) * nr, each = nr) rr <- 1:nr ind <- gl(ncol(x), nrow(x), labels = names(x)) xyplot(rep(qnorm((rr - 0.5)/nr), ncol(x)) ~ unlist(x)[ord] | ind[ord], se = se[ord], prepanel = prepanel.ci, panel = panel.ci, scales = list(x = list(relation = "free")), ylab = "Standard normal quantiles", xlab = NULL, ...) } else { qqmath(~values|ind, stack(x), scales = list(y = list(relation = "free")), xlab = "Standard normal quantiles", ylab = NULL, ...) } } lapply(x, f) } ##' @importFrom graphics plot ##' @S3method plot coef.mer plot.coef.mer <- function(x, y, ...) { ## remove non-varying columns from frames reduced <- lapply(x, function(el) el[, !sapply(el, function(cc) all(cc == cc[1]))]) plot.ranef.mer(reduced, ...) } ##' @importFrom lattice dotplot ##' @S3method dotplot coef.mer dotplot.coef.mer <- function(x, data, ...) { mc <- match.call() mc[[1]] <- as.name("dotplot.ranef.mer") eval(mc) } ##' @importFrom stats weights ##' @S3method weights merMod weights.merMod <- function(object, ...) { object@resp$weights } getOptfun <- function(optimizer) { optfun <- if (is.character(optimizer)) tryCatch(get(optimizer), error=function(e) NULL) if (is.null(optfun)) stop("couldn't find optimizer function ",optimizer) if (!is.function(optfun)) stop("non-function specified as optimizer") needArgs <- c("fn","par","lower","control") if (any(is.na(match(needArgs, names(formals(optfun)))))) stop("optimizer function must use (at least) formal parameters ", paste(sQuote(needArgs), collapse=", ")) optfun } optwrap <- function(optimizer, fn, par, lower=-Inf, upper=Inf, control=list(), adj=FALSE, verbose=0L) { ## control must be specified if adj==TRUE; ## otherwise this is a fairly simple wrapper optfun <- getOptfun(optimizer) lower <- rep(lower, length.out=length(par)) upper <- rep(upper, length.out=length(par)) if (adj && is.character(optimizer)) ## control parameter tweaks: only for second round in nlmer, glmer switch(optimizer, bobyqa = { if(!is.numeric(control$rhobeg)) control$rhobeg <- 0.0002 if(!is.numeric(control$rhoend)) control$rhoend <- 2e-7 }, Nelder_Mead = { if (is.null(control$xst)) { thetaStep <- 0.1 nTheta <- length(environment(fn)$pp$theta) betaSD <- sqrt(diag(environment(fn)$pp$unsc())) xst <- c(rep.int(thetaStep, nTheta), pmin(betaSD,10)) } control$xst <- 0.2*xst if (is.null(control$xt)) control$xt <- control$xst*5e-4 }) if (optimizer=="Nelder_Mead") control$verbose <- verbose if (optimizer=="bobyqa" && all(par==0)) par[] <- 0.001 ## minor kluge arglist <- list(fn=fn, par=par, lower=lower, upper=upper, control=control) ## optimx: must pass method in control (?) because 'method' was previously ## used in lme4 to specify REML vs ML ## FIXME: test -- does deparse(substitute(...)) clause work? if (optimizer=="optimx" || deparse(substitute(optimizer))=="optimx") { if (is.null(method <- control$method)) stop("must specify 'method' explicitly for optimx") arglist$control$method <- NULL arglist <- c(arglist,list(method=method)) } ## FIXME: test! effects of multiple warnings?? ## may not need to catch warnings after all?? curWarnings <- list() opt <- withCallingHandlers(do.call(optfun,arglist), warning = function(w) { curWarnings <<- append(curWarnings,list(w$message)) }) ## cat("***",unlist(tail(curWarnings,1)),"\n") ## FIXME: set code to warn on convergence !=0 ## post-fit tweaking if (optimizer=="bobyqa") { opt$convergence <- opt$ierr } if (optimizer=="optimx") { ## optr <- lapply(opt,"[[",1)[c("par","fvalues","conv")] ## opt$message <- attr(opt,"details")[[1]]$message opt <- list(par=coef(opt)[1,], fvalues=opt$value[1], conv=opt$convcode[1], message=attr(opt,"details")[,"message"][[1]]) } if (opt$conv!=0) { wmsg <- paste("convergence code",opt$conv,"from",optimizer) if (!is.null(opt$msg)) wmsg <- paste0(wmsg,": ",opt$msg) warning(wmsg) curWarnings <<- append(curWarnings,list(wmsg)) } ## store all auxiliary information attr(opt,"optimizer") <- optimizer attr(opt,"control") <- control attr(opt,"warnings") <- curWarnings opt } lme4/R/lmList.R0000644000176000001440000002613012204271665012742 0ustar ripleyusers## List of linear models according to a grouping factor ## Extract the model formula modelFormula <- function(form) { if (class(form) != "formula" || length(form) != 3) stop("formula must be a two-sided formula object") rhs <- form[[3]] if (class(rhs) != "call" || rhs[[1]] != as.symbol('|')) stop("rhs of formula must be a conditioning expression") form[[3]] <- rhs[[2]] list(model = form, groups = rhs[[3]]) } ##' @title List of lm Objects with a Common Model ##' @param formula a linear formula object of the form ##' \code{y ~ x1+...+xn | g}. In the formula object, \code{y} represents ##' the response, \code{x1,...,xn} the covariates, and \code{g} the ##' grouping factor specifying the partitioning of the data according to ##' which different \code{lm} fits should be performed. ##' @inheritParams lmer ##' @param family an optional family specification for a generalized ##' linear model. ##' @param pool logical scalar, should the variance estimate pool the ##' residual sums of squares ##' @param ... additional, optional arguments to be passed to the ##' model function or family evaluation. ##' @export lmList <- function(formula, data, family, subset, weights, na.action, offset, pool, ...) { stopifnot(is(formula, "formula")) ## model.frame(groupedData) is problematic ... data <- as.data.frame(data) mCall <- mf <- match.call() m <- match(c("family", "data", "subset", "weights", "na.action", "offset"), names(mf), 0) mf <- mf[c(1, m)] ## substitute `+' for `|' in the formula ### FIXME: Figure out what to do here instead of subbars ## mf$formula <- subbars(formula) mf$x <- mf$model <- mf$y <- mf$family <- NULL mf$drop.unused.levels <- TRUE mf[[1]] <- as.name("model.frame") frm <- eval(mf, parent.frame()) mform <- modelFormula(formula) if (missing(family)) { val <- lapply(split(frm, eval(mform$groups, frm)), function(dat, formula) { ans <- tryCatch({ data <- as.data.frame(dat) lm(formula, data) }, error=function(e) NULL)# => ans is NULL iff an error happened ## FIXME: catch errors and pass them on as warnings? ## (simply passing them along with silent=FALSE ## gives confusing output) }, formula = mform$model) } else { val <- lapply(split(frm, eval(mform$groups, frm)), function(dat, formula, family) { ans <- tryCatch({ data <- as.data.frame(dat) glm(formula, family, data) }, error=function(e) NULL) #-> == NULL iff an error happened }, formula = mform$model, family = family) } if (missing(pool)) pool <- TRUE new("lmList", val, call = mCall, pool = pool) } ##' @importFrom stats coef ##' @S3method coef lmList ## Extract the coefficients and form a data.frame if possible ## FIXME: commented out nlme stuff (augFrame etc.). Restore, or delete for good ## FIXME: modified so that non-estimated values will be NA rather than set to ## coefs of first non-null estimate. Is that OK?? coef.lmList <- function(object, ## augFrame = FALSE, data = NULL, ##which = NULL, FUN = mean, omitGroupingFactor = TRUE, ...) { coefs <- lapply(object, coef) non.null <- !unlist(lapply(coefs, is.null)) if (sum(non.null) > 0) { template <- coefs[non.null][[1]] ## different parameter sets may be estimated for different subsets of data ... allnames <- Reduce(union,lapply(coefs[non.null],names)) if (is.numeric(template)) { co <- matrix(NA, ncol = length(allnames), nrow = length(coefs), dimnames = list(names(object), allnames)) for (i in names(object)) { co[i,names(coefs[[i]])] <- coefs[[i]] } coefs <- as.data.frame(co) effectNames <- names(coefs) ## if(augFrame) { ## if (is.null(data)) { ## data <- getData(object) ## } ## data <- as.data.frame(data) ## if (is.null(which)) { ## which <- 1:ncol(data) ## } ## data <- data[, which, drop = FALSE] ## ## eliminating columns with same names as effects ## data <- data[, is.na(match(names(data), effectNames)), drop = FALSE] ## data <- gsummary(data, FUN = FUN, groups = getGroups(object)) ## if (omitGroupingFactor) { ## data <- data[, is.na(match(names(data), ## names(getGroupsFormula(object, ## asList = TRUE)))), ## drop = FALSE] ## } ## if (length(data) > 0) { ## coefs <- cbind(coefs, data[row.names(coefs),,drop = FALSE]) ## } ## } attr(coefs, "level") <- attr(object, "level") attr(coefs, "label") <- "Coefficients" attr(coefs, "effectNames") <- effectNames attr(coefs, "standardized") <- FALSE } ## is.numeric(template) } ## sum(non.null)>0 coefs } pooledSD <- function(x, ...) { stopifnot(is(x, "lmList")) sumsqr <- apply(sapply(x, function(el) { if (is.null(el)) { c(0,0) } else { res <- resid(el) c(sum(res^2), length(res) - length(coef(el))) } }), 1, sum) if (sumsqr[2] == 0) { stop("No degrees of freedom for estimating std. dev.") } val <- sqrt(sumsqr[1]/sumsqr[2]) attr(val, "df") <- sumsqr[2] val } ##' @importFrom methods show ##' @exportMethod show setMethod("show", signature(object = "lmList"), function(object) { mCall <- object@call cat("Call:", deparse(mCall), "\n") cat("Coefficients:\n") invisible(print(coef(object))) if (object@pool) { cat("\n") poolSD <- pooledSD(object) dfRes <- attr(poolSD, "df") RSE <- c(poolSD) cat("Degrees of freedom: ", length(unlist(lapply(object, fitted))), " total; ", dfRes, " residual\n", sep = "") cat("Residual standard error:", format(RSE)) cat("\n") } }) ##' @S3method confint lmList confint.lmList <- function(object, parm, level = 0.95, ...) { mCall <- match.call() if (length(object) < 1) return(new("lmList.confint", array(numeric(0), c(0,0,0)))) mCall$object <- object[[1]] ## the old recursive strategy doesn't work with S3 objects -- ## calls "confint.lmList" again instead of calling "confint" mCall[[1]] <- quote(confint) ## confint.glm() returns a data frame -- must cast to matrix! template <- as.matrix(eval(mCall)) val <- array(template, c(dim(template), length(object)), c(dimnames(template), list(names(object)))) pool <- list(...)$pool if (is.null(pool)) pool <- object$pool if (length(pool) > 0 && pool[1]) { sd <- pooledSD(object) a <- (1 - level)/2 fac <- sd * qt(c(a, 1 - a)/2, attr(sd, "df")) parm <- dimnames(template)[[1]] for (i in seq_along(object)) val[ , , i] <- coef(object[[i]])[parm] + sqrt(diag(summary(object[[i]], corr = FALSE)$cov.unscaled )[parm]) %o% fac } else { for (i in seq_along(object)) { mCall$object <- object[[i]] val[ , , i] <- eval(mCall) } } new("lmList.confint", aperm(val, c(3, 2, 1))) } ##' @importFrom graphics plot ##' @S3method plot lmList.confint plot.lmList.confint <- function(x, y, ...) { ## stopifnot(require("lattice")) arr <- as(x, "array") dd <- dim(arr) dn <- dimnames(arr) levs <- dn[[1]] dots <- list(...) if (length(dots$order) > 0 && (ord <- round(dots$order[1])) %in% seq(dd[3])) levs <- levs[order(rowSums(arr[ , , ord]))] ll <- length(arr) df <- data.frame(group = ordered(rep(dn[[1]], dd[2] * dd[3]), levels = levs), intervals = as.vector(arr), what = gl(dd[3], dd[1] * dd[2], length = ll, labels = dn[[3]]), end = gl(dd[2], dd[1], length = ll)) strip <- dots[["strip"]] if (is.null(strip)) { strip <- function(...) strip.default(..., style = 1) } xlab <- dots[["xlab"]] if (is.null(xlab)) xlab <- "" ylab <- dots[["ylab"]] if (is.null(ylab)) ylab <- "" dotplot(group ~ intervals | what, data = df, scales = list(x="free"), strip = strip, xlab = xlab, ylab = ylab, panel = function(x, y, pch = dot.symbol$pch, col = dot.symbol$col, cex = dot.symbol$cex, font = dot.symbol$font, ...) { x <- as.numeric(x) y <- as.numeric(y) ok <- !is.na(x) & !is.na(y) yy <- y[ok] xx <- x[ok] dot.symbol <- trellis.par.get("dot.symbol") dot.line <- trellis.par.get("dot.line") panel.abline(h = yy, lwd = dot.line$lwd, lty = dot.line$lty, col = dot.line$col) lpoints(xx, yy, pch = "|", col = col, cex = cex, font = font, ...) lower <- tapply(xx, yy, min) upper <- tapply(xx, yy, max) nams <- as.numeric(names(lower)) lsegments(lower, nams, upper, nams, col = 1, lty = 1, lwd = if (dot.line$lwd) { dot.line$lwd } else { 2 }) }, ...) } ##' @importFrom stats update ##' @S3method update lmList update.lmList <- function(object, formula., ..., evaluate = TRUE) { call <- object@call if (is.null(call)) stop("need an object with call slot") extras <- match.call(expand.dots = FALSE)$... if (!missing(formula.)) call$formula <- update.formula(formula(object), formula.) if (length(extras) > 0) { existing <- !is.na(match(names(extras), names(call))) for (a in names(extras)[existing]) call[[a]] <- extras[[a]] if (any(!existing)) { call <- c(as.list(call), extras[!existing]) call <- as.call(call) } } if (evaluate) eval(call, parent.frame()) else call } ##' @importFrom stats formula ##' @S3method formula lmList formula.lmList <- function(x, ...) x@call[["formula"]] lme4/R/lmerControl.R0000644000176000001440000002054112232467515014000 0ustar ripleyusersnamedList <- function(...) { L <- list(...) snm <- sapply(substitute(list(...)),deparse)[-1] if (is.null(nm <- names(L))) nm <- snm if (any(nonames <- nm=="")) nm[nonames] <- snm[nonames] setNames(L,nm) } ## TESTING: ## a <- b <- c <- 1 ## namedList(a,b,c) ## namedList(a,b,d=c) ## namedList(e=a,f=b,d=c) ##' @title Control of mixed model fitting ##' @param optimizer character - name of optimizing function(s). ##' A character vector or list of functions: length 1 for \code{lmer} ##' or \code{glmer}, possibly length 2 for \code{glmer}). The built-in optimizers are ##' \code{\link{Nelder_Mead}} and \code{\link[minqa]{bobyqa}} (from ##' the \pkg{minqa} package). Any minimizing function that allows ##' box constraints can be used ##' provided that it (1) takes input parameters \code{fn} (function ##' to be optimized), \code{par} (starting parameter values), ##' \code{lower} (lower bounds) and \code{control} (control parameters, ##' passed through from the \code{control} argument) and (2) ##' returns a list with (at least) elements \code{par} (best-fit ##' parameters), \code{fval} (best-fit function value), \code{conv} ##' (convergence code) and (optionally) \code{message} (informational ##' message, or explanation of convergence failure). ##' Special provisions are made for \code{\link{bobyqa}}, ##' \code{\link{Nelder_Mead}}, and optimizers wrapped in ##' the \pkg{optimx} package; to use \pkg{optimx} optimizers ##' (including \code{L-BFGS-B} from base \code{\link{optim}} and ##' \code{\link{nlminb}}), pass the \code{method} argument to \code{optim} ##' in the \code{control} argument. ##' ##' For \code{glmer}, if \code{length(optimizer)==2}, the first element will be used ##' for the preliminary (random effects parameters only) optimization, while ##' the second will be used for the final (random effects plus ##' fixed effect parameters) phase. See \code{\link{modular}} for more information on ##' these two phases. ##' @param sparseX logical - should a sparse model matrix be used for the ##' fixed-effects terms? Defaults to \code{FALSE}. Currently inactive. ##' @param restart_edge logical - should the optimizer attempt a restart when it finds a solution at the boundary (i.e. zero random-effect variances or perfect +/-1 correlations)? ##' @param check.nlev.gtreq.5 character - rules for checking whether all random effects have >= 5 levels. "ignore": skip the test. "warn": warn if test fails. "stop": throw an error if test fails. ##' @param check.nlev.gtr.1 character - rules for checking whether all random effects have > 1 level. As for \code{check.nlevel.gtr.5}. ##' @param check.nobs.vs.rankZ character - rules for checking whether the number of observations is greater than (or greater than or equal to) the rank of the random effects design matrix (Z), usually necessary for identifiable variances. As for \code{check.nlevel.gtreq.5}, with the addition of "warnSmall" and "stopSmall", which run the test only if the dimensions of \code{Z} are <1e6. \code{nobs>rank(Z)} will be tested for LMMs and GLMMs with estimated scale parameters; \code{nobs>=rank(Z)} will be tested for GLMMs with fixed scale parameter. ## FIXME: add (something like) allow.nobs.eq.nlev (and/or rankZ), and ## add better logic for distinguishing whether the scale parameter is ## being estimated ##' @param check.nobs.vs.nlev character - rules for checking whether the number of observations is less than (or less than or equal to) the number of levels of every grouping factor, usually necessary for identifiable variances. As for \code{check.nlevel.gtreq.5}: \code{nobs>= options(width=65,digits=5) #library(lme4) @ \maketitle \begin{abstract} The \package{lme4} package provides R functions to fit and analyze several different types of mixed-effects models, including linear mixed models, generalized linear mixed models and nonlinear mixed models. In this vignette we describe the formulation of these models and the computational approach used to evaluate or approximate the log-likelihood of a model/data/parameter value combination. \end{abstract} \section{Introduction} \label{sec:intro} The \package{lme4} package provides \code{R} functions to fit and analyze linear mixed models, generalized linear mixed models and nonlinear mixed models. These models are called \emph{mixed-effects models} or, more simply, \emph{mixed models} because they incorporate both \emph{fixed-effects} parameters, which apply to an entire population or to certain well-defined and repeatable subsets of a population, and \emph{random effects}, which apply to the particular experimental units or observational units in the study. Such models are also called \emph{multilevel} models because the random effects represent levels of variation in addition to the per-observation noise term that is incorporated in common statistical models such as linear regression models, generalized linear models and nonlinear regression models. We begin by describing common properties of these mixed models and the general computational approach used in the \package{lme4} package. The estimates of the parameters in a mixed model are determined as the values that optimize an objective function --- either the likelihood of the parameters given the observed data, for maximum likelihood (ML) estimates, or a related objective function called the REML criterion. Because this objective function must be evaluated at many different values of the model parameters during the optimization process, we focus on the evaluation of the objective function and a critical computation in this evalution --- determining the solution to a penalized, weighted least squares (PWLS) problem. The dimension of the solution of the PWLS problem can be very large, perhaps in the millions. Furthermore, such problems must be solved repeatedly during the optimization process to determine parameter estimates. The whole approach would be infeasible were it not for the fact that the matrices determining the PWLS problem are sparse and we can use sparse matrix storage formats and sparse matrix computations \citep{davis06:csparse_book}. In particular, the whole computational approach hinges on the extraordinarily efficient methods for determining the Cholesky decomposition of sparse, symmetric, positive-definite matrices embodied in the CHOLMOD library of C functions \citep{Cholmod}. % The three types of mixed models -- linear, generalized linear and % nonlinear -- share common characteristics in that the model is % specified in whole or in part by a \emph{mixed model formula} that % describes a \emph{linear predictor} and a variance-covariance % structure for the random effects. In the next section we describe % the mixed model formula and the forms of these matrices. The % following section presents a general formulation of the Laplace % approximation to the log-likelihood of a mixed model. % In subsequent sections we describe computational methods for specific % kinds of mixed models. In particular, we should how a profiled % log-likelihood for linear mixed models, and for some nonlinear mixed % models, can be evaluated exactly. In the next section we describe the general form of the mixed models that can be represented in the \package{lme4} package and the computational approach embodied in the package. In the following section we describe a particular form of mixed model, called a linear mixed model, and the computational details for those models. In the fourth section we describe computational methods for generalized linear mixed models, nonlinear mixed models and generalized nonlinear mixed models. \section{Formulation of mixed models} \label{sec:form-mixed-models} A mixed-effects model incorporates two vector-valued random variables: the $n$-dimensional response vector, $\bc Y$, and the $q$-dimensional random effects vector, $\bc B$. We observe the value, $\bm y$, of $\bc Y$. We do not observe the value of $\bc B$. The random variable $\bc Y$ may be continuous or discrete. That is, the observed data, $\bm y$, may be on a continuous scale or they may be on a discrete scale, such as binary responses or responses representing a count. In our formulation, the random variable $\bc B$ is always continous. We specify a mixed model by describing the unconditional distribution of $\bc B$ and the conditional distribution $(\bc Y|\bc B=\bm b)$. \subsection{The unconditional distribution of $\bc B$} \label{sec:uncond-distr-B} In our formulation, the unconditional distribution of $\bc B$ is always a $q$-dimensional multivariate Gaussian (or ``normal'') distribution with mean $\bm 0$ and with a parameterized covariance matrix, \begin{equation} \label{eq:2} \bc B\sim\mathcal{N}\left(\bm 0,\sigma^2\bm\Lambda(\bm\theta) \bm\Lambda\trans(\bm\theta)\right) . \end{equation} The scalar, $\sigma$, in (\ref{eq:2}), is called the \emph{common scale parameter}. As we will see later, not all types of mixed models incorporate this parameter. We will include $\sigma^2$ in the general form of the unconditional distribution of $\bc B$ with the understanding that, in some models, $\sigma\equiv 1$. The $q\times q$ matrix $\bm\Lambda(\bm\theta)$, which is a left factor of the covariance matrix (when $\sigma=1$) or the relative covariance matrix (when $\sigma\ne 1$), depends on an $m$-dimensional parameter $\bm\theta$. Typically $m\ll q$; in the examples we show below it is always the case that $m<5$, even when $q$ is in the thousands. The fact that $m$ is very small is important because, as we shall see, determining the parameter estimates in a mixed model can be expressed as an optimization problem with respect to $\bm\theta$ only. The parameter $\bm\theta$ may be, and typically is, subject to constraints. For ease of computation, we require that the constraints be expressed as ``box'' constraints of the form $\theta_{iL}\le\theta_i\le\theta_{iU},i=1,\dots,m$ for constants $\theta_{iL}$ and $\theta_{iU}, i=1,\dots,m$. We shall write the set of such constraints as $\bm\theta_L\le\bm\theta\le\bm\theta_R$. The matrix $\bm\Lambda(\bm\theta)$ is required to be non-singular (i.e.{} invertible) when $\bm\theta$ is not on the boundary. \subsection{The conditional distribution, $(\bc Y|\bc B=\bm b)$} \label{sec:cond-distr-YB} The conditional distribution, $(\bc Y|\bc B=\bm b)$, must satisfy: \begin{enumerate} \item The conditional mean, $\bm\mu_{\bc Y|\bc B}(\bm b) = \mathrm{E}[\bc Y|\bc B=\bm b]$, depends on $\bm b$ only through the value of the \emph{linear predictor}, $\bm Z\bm b+\bm X\bm\beta$, where $\bm\beta$ is the $p$-dimensional \emph{fixed-effects} parameter vector and the \emph{model matrices}, $\bm Z$ and $\bm X$, are fixed matrices of the appropriate dimension. That is, the two model matrices must have the same number of rows and must have $q$ and $p$ columns, respectively. The number of rows in $\bm Z$ and $\bm X$ is a multiple of $n$, the dimension of $\bm y$. \item The scalar distributions, $(\mathcal{Y}_i|\bc B=\bm b),i=1,\dots,n$, all have the same form and are completely determined by the conditional mean, $\bm\mu_{\bc Y|\bc B}(\bm b)$ and, at most, one additional parameter, $\sigma$, which is the common scale parameter. \item The scalar distributions, $(\mathcal{Y}_i|\bc B=\bm b),i=1,\dots,n$, are independent. That is, the components of $\bc Y$ are \emph{conditionally independent} given $\bc B$. \end{enumerate} An important special case of the conditional distribution is the multivariate Gaussian distribution of the form \begin{equation} \label{eq:1} (\bc Y|\bc B=\bm b)\sim\mathcal{N}(\bm Z\bm b+\bm X\bm\beta,\sigma^2\bm I_n) \end{equation} where $\bm I_n$ denotes the identity matrix of size $n$. In this case the conditional mean, $\bm\mu_{\bc Y|\bc B}(\bm b)$, is exactly the linear predictor, $\bm Z\bm b+\bm X\bm\beta$, a situation we will later describe as being an ``identity link'' between the conditional mean and the linear predictor. Models with conditional distribution (\ref{eq:1}) are called \emph{linear mixed models}. \subsection{A change of variable to ``spherical'' random effects} \label{sec:change-vari-spher} Because the conditional distribution $(\bc Y|\bc B=\bm b)$ depends on $\bm b$ only through the linear predictor, it is easy to express the model in terms of a linear transformation of $\bc B$. We define the linear transformation from a $q$-dimensional ``spherical'' Gaussian random variable, $\bc U$, to $\bc B$ as \begin{equation} \label{eq:3} \bc B=\bm\Lambda(\bm\theta)\bc U,\quad \bc U\sim\mathcal{N}(\bm 0,\sigma^2\bm I_q). \end{equation} (The term ``spherical'' refers to the fact that contours of constant probability density for $\bc U$ are spheres centered at the mean --- in this case, $\bm0$.) When $\bm\theta$ is not on the boundary this is an invertible transformation. When $\bm\theta$ is on the boundary the transformation can fail to be invertible. However, we will only need to be able to express $\bc B$ in terms of $\bc U$ and that transformation is well-defined, even when $\bm\theta$ is on the boundary. The linear predictor, as a function of $\bm u$, is \begin{equation} \label{eq:4} \bm\gamma(\bm u)=\bm Z\bm\Lambda(\bm\theta)\bm u + \bm X\bm\beta. \end{equation} When we wish to emphasize the role of the model parameters, $\bm\theta$ and $\bm\beta$, in the formulation of $\bm\gamma$, we will write the linear predictor as $\bm\gamma(\bm u,\bm\theta,\bm\beta)$. \subsection{The conditional density $(\bc U|\bc Y=\bm y)$} \label{sec:cond-dens-bc} Because we observe $\bm y$ and do not observe $\bm b$ or $\bm u$, the conditional distribution of interest, for the purposes of statistical inference, is $(\bc U|\bc Y=\bm y)$ (or, equivalently, $(\bc B|\bc Y=\bm y)$). This conditional distribution is always a continuous distribution with conditional probability density $f_{\bc U|\bc Y}(\bm u|\bm y)$. We can evaluate $f_{\bc U|\bc Y}(\bm u|\bm y)$ , up to a constant, as the product of the unconditional density, $f_{\bc U}(\bm u)$, and the conditional density (or the probability mass function, whichever is appropriate), $f_{\bc Y|\bc U}(\bm y|\bm u)$. We write this unnormalized conditional density as \begin{equation} \label{eq:5} h(\bm u|\bm y,\bm\theta,\bm\beta,\sigma) = f_{\bc Y|\bc U}(\bm y|\bm u,\bm\theta,\bm\beta,\sigma) f_{\bc U}(\bm u|\sigma) . \end{equation} We say that $h$ is the ``unnormalized'' conditional density because all we know is that the conditional density is proportional to $h(\bm u|\bm y,\bm\theta,\bm\beta,\sigma)$. To obtain the conditional density we must normalize $h$ by dividing by the value of the integral \begin{equation} \label{eq:6} L(\bm\theta,\bm\beta,\sigma|\bm y) = \int_{\mathbb{R}^q}h(\bm u|\bm y,\bm\theta,\bm\beta,\sigma)\,d\bm u . \end{equation} We write the value of the integral (\ref{eq:6}) as $L(\bm\theta,\bm\beta,\sigma|\bm y)$ because it is exactly the \emph{likelihood} of the parameters $\bm\theta$, $\bm\beta$ and $\sigma$, given the observed data $\bm y$. The \emph{maximum likelihood (ML) estimates} of these parameters are the values that maximize $L$. \subsection{Determining the ML estimates} \label{sec:DeterminingML} The general problem of maximizing $L(\bm\theta,\bm\beta,\sigma|\bm y)$ with respect to $\bm\theta$, $\bm\beta$ and $\sigma$ can be formidable because each evaluation of this function involves a potentially high-dimensional integral and because the dimension of $\bm\beta$ can be large. However, this general optimization problem can be split into manageable subproblems. Given a value of $\bm\theta$ we can determine the \emph{conditional mode}, $\tilde{\bm u}(\bm\theta)$, of $\bm u$ and the \emph{conditional estimate}, $\tilde{\bm\beta}(\bm\theta)$ simultaneously using \emph{penalized, iteratively re-weighted least squares} (PIRLS). The conditional mode and the conditional estimate are defined as \begin{equation} \label{eq:condmode} \begin{bmatrix} \tilde{\bm u}(\bm\theta)\\ \tilde{\bm\beta}(\bm\theta) \end{bmatrix}=\arg\max_{\bm u,\bm\beta}h(\bm u|\bm y,\bm\theta,\bm\beta,\sigma) . \end{equation} (It may look as if we have missed the dependence on $\sigma$ on the left-hand side but it turns out that the scale parameter does not affect the location of the optimal values of quantities in the linear predictor.) As is common in such optimization problems, we re-express the conditional density on the \emph{deviance scale}, which is negative twice the logarithm of the density, where the optimization becomes \begin{equation} \label{eq:condmode2} \begin{bmatrix} \tilde{\bm u}(\bm\theta)\\ \tilde{\bm\beta}(\bm\theta) \end{bmatrix}=\arg\min_{\bm u,\bm\beta}-2\log\left(h(\bm u|\bm y,\bm\theta,\bm\beta,\sigma)\right) . \end{equation} It is this optimization problem that can be solved quite efficiently using PIRLS. In fact, for linear mixed models, which are described in the next section, $\tilde{\bm u}(\bm\theta)$ and $\tilde{\bm\beta}(\bm\theta)$ can be directly evaluated. The second-order Taylor series expansion of $-2\log h$ at $\tilde{\bm u}(\bm\theta)$ and $\tilde{\bm\beta}(\bm\theta)$ provides the Laplace approximation to the profiled deviance. Optimizing this function with respect to $\bm\theta$ provides the ML estimates of $\bm\theta$, from which the ML estimates of $\bm\beta$ and $\sigma$ (if used) are derived. \section{Methods for linear mixed models} \label{sec:pwls-problem} As indicated in the introduction, a critical step in our methods for determining the maximum likelihood estimates of the parameters in a mixed model is solving a penalized, weighted least squares (PWLS) problem. We will motivate the general form of the PWLS problem by first considering computational methods for linear mixed models that result in a penalized least squares (PLS) problem. Recall from \S\ref{sec:cond-distr-YB} that, in a linear mixed model, both the conditional distribution, $(\bc Y|\bc U=\bm u)$, and the unconditional distribution, $\bc U$, are spherical Gaussian distributions and that the conditional mean, $\bm\mu_{\bc Y|\bc U}(\bm u)$, is the linear predictor, $\bm\gamma(\bm u)$. Because all the distributions determining the model are continuous distributions, we consider their densities. On the deviance scale these are \begin{equation} \label{eq:7} \begin{aligned} -2\log(f_{\bc U}(\bm u))&=q\log(2\pi\sigma^2)+\frac{\|\bm u\|^2}{\sigma^2}\\ -2\log(f_{\bc Y|\bc U}(\bm y|\bm u))&=n\log(2\pi\sigma^2)+ \frac{\|\bm y-\bm Z\bm\Lambda(\bm\theta)\bm u-\bm X\bm\beta\|^2}{\sigma^2}\\ -2\log(h(\bm u|\bm y,\bm\theta,\bm\beta,\sigma)) &= (n+q)\log(2\pi\sigma^2)+ \frac{\|\bm y-\bm\gamma(\bm u,\bm\theta,\bm\beta)\|^2+\|\bm u\|^2}{\sigma^2}\\ &= (n+q)\log(2\pi\sigma^2)+ \frac{d(\bm u|\bm y,\bm\theta,\bm\beta)}{\sigma^2} \end{aligned} \end{equation} In (\ref{eq:7}) the \emph{discrepancy} function, \begin{equation} \label{eq:9} d(\bm u|\bm y,\bm\theta,\bm\beta) = \|\bm y-\bm\gamma(\bm u,\bm\theta,\bm\beta)\|^2+\|\bm u\|^2 \end{equation} has the form of a penalized residual sum of squares in that the first term, $\|\bm y-\bm\gamma(\bm u,\bm\theta,\bm\beta)\|^2$ is the residual sum of squares for $\bm y$, $\bm u$, $\bm\theta$ and $\bm\beta$ and the second term, $\|\bm u\|^2$, is a penalty on the size of $\bm u$. Notice that the discrepancy does not depend on the common scale parameter, $\sigma$. \subsection{The canonical form of the discrepancy} \label{sec:conditional-mode-bm} Using a so-called ``pseudo data'' representation, we can write the discrepancy as a residual sum of squares for a regression model that is linear in both $\bm u$ and $\bm\beta$ \begin{equation} \label{eq:10} d(\bm u|\bm y,\bm\theta,\bm\beta) =\left\| \begin{bmatrix} \bm y\\\bm 0 \end{bmatrix} - \begin{bmatrix} \bm Z\bm\Lambda(\bm\theta) & \bm X \\ \bm I_q & \bm0 \end{bmatrix} \begin{bmatrix}\bm u\\\bm\beta\end{bmatrix} \right\|^2 . \end{equation} The term ``pseudo data'' reflects the fact that we have added $q$ ``pseudo observations'' to the observed response, $\bm y$, and to the linear predictor, $\bm\gamma(\bm u,\bm\theta,\bm\beta)=\bm Z\bm\Lambda(\bm\theta)\bm u+\bm X\bm\beta$, in such a way that their contribution to the overall residual sum of squares is exactly the penalty term in the discrepancy. In the form (\ref{eq:10}) we can see that the discrepancy is a quadratic form in both $\bm u$ and $\bm\beta$. Furthermore, because we require that $\bm X$ has full column rank, the discrepancy is a positive-definite quadratic form in $\bm u$ and $\bm\beta$ that is minimized at $\tilde{\bm u}(\bm\theta)$ and $\tilde{\bm\beta}(\bm\theta)$ satisfying \begin{equation} \label{eq:13} \begin{bmatrix} \bm\Lambda\trans(\bm\theta)\bm Z\trans\bm Z\bm\Lambda(\theta) +\bm I_q&\bm\Lambda\trans(\bm\theta)\bm Z\trans\bm X\\ \bm X\trans\bm Z\bm\Lambda(\theta) &\bm X\trans\bm X \end{bmatrix} \begin{bmatrix} \tilde{\bm u}(\bm\theta)\\\tilde{\bm\beta}(\bm\theta) \end{bmatrix} = \begin{bmatrix} \bm\Lambda\trans(\bm\theta)\bm Z\trans\bm y\\ \bm X\trans\bm y \end{bmatrix} \end{equation} An effective way of determining the solution to a sparse, symmetric, positive definite system of equations such as (\ref{eq:13}) is the sparse Cholesky decomposition \citep{davis06:csparse_book}. If $\bm A$ is a sparse, symmetric positive definite matrix then the sparse Cholesky factor with fill-reducing permutation $\bm P$ is the lower-triangular matrix $\bm L$ such that \begin{equation} \label{eq:14} \bm L\bm L\trans=\bm P\bm A\bm P\trans . \end{equation} (Technically, the factor $\bm L$ is only determined up to changes in the sign of the diagonal elements. By convention we require the diagonal elements to be positive.) The fill-reducing permutation represented by the permutation matrix $\bm P$, which is determined from the pattern of nonzeros in $\bm A$ but does not depend on particular values of those nonzeros, can have a profound impact on the number of nonzeros in $\bm L$ and hence on the speed with which $\bm L$ can be calculated from $\bm A$. In most applications of linear mixed models the matrix $\bm Z\bm\Lambda(\bm\theta)$ is sparse while $\bm X$ is dense or close to it so the permutation matrix $\bm P$ can be restricted to the form \begin{equation} \label{eq:15} \bm P=\begin{bmatrix}\bm P_{\bm Z}&\bm0\\ \bm0&\bm P_{\bm X}\end{bmatrix} \end{equation} without loss of efficiency. In fact, in most cases we can set $\bm P_{\bm X}=\bm I_p$ without loss of efficiency. Let us assume that the permutation matrix is required to be of the form (\ref{eq:15}) so that we can write the Cholesky factorization for the positive definite system (\ref{eq:13}) as \begin{multline} \label{eq:16} \begin{bmatrix} \bm L_{\bm Z}&\bm0\\\bm L_{\bm{XZ}}&\bm L_{\bm X} \end{bmatrix} \begin{bmatrix} \bm L_{\bm Z}&\bm0\\\bm L_{\bm{XZ}}&\bm L_{\bm X} \end{bmatrix}\trans =\\ \begin{bmatrix}\bm P_{\bm Z}&\bm0\\ \bm0&\bm P_{\bm X}\end{bmatrix} \begin{bmatrix} \bm\Lambda\trans(\bm\theta)\bm Z\trans\bm Z\bm\Lambda(\theta) +\bm I_q&\bm\Lambda\trans(\bm\theta)\bm Z\trans\bm X\\ \bm X\trans\bm Z\bm\Lambda(\theta) &\bm X\trans\bm X \end{bmatrix} \begin{bmatrix}\bm P_{\bm Z}&\bm0\\ \bm0&\bm P_{\bm X}\end{bmatrix}\trans . \end{multline} The discrepancy can now be written in the canonical form \begin{equation} \label{eq:17} d(\bm u|\bm y,\bm\theta,\bm\beta) =\tilde{d}(\bm y,\bm\theta) + \left\| \begin{bmatrix} \bm L_{\bm Z}\trans&\bm L_{\bm{XZ}}\trans\\ \bm 0&\bm L_{\bm X}\trans \end{bmatrix} \begin{bmatrix} \bm P_{\bm Z}(\bm u-\tilde{\bm u})\\ \bm P_{\bm X}(\bm\beta-\tilde{\bm\beta}) \end{bmatrix} \right\|^2 \end{equation} where \begin{equation} \label{eq:18} \tilde{d}(\bm y,\bm\theta)= d(\tilde{\bm u}(\bm\theta)|\bm y,\bm\theta,\tilde{\bm\beta}(\bm\theta)) \end{equation} is the minimum discrepancy, given $\bm\theta$. \subsection{The profiled likelihood for linear mixed models} \label{sec:prof-log-likel} Substituting (\ref{eq:17}) into (\ref{eq:7}) provides the unnormalized conditional density $h(\bm u|\bm y,\bm\theta,\bm\beta,\sigma)$ on the deviance scale as \begin{multline} \label{eq:32} -2\log(h(\bm u|\bm y,\bm\theta,\bm\beta,\sigma))\\= (n+q)\log(2\pi\sigma^2)+\frac{\tilde{d}(\bm y,\bm\theta) + \left\| \begin{bmatrix} \bm L_{\bm Z}\trans&\bm L_{\bm{XZ}}\trans\\ \bm 0&\bm L_{\bm X}\trans \end{bmatrix} \begin{bmatrix} \bm P_{\bm Z}(\bm u-\tilde{\bm u})\\ \bm P_{\bm X}(\bm\beta-\tilde{\bm\beta}) \end{bmatrix} \right\|^2}{\sigma^2} . \end{multline} As shown in Appendix \ref{sec:integr-quadr-devi}, the integral of a quadratic form on the deviance scale, such as (\ref{eq:32}), is easily evaluated, providing the log-likelihood, $\ell(\bm\theta,\bm\beta,\sigma|\bm y)$, as \begin{multline} \label{eq:lmmdev} -2\ell(\bm\theta,\bm\beta,\sigma|\bm y)\\ =-2\log\left(L(\bm\theta,\bm\beta,\sigma|\bm y)\right)\\ =n\log(2\pi\sigma^2)+\log(|\bm L_{\bm Z}|^2)+\frac{\tilde{d}(\bm y,\bm\theta) + \left\|\bm L_{\bm X}\trans\bm P_{\bm X}(\bm\beta-\tilde{\bm\beta})\right\|^2}{\sigma^2}, \end{multline} from which we can see that the conditional estimate of $\bm\beta$, given $\bm\theta$, is $\tilde{\bm\beta}(\bm\theta)$ and the conditional estimate of $\sigma$, given $\bm\theta$, is \begin{equation} \label{eq:condsigma} \tilde{\sigma^2}(\bm\theta)= \frac{\tilde{d}(\bm\theta|\bm y)}{n} . \end{equation} Substituting these conditional estimates into (\ref{eq:lmmdev}) produces the \emph{profiled likelihood}, $\tilde{L}(\bm\theta|\bm y)$, as \begin{equation} \label{eq:19} -2\tilde{\ell}(\bm\theta|\bm y))= \log(|\bm L_{\bm Z}(\bm\theta)|^2)+ n\left(1+\log\left(\frac{2\pi\tilde{d}(\bm y,\bm\theta)}{n}\right)\right) . \end{equation} The maximum likelihood estimate of $\bm\theta$ can then be expressed as \begin{equation} \label{eq:29} \widehat{\bm\theta}_L=\arg\min_{\bm\theta} \left(-2\tilde{\ell}(\bm\theta|\bm y)\right) . \end{equation} from which the ML estimates of $\sigma^2$ and $\bm\beta$ are evaluated as \begin{align} \label{eq:30} \widehat{\sigma^2_L}&= \frac{\tilde{d}(\widehat{\bm\theta}_L,\bm y)}{n}\\ \widehat{\bm\beta}_L&=\tilde{\bm\beta}(\widehat{\bm\theta}_L) . \end{align} The important thing to note about optimizing the profiled likelihood, (\ref{eq:19}), is that it is a $m$-dimensional optimization problem and typically $m$ is very small. \subsection{The REML criterion} \label{sec:reml-criterion} In practice the so-called REML estimates of variance components are often preferred to the maximum likelihood estimates. (``REML'' can be considered to be an acronym for ``restricted'' or ``residual'' maximum likelihood, although neither term is completely accurate because these estimates do not maximize a likelihood.) We can motivate the use of the REML criterion by considering a linear regression model, \begin{equation} \label{eq:20} \bc Y\sim\mathcal{N}(\bm X\bm\beta,\sigma^2\bm I_n), \end{equation} in which we typically estimate $\sigma^2$ by \begin{equation} \label{eq:21} \widehat{\sigma^2_R}=\frac{\|\bm y-\bm X\widehat{\bm\beta}\|^2}{n-p} \end{equation} even though the maximum likelihood estimate of $\sigma^2$ is \begin{equation} \label{eq:22} \widehat{\sigma^2_{L}}=\frac{\|\bm y-\bm X\widehat{\bm\beta}\|^2}{n} . \end{equation} The argument for preferring $\widehat{\sigma^2_R}$ to $\widehat{\sigma^2_{L}}$ as an estimate of $\sigma^2$ is that the numerator in both estimates is the sum of squared residuals at $\widehat{\bm\beta}$ and, although the residual vector $\bm y-\bm X\bm\beta$ is an $n$-dimensional vector, the residual at $\widehat{\bm\theta}$ satisfies $p$ linearly independent constraints, $\bm X\trans(\bm y-\bm X\widehat{\bm\beta})=\bm 0$. That is, the residual at $\widehat{\bm\theta}$ is the projection of the observed response vector, $\bm y$, into an $(n-p)$-dimensional linear subspace of the $n$-dimensional response space. The estimate $\widehat{\sigma^2_R}$ takes into account the fact that $\sigma^2$ is estimated from residuals that have only $n-p$ \emph{degrees of freedom}. The REML criterion for determining parameter estimates $\widehat{\bm\theta}_R$ and $\widehat{\sigma_R^2}$ in a linear mixed model has the property that these estimates would specialize to $\widehat{\sigma^2_R}$ from (\ref{eq:21}) for a linear regression model. Although not usually derived in this way, the REML criterion can be expressed as \begin{equation} \label{eq:23} c_R(\bm\theta,\bm\sigma|\bm y)=-2\log \int_{\mathbb{R}^p}L(\bm u|\bm y,\bm\theta,\bm\beta,\sigma)\,d\bm\beta \end{equation} on the deviance scale. The REML estimates $\widehat{\bm\theta}_R$ and $\widehat{\sigma_R^2}$ minimize $c_R(\bm\theta,\bm\sigma|\bm y)$. The profiled REML criterion, a function of $\bm\theta$ only, is \begin{equation} \label{eq:24} \tilde{c}_R(\bm\theta|\bm y)= \log(|\bm L_{\bm Z}(\bm\theta)|^2|\bm L_{\bm X}(\bm\theta)|^2)+(n-p) \left(1+\log\left(\frac{2\pi\tilde{d}(\bm\theta|\bm y)}{n-p}\right)\right) \end{equation} and the REML estimate of $\bm\theta$ is \begin{equation} \label{eq:31} \widehat{\bm\theta}_R = \arg\min_{\bm\theta}\tilde{c}_R(\bm\theta,\bm y) . \end{equation} The REML estimate of $\sigma^2$ is $\widehat{\sigma^2_R}=\tilde{d}(\widehat{\bm\theta}_R|\bm y)/(n-p)$. It is not entirely clear how one would define a ``REML estimate'' of $\bm\beta$ because the REML criterion, $c_R(\bm\theta,\bm\sigma|\bm y)$, defined in (\ref{eq:23}), does not depend on $\bm\beta$. However, it is customary (and not unreasonable) to use $\widehat{\bm\beta}_R=\tilde{\bm\beta}(\widehat{\bm\theta}_R)$ as the REML estimate of $\bm\beta$. Note that the profiled REML criterion can be evaluated from a sparse Cholesky decomposition like that in (\ref{eq:16}) but without the requirement that the permutation can be applied to the columns of $\bm Z\bm\Lambda(\bm\theta)$ separately from the columnns of $\bm X$. That is, we can use a general fill-reducing permutation rather than the specific form (\ref{eq:15}) with separate permutations represented by $\bm P_{\bm Z}$ and $\bm P_{\bm X}$. This can be useful in cases where both $\bm Z$ and $\bm X$ are large and sparse. \subsection{Summary for linear mixed models} \label{sec:lmmsummary} A linear mixed model is characterized by the conditional distribution \begin{equation} \label{eq:lmmcond} (\bc Y|\bc U=\bm u)\sim\mathcal{N}(\bm\gamma(\bm u,\bm\theta,\bm\beta),\sigma^2\bm I_n)\text{ where } \bm\gamma(\bm u,\bm\theta,\bm\beta)=\bm Z\bm\Lambda(\bm\theta)\bm u+\bm X\bm\beta \end{equation} and the unconditional distribution $\bc U\sim\mathcal{N}(\bm 0,\sigma^2\bm I_q)$. The discrepancy function, \begin{displaymath} d(\bm u|\bm y,\bm\theta,\bm\beta)= \left\|\bm y-\bm\gamma(\bm u,\bm\theta,\bm\beta)\right\|^2+\|\bm u\|^2, \end{displaymath} is minimized at the conditional mode, $\tilde{\bm u}(\bm\theta)$, and the conditional estimate, $\tilde{\bm\beta}(\bm\theta)$, which are the solutions to the sparse, positive-definite linear system \begin{displaymath} \begin{bmatrix} \bm\Lambda\trans(\bm\theta)\bm Z\trans\bm Z\bm\Lambda(\theta) +\bm I_q&\bm\Lambda\trans(\bm\theta)\bm Z\trans\bm X\\ \bm X\trans\bm Z\bm\Lambda(\theta) &\bm X\trans\bm X \end{bmatrix} \begin{bmatrix} \tilde{\bm u}(\bm\theta)\\\tilde{\bm\beta}(\bm\theta) \end{bmatrix} = \begin{bmatrix} \bm\Lambda\trans(\bm\theta)\bm Z\trans\bm y\\ \bm X\trans\bm y \end{bmatrix} . \end{displaymath} In the process of solving this system we create the sparse left Cholesky factor, $L_{\bm Z}(\bm\theta)$, which is a lower triangular sparse matrix satisfying \begin{displaymath} \bm L_{\bm Z}(\bm\theta)\bm L_{\bm Z}(\bm\theta)\trans=\bm P_{\bm Z}\left(\bm\Lambda\trans(\bm\theta)\bm Z\trans\bm Z\bm\Lambda(\theta)+\bm I_q\right)\bm P_{\bm Z}\trans \end{displaymath} where $\bm P_{\bm Z}$ is a permutation matrix representing a fill-reducing permutation formed from the pattern of nonzeros in $\bm Z\bm\Lambda(\bm\theta)$ for any $\bm\theta$ not on the boundary of the parameter region. (The values of the nonzeros depend on $\bm\theta$ but the pattern doesn't.) The profiled log-likelihood, $\tilde{\ell}(\bm\theta|\bm y)$, is \begin{displaymath} -2\tilde{\ell}(\bm\theta|\bm y)= \log(|\bm L_{\bm Z}(\bm\theta)|^2)+ n\left(1+\log\left(\frac{2\pi\tilde{d}(\bm y,\bm\theta)}{n}\right)\right) \end{displaymath} where $\tilde{d}(\bm y,\bm\theta)=d(\tilde{\bm u}(\bm\theta)|\bm y,\tilde{\bm\beta}(\bm\theta),\bm\theta)$. \section{Generalizing the discrepancy function} \label{sec:generalizations} Because one of the factors influencing the choice of implementation for linear mixed models is the extent to which the methods can also be applied to other mixed models, we describe several other classes of mixed models before discussing the implementation details for linear mixed models. At the core of our methods for determining the maximum likelihood estimates (MLEs) of the parameters in the mixed model are methods for minimizing the discrepancy function with respect to the coefficients $\bm u$ and $\bm\beta$ in the linear predictor $\bm\gamma(\bm u,\bm\theta,\bm\beta)$. In this section we describe the general form of the discrepancy function that we will use and a penalized iteratively reweighted least squares (PIRLS) algorithm for determining the conditional modes $\tilde{\bm u}(\bm\theta)$ and $\tilde{\bm\beta}(\bm\theta)$. We then describe several types of mixed models and the form of the discrepancy function for each. \subsection{A weighted residual sum of squares} \label{sec:weighted} As shown in \S\ref{sec:conditional-mode-bm}, the discrepancy function for a linear mixed model has the form of a penalized residual sum of squares from a linear model (\ref{eq:10}). In this section we generalize that definition to \begin{equation} \label{eq:11} d(\bm u|\bm y,\bm\theta,\bm\beta) =\left\|\bm W^{1/2}(\bm\mu) \left[\bm y-\bm\mu_{\bc Y|\bc U}(\bm u,\bm\theta,\bm\beta)\right]\right\|^2+ \|\bm 0-\bm u\|^2 . \end{equation} where $\bm W$ is an $n\times n$ diagonal matrix, called the \emph{weights matrix}, with positive diagonal elements and $\bm W^{1/2}$ is the diagonal matrix with the square roots of the weights on the diagonal. The $i$th weight is inversely proportional to the conditional variances of $(\mathcal{Y}|\bc U=\bm u)$ and may depend on the conditional mean, $\bm\mu_{\bc Y|\bc U}$. We allow the conditional mean to be a nonlinear function of the linear predictor, but with certain restrictions. We require that the mapping from $\bm u$ to $\bm\mu_{\bc Y|\bc U=\bm u}$ be expressed as \begin{equation} \label{eq:uGammaEtaMu} \bm u\;\rightarrow\;\bm\gamma\;\rightarrow\;\bm\eta\;\rightarrow\;\bm\mu \end{equation} where $\bm\gamma=\bm Z\bm\Lambda(\bm\theta)\bm u+\bm X\bm\theta$ is an $ns$-dimensional vector ($s > 0$) while $\bm\eta$ and $\bm\mu$ are $n$-dimensional vectors. The map $\bm\eta\rightarrow\bm\mu$ has the property that $\mu_i$ depends only on $\eta_i$, $i=1,\dots,n$. The map $\bm\gamma\rightarrow\bm\eta$ has a similar property in that, if we write $\bm\gamma$ as an $n\times s$ matrix $\bm\Gamma$ such that \begin{equation} \label{eq:vecGamma} \bm\gamma=\vec{\bm\Gamma} \end{equation} (i.e.{} concatenating the columns of $\bm\Gamma$ produces $\bm\gamma$) then $\eta_i$ depends only on the $i$th row of $\bm\Gamma$, $i=1,\dots,n$. Thus the Jacobian matrix $\frac{d\bm\mu}{d\bm\eta\trans}$ is an $n\times n$ diagonal matrix and the Jacobian matrix $\frac{d\bm\eta}{d\bm\gamma\trans}$ is the horizontal concatenation of $s$ diagonal $n\times n$ matrices. For historical reasons, the function that maps $\eta_i$ to $\mu_i$ is called the \emph{inverse link} function and is written $\mu=g^{-1}(\eta)$. The \emph{link function}, naturally, is $\eta=g(\mu)$. When applied component-wise to vectors $\bm\mu$ or $\bm\eta$ we write these as $\bm\eta=\bm g(\bm\mu)$ and $\bm\mu=\bm g^{-1}(\bm\eta)$. Recall that the conditional distribution, $(\mathcal{Y}_i|\bc U=\bm u)$, is required to be independent of $(\mathcal{Y}_j|\bc U=\bm u)$ for $i,j=1,\dots,n,\,i\ne j$ and that all the component conditional distributions must be of the same form and differ only according to the value of the conditional mean. Depending on the family of the conditional distributions, the allowable values of the $\mu_i$ may be in a restricted range. For example, if the conditional distributions are Bernoulli then $0\le\mu_i\le1,i=1,\dots,n$. If the conditional distributions are Poisson then $0\le\mu_i,i=1,\dots,n$. A characteristic of the link function, $g$, is that it must map the restricted range to an unrestricted range. That is, a link function for the Bernoulli distribution must map $[0,1]$ to $[-\infty,\infty]$ and must be invertible within the range. The mapping from $\bm\gamma$ to $\bm\eta$ is defined by a function $m:\mathbb{R}^s\rightarrow\mathbb{R}$, called the \emph{nonlinear model} function, such that $\eta_i=m(\bm\gamma_i),i=1,\dots,n$ where $\bm\gamma_i$ is the $i$th row of $\bm\Gamma$. The vector-valued function is $\bm\eta=\bm m(\bm\gamma)$. Determining the conditional modes, $\tilde{\bm u}(\bm y|\bm\theta)$, and $\tilde{\bm\beta}(\bm y|\bm\theta)$, that jointly minimize the discrepancy, \begin{equation} \label{eq:12} \begin{bmatrix} \tilde{\bm u}(\bm y|\bm\theta)\\ \tilde{\bm\beta}(\bm y|\bm\theta) \end{bmatrix} =\arg\min_{\bm u,\bm\beta}\left[(\bm y-\bm\mu)\trans\bm W(\bm y-\bm\mu)+\|\bm u\|^2\right] \end{equation} becomes a weighted, nonlinear least squares problem except that the weights, $\bm W$, can depend on $\bm\mu$ and, hence, on $\bm u$ and $\bm\beta$. In describing an algorithm for linear mixed models we called $\tilde{\bm\beta}(\bm\theta)$ the \emph{conditional estimate}. That name reflects that fact that this is the maximum likelihood estimate of $\bm\beta$ for that particular value of $\bm\theta$. Once we have determined the MLE, $\widehat(\bm\theta)_L$ of $\bm\theta$, we have a ``plug-in'' estimator, $\widehat{\bm\beta}_L=\tilde{\bm\beta}(\bm\theta)$ for $\bm\beta$. This property does not carry over exactly to other forms of mixed models. The values $\tilde{\bm u}(\bm\theta)$ and $\tilde{\bm\beta}(\bm\theta)$ are conditional modes in the sense that they are the coefficients in $\bm\gamma$ that jointly maximize the unscaled conditional density $h(\bm u|\bm y,\bm\theta,\bm\beta,\sigma)$. Here we are using the adjective ``conditional'' more in the sense of conditioning on $\bc Y=\bm y$ than in the sense of conditioning on $\bm\theta$, although these values are determined for a fixed value of $\bm\theta$. \subsection{The PIRLS algorithm for $\tilde{\bm u}$ and $\tilde{\bm\beta}$} \label{sec:pirls-algor-tild} The penalized, iteratively reweighted, least squares (PIRLS) algorithm to determine $\tilde{\bm u}(\bm\theta)$ and $\tilde{\bm\beta}(\bm\theta)$ is a form of the Fisher scoring algorithm. We fix the weights matrix, $\bm W$, and use penalized, weighted, nonlinear least squares to minimize the penalized, weighted residual sum of squares conditional on these weights. Then we update the weights to those determined by the current value of $\bm\mu$ and iterate. To describe this algorithm in more detail we will use parenthesized superscripts to denote the iteration number. Thus $\bm u^{(0)}$ and $\bm\beta^{(0)}$ are the initial values of these parameters, while $\bm u^{(i)}$ and $\bm\beta^{(i)}$ are the values at the $i$th iteration. Similarly $\bm\gamma^{(i)}=\bm Z\bm\Lambda(\bm\theta)\bm u^{(i)}+\bm X\bm\beta^{(i)}$, $\bm\eta^{(i)}=\bm m(\bm\gamma^{(i)})$ and $\bm\mu^{(i)}=\bm g^{-1}(\bm\eta^{(i)})$. We use a penalized version of the Gauss-Newton algorithm \citep[ch.~2]{bateswatts88:_nonlin} for which we define the weighted Jacobian matrices \begin{align} \label{eq:Jacobian} \bm U^{(i)}&=\bm W^{1/2}\left.\frac{d\bm\mu}{d\bm u\trans}\right|_{\bm u=\bm u^{(i)},\bm\beta=\bm\beta^{(i)}}=\bm W^{1/2} \left.\frac{d\bm\mu}{d\bm\eta\trans}\right|_{\bm\eta^{(i)}} \left.\frac{d\bm\eta}{d\bm\gamma\trans}\right|_{\bm\gamma^{(i)}} \bm Z\bm\Lambda(\bm\theta)\\ \bm V^{(i)}&=\bm W^{1/2}\left.\frac{d\bm\mu}{d\bm\beta\trans}\right|_{\bm u=\bm u^{(i)},\bm\beta=\bm\beta^{(i)}}=\bm W^{1/2} \left.\frac{d\bm\mu}{d\bm\eta\trans}\right|_{\bm\eta^{(i)}} \left.\frac{d\bm\eta}{d\bm\gamma\trans}\right|_{\bm\gamma^{(i)}} \bm X \end{align} of dimension $n\times q$ and $n\times p$, respectively. The increments at the $i$th iteration, $\bm\delta_{\bm u}^{(i)}$ and $\bm\delta_{\bm\beta}^{(i)}$, are the solutions to \begin{equation} \label{eq:PNLSinc} \begin{bmatrix} {\bm U^{(i)}}\trans\bm U^{(i)}+\bm I_q&{\bm U^{(i)}}\trans\bm V^{(i)}\\ {\bm V^{(i)}}\trans\bm U^{(i)}&{\bm V^{(i)}}\trans\bm V^{(i)} \end{bmatrix} \begin{bmatrix} \bm\delta_{\bm u}^{(i)}\\ \bm\delta_{\bm\beta}^{(i)} \end{bmatrix} = \begin{bmatrix} {\bm U^{(i)}}\trans\bm W^{1/2}(\bm y-\bm\mu^{(i)})-\bm u^{(i)}\\ {\bm U^{(i)}}\trans\bm W^{1/2}(\bm y-\bm\mu^{(i)}) \end{bmatrix} \end{equation} providing the updated parameter values \begin{equation} \label{eq:33} \begin{bmatrix}\bm u^{(i+1)}\\\bm\beta^{(i+1)}\end{bmatrix}= \begin{bmatrix}\bm u^{(i)}\\\bm\beta^{(i)}\end{bmatrix}+\lambda \begin{bmatrix}\bm\delta_{\bm u}^{(i)}\\\bm\delta_{\bm\beta}^{(i)} \end{bmatrix} \end{equation} where $\lambda>0$ is a step factor chosen to ensure that \begin{equation} \label{eq:34} (\bm y-\bm\mu^{(i+1)})\trans\bm W(\bm y-\bm\mu^{(i+1)})+\|\bm u^{(i+1)}\|^2 < (\bm y-\bm\mu^{(i)})\trans\bm W(\bm y-\bm\mu^{(i)})+\|\bm u^{(i)}\|^2 . \end{equation} In the process of solving for the increments we form the sparse, lower triangular, Cholesky factor, $\bm L^{(i)}$, satisfying \begin{equation} \label{eq:35} \bm L^{(i)} {\bm L^{(i)}}\trans = \bm P_{\bm Z}\left({\bm U^{(i)}}\trans\bm U^{(i)}+ \bm I_n\right)\bm P_{\bm Z}\trans . \end{equation} After each successful iteration, determining new values of the coefficients, $\bm u^{(i+1)}$ and $\bm\beta^{(i+1)}$, that reduce the penalized, weighted residual sum of squqres, we update the weights matrix to $\bm W(\bm\mu^{(i+1)})$ and the weighted Jacobians, $\bm U^{(i+1)}$ and $\bm V^{(i+1)}$, then iterate. Convergence is determined according to the orthogonality convergence criterion~\citep[ch.~2]{bateswatts88:_nonlin}, suitably adjusted for the weights matrix and the penalty. \subsection{Weighted linear mixed models} \label{sec:weightedLMM} One of the simplest generalizations of linear mixed models is a weighted linear mixed model where $s=1$, the link function, $g$, and the nonlinear model function, $m$, are both the identity, the weights matrix, $\bm W$, is constant and the conditional distribution family is Gaussian. That is, the conditional distribution can be written \begin{equation} \label{eq:weightedLMM} (\bc Y|\bc U=\bm u)\sim\mathcal{N}(\bm\gamma(\bm u,\bm\theta,\bm\beta),\sigma^2\bm W^{-1}) \end{equation} with discrepancy function \begin{equation} \label{eq:wtddisc} d(\bm u|\bm y,\bm\theta,\bm\beta)=\left\|\bm W^{1/2}(\bm y-\bm Z\bm\Lambda(\bm\theta)\bm u-\bm X\bm\theta)\right\|^2+\|\bm u\|^2 . \end{equation} The conditional mode, $\tilde{\bm u}(\bm\theta)$, and the conditional estimate, $\tilde{\bm\beta}(\bm\theta)$, are the solutions to \begin{equation} \begin{bmatrix} \bm\Lambda\trans(\bm\theta)\bm Z\trans\bm W\bm Z\bm\Lambda(\theta) +\bm I_q&\bm\Lambda\trans(\bm\theta)\bm Z\trans\bm W\bm X\\ \bm X\trans\bm W\bm Z\bm\Lambda(\theta) &\bm X\trans\bm W\bm X \end{bmatrix} \begin{bmatrix} \tilde{\bm u}(\bm\theta)\\\tilde{\bm\beta}(\bm\theta) \end{bmatrix} = \begin{bmatrix} \bm\Lambda\trans(\bm\theta)\bm Z\trans\bm W\bm y\\ \bm X\trans\bm W\bm y \end{bmatrix} , \end{equation} which can be solved directly, and the Cholesky factor, $\bm L_{\bm Z}(\bm\theta)$, satisfies \begin{equation} \bm L_{\bm Z}(\bm\theta)\bm L_{\bm Z}(\bm\theta)\trans=\bm P_{\bm Z}\left(\bm\Lambda\trans(\bm\theta)\bm Z\trans\bm W\bm Z\bm\Lambda(\theta)+\bm I_q\right)\bm P_{\bm Z}\trans . \end{equation} The profiled log-likelihood, $\tilde{\ell}(\bm\theta|\bm y)$, is \begin{equation} \label{eq:wtdprofilelik} -2\tilde{\ell}(\bm\theta|\bm y)= \log\left(\frac{|\bm L_{\bm Z}(\bm\theta)|^2}{|\bm W|}\right)+ n\left(1+\log\left(\frac{2\pi\tilde{d}(\bm y,\bm\theta)}{n}\right)\right) . \end{equation} If the matrix $\bm W$ is fixed then we can ignore the term $|\bm W|$ in (\ref{eq:wtdprofilelik}) when determining the MLE, $\widehat{\bm\theta}_L$. However, in some models, we use a parameterized weight matrix, $\bm W(\bm\phi)$, and wish to determine the MLEs, $\widehat{\bm\phi}_L$ and $\widehat{\bm\theta}_L$ simultaneously. In these cases we must include the term involving $|\bm W(\bm\phi)|$ when evaluating the profiled log-likelihood. Note that we must define the parameterization of $\bm W(\bm\phi)$ such that $\sigma^2$ and $\bm\phi$ are not a redundant parameterization of $\sigma^2\bm W(\bm\phi)$. For example, we could require that the first diagonal element of $\bm W$ be unity. \subsection{Nonlinear mixed models} \label{sec:NLMMs} In an unweighted, nonlinear mixed model the conditional distribution is Gaussian, the link, $g$, is the identity and the weights matrix, $\bm W=\bm I_n$. That is, \begin{equation} \label{eq:conddistNLMM} (\bc Y|\bc U=\bm u)\sim\mathcal{N}(\bm m(\bm\gamma),\sigma^2\bm I_n) \end{equation} with discrepancy function \begin{equation} \label{eq:discNLMM} d(\bm u|\bm y,\bm\theta,\bm\beta)= \|\bm y-\bm\mu\|^2 + \|\bm u\|^2 . \end{equation} For a given value of $\bm\theta$ we determine the conditional modes, $\tilde{\bm u}(\bm\theta)$ and $\tilde{\bm\beta}(\bm\theta)$, as the solution to the penalized nonlinear least squares problem \begin{equation} \label{eq:NLMMpnls} \begin{bmatrix} \tilde{\bm u}(\bm\theta)\\ \tilde{\bm\beta}(\bm\theta) \end{bmatrix} = \arg\min_{\bm u,\bm\theta}d(\bm u|\bm y,\bm\theta,\bm\beta) \end{equation} and we write the minimum discrepancy, given $\bm y$ and $\bm\theta$, as \begin{equation} \label{eq:25} \tilde{d}(\bm y,\bm\theta)=d(\tilde{\bm u}(\bm\theta)|\bm y,\bm\theta,\tilde{\bm\beta}(\bm\theta)). \end{equation} Let $\tilde{\bm L}_Z(\bm\theta)$ and $\tilde{\bm L}_X(\bm\theta)$ be the Cholesky factors at $\bm\theta$, $\tilde{\bm\beta}(\bm\theta)$ and $\tilde{\bm u}(\bm\theta)$. Then the \emph{Laplace approximation} to the log-likelihood is \begin{equation} \label{eq:36} -2\ell_P(\bm\theta,\bm\beta,\sigma|\bm y)\approx n\log(2\pi\sigma^2)+\log(|\tilde{\bm L}_{\bm Z}|^2)+ \frac{\tilde{d}(\bm y,\bm\theta) + \left\|\tilde{\bm L}_{\bm X}\trans(\bm\beta-\tilde{\bm\beta})\right\|^2}{\sigma^2}, \end{equation} producing the approximate profiled log-likelihood, $\tilde{\ell}_P(\bm\theta|\bm y)$, \begin{equation} \label{eq:37} -2\tilde{\ell}_P(\bm\theta|\bm y)\approx \log(|\tilde{\bm L}_{\bm Z}|^2)+n\left(1+\log(2\pi \tilde{d}(\bm y,\bm\theta)/n) \right). \end{equation} \subsubsection{Nonlinear mixed model summary} \label{sec:nonl-mixed-model} In a nonlinear mixed model we determine the parameter estimate, $\widehat{\bm\theta}_P$, from the Laplace approximation to the log-likelihood as \begin{equation} \label{eq:38} \widehat{\bm\theta}_P = \arg\max_{\bm\theta}\tilde{\ell}_P(\bm\theta|\bm y) =\arg\min_{\bm\theta} \log(|\tilde{\bm L}_{\bm Z}|^2)+ n\left(1+\log(2\pi \tilde{d}(\bm y,\bm\theta)/n) \right). \end{equation} Each evaluation of $\tilde{\ell}_P(\bm\theta|\bm y)$ requires a solving the penalized nonlinear least squares problem (\ref{eq:NLMMpnls}) simultaneously with respect to both sets of coefficients, $\bm u$ and $\bm\beta$, in the linear predictor, $\bm\gamma$. For a weighted nonlinear mixed model with fixed weights, $\bm W$, we replace the unweighted discrepancy function $d(\bm u|\bm y,\bm\theta,\bm\beta)$ with the weighted discrepancy function, %% Finish this off \section{Details of the implementation} \label{sec:details} \subsection{Implementation details for linear mixed models} \label{sec:impl-line-mixed} The crucial step in implementing algorithms for determining ML or REML estimates of the parameters in a linear mixed model is evaluating the factorization (\ref{eq:16}) for any $\bm\theta$ satisfying $\bm\theta_L\le\bm\theta\le\bm\theta_U$. We will assume that $\bm Z$ is sparse as is $\bm Z\bm\Lambda(\bm\theta)$. When $\bm X$ is not sparse we will use the factorization (\ref{eq:16}) setting $\bm P_{\bm X}=\bm I_p$ and storing $\bm L_{\bm X\bm Z}$ and $\bm L_{\bm X}$ as dense matrices. The permutation matrix $\bm P_{\bm Z}$ is determined from the pattern of non-zeros in $\bm Z\bm\Lambda(\bm\theta)$ which is does not depend on $\bm\theta$, as long as $\bm\theta$ is not on the boundary. In fact, in most cases the pattern of non-zeros in $\bm Z\bm\Lambda(\bm\theta)$ is the same as the pattern of non-zeros in $\bm Z$. For many models, in particular models with scalar random effects (described later), the matrix $\bm\Lambda(\bm\theta)$ is diagonal. Given a value of $\bm\theta$ we determine the Cholesky factor $\bm L_{\bm Z}$ satisfying \begin{equation} \label{eq:LZ} \bm L_{\bm Z}\bm L_{\bm Z}\trans=\bm P_{\bm Z}( \bm\Lambda\trans(\bm\theta)\bm Z\trans\bm Z\bm\Lambda(\theta) +\bm I_q)\bm P_{\bm Z}\trans . \end{equation} The CHOLMOD package allows for $\bm L_{\bm Z}$ to be calculated directly from $\bm\Lambda\trans(\bm\theta)\bm Z\trans$ or from $\bm\Lambda\trans(\bm\theta)\bm Z\trans\bm Z\bm\Lambda(\theta)$. The choice in implementation is whether to store $\bm Z\trans$ and update it to $\bm\Lambda\trans(\bm\theta)\bm Z$ or to store $\bm Z\trans\bm Z$ and use it to form $\bm\Lambda\trans(\bm\theta)\bm Z\trans\bm Z\bm\Lambda(\theta)$ at each evaluation. In the \package{lme4} package we store $\bm Z\trans$ and use it to form $\bm\Lambda\trans(\bm\theta)\bm Z\trans$ from which $\bm L_{\bm Z}$ is evaluated. There are two reasons for this choice. First, the calculations for the more general forms of mixed models cannot be reduced to calculations involving $\bm Z\trans\bm Z$ and by expressing these calculations in terms of $\bm\Lambda(\bm\theta)\bm Z\trans$ for linear mixed models we can reuse the code for the more general models. Second, the calculation of $\bm\Lambda(\bm\theta)\trans\left(\bm Z\trans\bm Z\right)\bm\Lambda(\bm\theta)$ from $\bm Z\trans\bm Z$ is complicated compared to the calculation of $\bm\Lambda(\bm\theta)\trans\bm Z\trans$ from $\bm Z\trans$. This choice is disadvantageous when $n\gg q$ because $\bm Z\trans$ is much larger than $\bm Z\trans\bm Z$, even when they are stored as sparse matrices. Evaluation of $\bm L_{\bm Z}$ directly from $\bm Z\trans$ requires more storage and more calculation that evaluating $\bm L_{\bm Z}$ from $\bm Z\trans\bm Z$. Next we evaluate $\bm L_{\bm X\bm Z}\trans$ as the solution to \begin{equation} \label{eq:LXZ} \bm L_{\bm Z}\bm L_{\bm X\bm Z}\trans=\bm P_{\bm Z}\bm\Lambda\trans(\bm\theta)\bm Z\trans\bm X . \end{equation} Again we have the choice of calculating and storing $\bm Z\trans\bm X$ or storing $\bm X$ and using it to reevaluate $\bm Z\trans\bm X$. In the \package{lme4} package we store $\bm X$, because the calculations for the more general models cannot be expressed in terms of $\bm Z\trans\bm X$. Finally $\bm L_{\bm X}$ is evaluated as the (dense) solution to \begin{equation} \label{eq:LX} \bm L_{\bm X}\bm L_{\bm X}\trans= \bm X\trans\bm X-\bm L_{\bm X\bm Z}\bm L_{\bm X\bm Z} . \end{equation} from which $\tilde{\bm\beta}$ can be determined as the solution to dense system \begin{equation} \label{eq:tildebeta} \bm L_{\bm X}\bm L_{\bm X}\tilde{\bm\beta}=\bm X\trans\bm y \end{equation} and $\tilde{\bm u}$ as the solution to the sparse system \begin{equation} \label{eq:tildeu} \bm L_{\bm Z}\bm L_{\bm Z}\tilde{u}=\bm\Lambda\trans\bm Z\trans\bm y \end{equation} For many models, in particular models with scalar random effects, which are described later, the matrix $\bm\Lambda(\bm\theta)$ is diagonal. For such a model, if both $\bm Z$ and $\bm X$ are sparse and we plan to use the REML criterion then we create and store \begin{equation} \label{eq:8} \bm A= \begin{bmatrix} \bm Z\trans\bm Z & \bm Z\trans\bm X\\ \bm X\trans\bm Z & \bm X\trans\bm X \end{bmatrix}\quad\text{and}\quad \bm c =\begin{bmatrix}\bm Z\trans\bm y\\\bm X\trans\bm y\end{bmatrix} \end{equation} and determine a fill-reducing permutation, $\bm P$, for $\bm A$. Given a value of $\bm\theta$ we create the factorization \begin{equation} \label{eq:26} \bm L(\bm\theta)\bm L(\bm\theta)\trans=\bm P\left( \begin{bmatrix} \bm\Lambda(\bm\theta) & \bm0\\\bm0&\bm I_p \end{bmatrix} \bm A \begin{bmatrix} \bm\Lambda(\bm\theta) & \bm0\\\bm0&\bm I_p \end{bmatrix}+ \begin{bmatrix}\bm I_q&\bm0\\\bm0&\bm0\end{bmatrix}\right) \bm P\trans \end{equation} solve for $\tilde{\bm u}(\bm\theta)$ and $\tilde{\bm\beta}(\bm\theta)$ in \begin{equation} \label{eq:28} \bm L\bm L\trans\bm P \begin{bmatrix} \tilde{\bm u}(\bm\theta)\\\tilde{\bm\beta}(\bm\theta) \end{bmatrix}= \bm P \begin{bmatrix}\bm\Lambda(\bm\theta) & \bm0\\\bm0&\bm I_p \end{bmatrix} \bm c \end{equation} then evaluate $\tilde{d}(\bm y|\bm\theta)$ and the profiled REML criterion as \begin{equation} \label{eq:27} \tilde{d}_R(\bm\theta|\bm y)=\log(|\bm L(\bm\theta)|^2)+ (n-p)\left(1+\log\left(\frac{2\pi\tilde{d}(\bm y|\bm\theta)} {n-p}\right)\right) . \end{equation} \bibliography{lme4} \appendix{} \section{Notation} \label{sec:notation} \subsection{Random variables in the model} \label{sec:rand-vari-model} \begin{description} \item[$\bc B$] Random-effects vector of dimension $q$, $\bc{B}\sim\mathcal{N}(\bm 0,\sigma^2\bm V(\bm\theta)\bm V(\bm\theta)\trans)$. \item[$\bm U$] ``Spherical'' random-effects vector of dimension $q$, $\bc U\sim\mathcal{N}(\bm 0,\sigma^2\bm I_q)$, $\bc B=\bm V(\bm\theta)\bc U$. \item[$\bc Y$] Response vector of dimension $n$. \end{description} \subsection{Parameters of the model} \label{sec:parameters-model} \begin{description} \item[$\bm\beta$] Fixed-effects parameters (dimension $p$). \item[$\bm\theta$] Parameters determining the left factor, $\bm\Lambda(\bm\theta)$ of the relative covariance matrix of $\bc B$ (dimension $m$). \item[$\sigma$] the common scale parameter - not used in some generalized linear mixed models and generalized nonlinear mixed models. \end{description} \subsection{Dimensions} \label{sec:dimensions} \begin{description} \item[$m$] dimension of the parameter $\bm\theta$. \item[$n$] dimension of the response vector, $\bm y$, and the random variable, $\bm{\mathcal{Y}}$. \item[$p$] dimension of the fixed-effects parameter, $\bm\beta$. \item[$q$] dimension of the random effects, $\bc B$ or $\bc U$. \item[$s$] dimension of the parameter vector, $\bm\phi$, in the nonlinear model function. \end{description} \subsection{Matrices} \label{sec:matrices} \begin{description} \item[$\bm L$] Left Cholesky factor of a positive-definite symmetric matrix. $\bm L_{\bm Z}$ is $q\times q$; $\bm L_{\bm X}$ is $p\times p$. \item[$\bm P$] Fill-reducing permutation for the random effects model matrix. (Size $q\times q$.) \item[$\bm V$] Left factor of the relative covariance matrix of the random effects. (Size $q\times q$.) \item[$\bm X$] Model matrix for the fixed-effects parameters, $\bm\beta$. (Size $(ns)\times p$.) \item[$\bm Z$] Model matrix for the random effects. (Size $(ns)\times q$.) \end{description} \section{Integrating a quadratic deviance expression} \label{sec:integr-quadr-devi} In (\ref{eq:6}) we defined the likelihood of the parameters given the observed data as \begin{displaymath} L(\bm\theta,\bm\beta,\sigma|\bm y) = \int_{\mathbb{R}^q}h(\bm u|\bm y,\bm\theta,\bm\beta,\sigma)\,d\bm u . \end{displaymath} which is often alarmingly described as ``an intractable integral''. 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{formatcom={\vspace{-1ex}},fontfamily=courier,fontseries=b,% fontsize=\footnotesize} %%\VignetteIndexEntry{PLS vs GLS for LMMs} %%\VignetteDepends{lme4} \title{Penalized least squares versus generalized least squares representations of linear mixed models} \author{Douglas Bates\\Department of Statistics\\% University of Wisconsin -- Madison} \begin{document} \SweaveOpts{engine=R,eps=FALSE,pdf=TRUE,strip.white=true,keep.source=TRUE} \SweaveOpts{include=FALSE} \setkeys{Gin}{width=\textwidth} \newcommand{\code}[1]{\texttt{\small{#1}}} \newcommand{\package}[1]{\textsf{\small{#1}}} \newcommand{\trans}{\ensuremath{^\prime}} <>= options(width=65,digits=5) #library(lme4) @ \maketitle \begin{abstract} The methods in the \code{lme4} package for \code{R} for fitting linear mixed models are based on sparse matrix methods, especially the Cholesky decomposition of sparse positive-semidefinite matrices, in a penalized least squares representation of the conditional model for the response given the random effects. The representation is similar to that in Henderson's mixed-model equations. An alternative representation of the calculations is as a generalized least squares problem. We describe the two representations, show the equivalence of the two representations and explain why we feel that the penalized least squares approach is more versatile and more computationally efficient. \end{abstract} \section{Definition of the model} \label{sec:Definition} We consider linear mixed models in which the random effects are represented by a $q$-dimensional random vector, $\bm{\mathcal{B}}$, and the response is represented by an $n$-dimensional random vector, $\bm{\mathcal{Y}}$. We observe a value, $\bm y$, of the response. The random effects are unobserved. For our purposes, we will assume a ``spherical'' multivariate normal conditional distribution of $\bm{\mathcal{Y}}$, given $\bm{\mathcal{B}}$. That is, we assume the variance-covariance matrix of $\bm{\mathcal{Y}}|\bm{\mathcal{B}}$ is simply $\sigma^2\bm I_n$, where $\bm I_n$ denotes the identity matrix of order $n$. (The term ``spherical'' refers to the fact that contours of the conditional density are concentric spheres.) The conditional mean, $\mathrm{E}[\bm{\mathcal{Y}}|\bm{\mathcal{B}}=\bm b]$, is a linear function of $\bm b$ and the $p$-dimensional fixed-effects parameter, $\bm\beta$, \begin{equation} \label{eq:condmean} \mathrm{E}[\bm{\mathcal{Y}}|\bm{\mathcal{B}}=\bm b]= \bm X\bm\beta+\bm Z\bm b , \end{equation} where $\bm X$ and $\bm Z$ are known model matrices of sizes $n\times p$ and $n\times q$, respectively. Thus \begin{equation} \label{eq:yconditional} \bm{\mathcal{Y}}|\bm{\mathcal{B}}\sim \mathcal{N}\left(\bm X\bm\beta+\bm Z\bm b,\sigma^2\bm I_n\right) . \end{equation} The marginal distribution of the random effects \begin{equation} \label{eq:remargin} \bm{\mathcal{B}}\sim\mathcal{N}\left(\bm 0,\sigma^2\bm\Sigma(\bm\theta)\right) \end{equation} is also multivariate normal, with mean $\bm 0$ and variance-covariance matrix $\sigma^2\bm\Sigma(\bm\theta)$. The scalar, $\sigma^2$, in (\ref{eq:remargin}) is the same as the $\sigma^2$ in (\ref{eq:yconditional}). As described in the next section, the relative variance-covariance matrix, $\bm\Sigma(\bm\theta)$, is a $q\times q$ positive semidefinite matrix depending on a parameter vector, $\bm\theta$. Typically the dimension of $\bm\theta$ is much, much smaller than $q$. \subsection{Variance-covariance of the random effects} \label{sec:revarcov} The relative variance-covariance matrix, $\bm\Sigma(\bm\theta)$, must be symmetric and positive semidefinite (i.e. $\bm x\trans\bm\Sigma\bm x\ge0,\forall\bm x\in\mathbb{R}^q$). Because the estimate of a variance component can be zero, it is important to allow for a semidefinite $\bm\Sigma$. We do not assume that $\bm\Sigma$ is positive definite (i.e. $\bm x\trans\bm\Sigma\bm x>0,\forall\bm x\in\mathbb{R}^q, \bm x\ne\bm 0$) and, hence, we cannot assume that $\bm\Sigma^{-1}$ exists. A positive semidefinite matrix such as $\bm\Sigma$ has a Cholesky decomposition of the so-called ``LDL$\trans$'' form. We use a slight modification of this form, \begin{equation} \label{eq:TSdef} \bm\Sigma(\bm\theta)=\bm T(\bm\theta)\bm S(\bm\theta)\bm S(\bm\theta)\bm T(\bm\theta)\trans , \end{equation} where $\bm T(\bm\theta)$ is a unit lower-triangular $q\times q$ matrix and $\bm S(\bm\theta)$ is a diagonal $q\times q$ matrix with nonnegative diagonal elements that act as scale factors. (They are the relative standard deviations of certain linear combinations of the random effects.) Thus, $\bm T$ is a triangular matrix and $\bm S$ is a scale matrix. Both $\bm T$ and $\bm S$ are highly patterned. \subsection{Orthogonal random effects} \label{sec:orthogonal} Let us define a $q$-dimensional random vector, $\bm{\mathcal{U}}$, of orthogonal random effects with marginal distribution \begin{equation} \label{eq:Udist} \bm{\mathcal{U}}\sim\mathcal{N}\left(\bm 0,\sigma^2\bm I_q\right) \end{equation} and, for a given value of $\bm\theta$, express $\bm{\mathcal{B}}$ as a linear transformation of $\bm{\mathcal{U}}$, \begin{equation} \label{eq:UtoB} \bm{\mathcal{B}}=\bm T(\bm\theta)\bm S(\bm\theta)\bm{\mathcal{U}} . \end{equation} Note that the transformation (\ref{eq:UtoB}) gives the desired distribution of $\bm{\mathcal{B}}$ in that $\mathrm{E}[\bm{\mathcal{B}}]=\bm T\bm S\mathrm{E}[\bm{\mathcal{U}}]=\bm 0$ and \begin{displaymath} \mathrm{Var}(\bm{\mathcal{B}})=\mathrm{E}[\bm{\mathcal{B}}\bm{\mathcal{B}}\trans] =\bm T\bm S\mathrm{E}[\bm{\mathcal{U}}\bm{\mathcal{U}}\trans]\bm S\bm T\trans=\sigma^2\bm T\bm S\bm S\bm T\trans=\bm\Sigma . \end{displaymath} The conditional distribution, $\bm{\mathcal{Y}}|\bm{\mathcal{U}}$, can be derived from $\bm{\mathcal{Y}}|\bm{\mathcal{B}}$ as \begin{equation} \label{eq:YgivenU} \bm{\mathcal{Y}}|\bm{\mathcal{U}}\sim\mathcal{N}\left(\bm X\bm\beta+\bm Z\bm T\bm S\bm u, \sigma^2\bm I\right) \end{equation} We will write the transpose of $\bm Z\bm T\bm S$ as $\bm A$. Because the matrices $\bm T$ and $\bm S$ depend on the parameter $\bm\theta$, $\bm A$ is also a function of $\bm\theta$, \begin{equation} \label{eq:Adef} \bm A\trans(\bm\theta)=\bm Z\bm T(\bm\theta)\bm S(\bm\theta) . \end{equation} In applications, the matrix $\bm Z$ is derived from indicator columns of the levels of one or more factors in the data and is a \emph{sparse} matrix, in the sense that most of its elements are zero. The matrix $\bm A$ is also sparse. In fact, the structure of $\bm T$ and $\bm S$ are such that pattern of nonzeros in $\bm A$ is that same as that in $\bm Z\trans$. \subsection{Sparse matrix methods} \label{sec:sparseMatrix} The reason for defining $\bm A$ as the transpose of a model matrix is because $\bm A$ is stored and manipulated as a sparse matrix. In the compressed column-oriented storage form that we use for sparse matrices, there are advantages to storing $\bm A$ as a matrix of $n$ columns and $q$ rows. In particular, the CHOLMOD sparse matrix library allows us to evaluate the sparse Cholesky factor, $\bm L(\bm\theta)$, a sparse lower triangular matrix that satisfies \begin{equation} \label{eq:SparseChol} \bm L(\bm\theta)\bm L(\bm\theta)\trans= \bm P\left(\bm A(\bm\theta)\bm A(\bm\theta)\trans+\bm I_q\right)\bm P\trans , \end{equation} directly from $\bm A(\bm\theta)$. In (\ref{eq:SparseChol}) the $q\times q$ matrix $\bm P$ is a ``fill-reducing'' permutation matrix determined from the pattern of nonzeros in $\bm Z$. $\bm P$ does not affect the statistical theory (if $\bm{\mathcal{U}}\sim\mathcal{N}(\bm 0,\sigma^2\bm I)$ then $\bm P\trans\bm{\mathcal{U}}$ also has a $\mathcal{N}(\bm 0,\sigma^2\bm I)$ distribution because $\bm P\bm P\trans=\bm P\trans\bm P=\bm I$) but, because it affects the number of nonzeros in $\bm L$, it can have a tremendous impact on the amount storage required for $\bm L$ and the time required to evaluate $\bm L$ from $\bm A$. Indeed, it is precisely because $\bm L(\bm\theta)$ can be evaluated quickly, even for complex models applied the large data sets, that the \code{lmer} function is effective in fitting such models. \section{The penalized least squares approach to linear mixed models} \label{sec:Penalized} Given a value of $\bm\theta$ we form $\bm A(\bm\theta)$ from which we evaluate $\bm L(\bm\theta)$. We can then solve for the $q\times p$ matrix, $\bm R_{\bm{ZX}}$, in the system of equations \begin{equation} \label{eq:RZX} \bm L(\theta)\bm R_{\bm{ZX}}=\bm P\bm A(\bm\theta)\bm X \end{equation} and for the $p\times p$ upper triangular matrix, $\bm R_{\bm X}$, satisfying \begin{equation} \label{eq:RX} \bm R_{\bm X}\trans\bm R_{\bm X}= \bm X\trans\bm X-\bm R_{\bm{ZX}}\trans\bm R_{\bm{ZX}} \end{equation} The conditional mode, $\tilde{\bm u}(\bm\theta)$, of the orthogonal random effects and the conditional mle, $\widehat{\bm\beta}(\bm\theta)$, of the fixed-effects parameters can be determined simultaneously as the solutions to a penalized least squares problem, \begin{equation} \label{eq:PLS} \begin{bmatrix} \tilde{\bm u}(\bm\theta)\\ \widehat{\bm\beta}(\bm\theta) \end{bmatrix}= \arg\min_{\bm u,\bm\beta}\left\| \begin{bmatrix}\bm y\\\bm 0\end{bmatrix} - \begin{bmatrix} \bm A\trans\bm P\trans & \bm X\\ \bm I_q & \bm 0 \end{bmatrix} \begin{bmatrix}\bm u\\\bm\beta\end{bmatrix} , \right\|^2 \end{equation} for which the solution satisfies \begin{equation} \label{eq:PLSsol} \begin{bmatrix} \bm P\left(\bm A\bm A\trans+\bm I\right)\bm P\trans & \bm P\bm A\bm X\\ \bm X\trans\bm A\trans\bm P\trans & \bm X\trans\bm X \end{bmatrix} \begin{bmatrix} \tilde{\bm u}(\bm\theta)\\ \widehat{\bm\beta}(\bm\theta) \end{bmatrix}= \begin{bmatrix}\bm P\bm A\bm y\\\bm X\trans\bm y\end{bmatrix} . \end{equation} The Cholesky factor of the system matrix for the PLS problem can be expressed using $\bm L$, $\bm R_{\bm Z\bm X}$ and $\bm R_{\bm X}$, because \begin{equation} \label{eq:PLSChol} \begin{bmatrix} \bm P\left(\bm A\bm A\trans+\bm I\right)\bm P\trans & \bm P\bm A\bm X\\ \bm X\trans\bm A\trans\bm P\trans & \bm X\trans\bm X \end{bmatrix} = \begin{bmatrix} \bm L & \bm 0\\ \bm R_{\bm Z\bm X}\trans & \bm R_{\bm X}\trans \end{bmatrix} \begin{bmatrix} \bm L\trans & \bm R_{\bm Z\bm X}\\ \bm 0 & \bm R_{\bm X} \end{bmatrix} . \end{equation} In the \code{lme4} package the \code{"mer"} class is the representation of a mixed-effects model. Several slots in this class are matrices corresponding directly to the matrices in the preceding equations. The \code{A} slot contains the sparse matrix $\bm A(\bm\theta)$ and the \code{L} slot contains the sparse Cholesky factor, $\bm L(\bm\theta)$. The \code{RZX} and \code{RX} slots contain $\bm R_{\bm Z\bm X}(\bm\theta)$ and $\bm R_{\bm X}(\bm\theta)$, respectively, stored as dense matrices. It is not necessary to solve for $\tilde{\bm u}(\bm\theta)$ and $\widehat{\bm\beta}(\bm\theta)$ to evaluate the \emph{profiled} log-likelihood, which is the log-likelihood evaluated $\bm\theta$ and the conditional estimates of the other parameters, $\widehat{\bm\beta}(\bm\theta)$ and $\widehat{\sigma^2}(\bm\theta)$. All that is needed for evaluation of the profiled log-likelihood is the (penalized) residual sum of squares, $r^2$, from the penalized least squares problem (\ref{eq:PLS}) and the determinant $|\bm A\bm A\trans+\bm I|=|\bm L|^2$. Because $\bm L$ is triangular, its determinant is easily evaluated as the product of its diagonal elements. Furthermore, $|\bm L|^2 > 0$ because it is equal to $|\bm A\bm A\trans + \bm I|$, which is the determinant of a positive definite matrix. Thus $\log(|\bm L|^2)$ is both well-defined and easily calculated from $\bm L$. The profiled deviance (negative twice the profiled log-likelihood), as a function of $\bm\theta$ only ($\bm\beta$ and $\sigma^2$ at their conditional estimates), is \begin{equation} \label{eq:profiledDev} d(\bm\theta|\bm y)=\log(|\bm L|^2)+n\left(1+\log(r^2)+\frac{2\pi}{n}\right) \end{equation} The maximum likelihood estimates, $\widehat{\bm\theta}$, satisfy \begin{equation} \label{eq:thetamle} \widehat{\bm\theta}=\arg\min_{\bm\theta}d(\bm\theta|\bm y) \end{equation} Once the value of $\widehat{\bm\theta}$ has been determined, the mle of $\bm\beta$ is evaluated from (\ref{eq:PLSsol}) and the mle of $\sigma^2$ as $\widehat{\sigma^2}(\bm\theta)=r^2/n$. Note that nothing has been said about the form of the sparse model matrix, $\bm Z$, other than the fact that it is sparse. In contrast to other methods for linear mixed models, these results apply to models where $\bm Z$ is derived from crossed or partially crossed grouping factors, in addition to models with multiple, nested grouping factors. The system (\ref{eq:PLSsol}) is similar to Henderson's ``mixed-model equations'' (reference?). One important difference between (\ref{eq:PLSsol}) and Henderson's formulation is that Henderson represented his system of equations in terms of $\bm\Sigma^{-1}$ and, in important practical examples, $\bm\Sigma^{-1}$ does not exist at the parameter estimates. Also, Henderson assumed that equations like (\ref{eq:PLSsol}) would need to be solved explicitly and, as we have seen, only the decomposition of the system matrix is needed for evaluation of the profiled log-likelihood. The same is true of the profiled the logarithm of the REML criterion, which we define later. \section{The generalized least squares approach to linear mixed models} \label{sec:GLS} Another common approach to linear mixed models is to derive the marginal variance-covariance matrix of $\bm{\mathcal{Y}}$ as a function of $\bm\theta$ and use that to determine the conditional estimates, $\widehat{\bm\beta}(\bm\theta)$, as the solution of a generalized least squares (GLS) problem. In the notation of \S\ref{sec:Definition} the marginal mean of $\bm{\mathcal{Y}}$ is $\mathrm{E}[\bm{\mathcal{Y}}]=\bm X\bm\beta$ and the marginal variance-covariance matrix is \begin{equation} \label{eq:marginalvarcovY} \mathrm{Var}(\bm{\mathcal{Y}})=\sigma^2\left(\bm I_n+\bm Z\bm T\bm S\bm S\bm T\trans\bm Z\trans\right)=\sigma^2\left(\bm I_n+\bm A\trans\bm A\right) =\sigma^2\bm V(\bm\theta) , \end{equation} where $\bm V(\bm\theta)=\bm I_n+\bm A\trans\bm A$. The conditional estimates of $\bm\beta$ are often written as \begin{equation} \label{eq:condbeta} \widehat{\bm\beta}(\bm\theta)=\left(\bm X\trans\bm V^{-1}\bm X\right)^{-1}\bm X\trans\bm V^{-1}\bm y \end{equation} but, of course, this formula is not suitable for computation. The matrix $\bm V(\bm\theta)$ is a symmetric $n\times n$ positive definite matrix and hence has a Cholesky factor. However, this factor is $n\times n$, not $q\times q$, and $n$ is always larger than $q$ --- sometimes orders of magnitude larger. Blithely writing a formula in terms of $\bm V^{-1}$ when $\bm V$ is $n\times n$, and $n$ can be in the millions does not a computational formula make. \subsection{Relating the GLS approach to the Cholesky factor} \label{sec:GLStoL} We can use the fact that \begin{equation} \label{eq:Vinv} \bm V^{-1}(\bm\theta)=\left(\bm I_n+\bm A\trans\bm A\right)^{-1}= \bm I_n-\bm A\trans\left(\bm I_q+\bm A\bm A\trans\right)^{-1}\bm A \end{equation} to relate the GLS problem to the PLS problem. One way to establish (\ref{eq:Vinv}) is simply to show that the product \begin{multline*} (\bm I+\bm A\trans\bm A)\left(\bm I-\bm A\trans\left(\bm I+\bm A\bm A\trans\right)^{-1}\bm A\right)\\ \begin{aligned} =&\bm I+\bm A\trans\bm A-\bm A\trans\left(\bm I+\bm A\bm A\trans\right) \left(\bm I+\bm A\bm A\trans\right)^{-1}\bm A\\ =&\bm I+\bm A\trans\bm A-\bm A\trans\bm A\\ =&\bm I . \end{aligned} \end{multline*} Incorporating the permutation matrix $\bm P$ we have \begin{equation} \label{eq:PLA} \begin{aligned} \bm V^{-1}(\bm\theta)=&\bm I_n-\bm A\trans\bm P\trans\bm P\left(\bm I_q+\bm A\bm A\trans\right)^{-1}\bm P\trans\bm P\bm A\\ =&\bm I_n-\bm A\trans\bm P\trans(\bm L\bm L\trans)^{-1}\bm P\bm A\\ =&\bm I_n-\left(\bm L^{-1}\bm P\bm A\right)\trans\bm L^{-1}\bm P\bm A . \end{aligned} \end{equation} Even in this form we would not want to routinely evaluate $\bm V^{-1}$. However, (\ref{eq:PLA}) does allow us to simplify many common expressions. For example, the variance-covariance of the estimator $\widehat{\bm \beta}$, conditional on $\bm\theta$ and $\sigma$, can be expressed as \begin{equation} \label{eq:varcovbeta} \begin{aligned} \sigma^2\left(\bm X\trans\bm V^{-1}(\bm\theta)\bm X\right)^{-1} =&\sigma^2\left(\bm X\trans\bm X-\left(\bm L^{-1}\bm P\bm A\bm X\right)\trans\left(\bm L^{-1}\bm P\bm A\bm X\right)\right)^{-1}\\ =&\sigma^2\left(\bm X\trans\bm X-\bm R_{\bm Z\bm X}\trans\bm R_{\bm Z\bm X}\right)^{-1}\\ =&\sigma^2\left(\bm R_{\bm X}\trans\bm R_{\bm X}\right)^{-1} . \end{aligned} \end{equation} \section{Trace of the ``hat'' matrix} \label{sec:hatTrace} Another calculation that is of interest to some is the the trace of the ``hat'' matrix, which can be written as \begin{multline} \label{eq:hatTrace} \tr\left(\begin{bmatrix}\bm A\trans&\bm X\end{bmatrix} \left(\begin{bmatrix}\bm A\trans&\bm X\\\bm I&\bm0\end{bmatrix}\trans \begin{bmatrix}\bm A\trans&\bm X\\\bm I&\bm0\end{bmatrix}\right)^{-1} \begin{bmatrix}\bm A\\\bm X\trans\end{bmatrix}\right)\\ = \tr\left(\begin{bmatrix}\bm A\trans&\bm X\end{bmatrix} \left(\begin{bmatrix}\bm L&\bm0\\ \bm R_{\bm{ZX}}\trans&\bm R_{\bm X}\trans\end{bmatrix} \begin{bmatrix}\bm L\trans&\bm R_{\bm{ZX}}\\ \bm0&\bm R_{\bm X}\end{bmatrix}\right)^{-1} \begin{bmatrix}\bm A\\\bm X\trans\end{bmatrix}\right) \end{multline} \end{document} lme4/vignettes/Theory.pdf0000644000176000001440000112520112156422373015127 0ustar ripleyusers%PDF-1.4 %ÐÔÅØ 3 0 obj << /Length 1964 /Filter /FlateDecode >> stream xÚXKsÛ6¾çWèH͘4|›¶™igzIÝÉ¡í–hY½†”¤ýóÝ'HJT܃eXì.öñí.ß?¼»ÿÌ™¬(\X<<-ln²PÛEaBVçnñ°^ü™ü¸´Éq‚ßø;7ø³ÅµüàÛn™æÆ&ûwžqg™º"YÃS[&y:Âs'dxö+üµkÞÜ#y òvGgþ~øõþƒ3 ë³ÜN5+¬_¤yÈ|U³f?¡,TjCZ°6(¤¿'=‘'ñƒƒ6sÅ"µ%Ü,η'9×!ñWKWÁQ`Tü‰9þ. üÁKôú°D”¾b½?BôŠ\ÛÏm‰¼~!"áqE†dÛ^N˜ê_&ú •]« GÚGéEž¹Ê.RÂCʼnE®–®Lž™OuÇÿ¬ø³– Ê…5YmÀÿhð¢ìí}fBÁì~x­ÎȳYù xht0µÎgE¨A“¹ÜóÑŒ‹Vè'‚œË*p¬a:ô"’Ÿãí„g©´ä9ºÙçe(’fÓÂÅJ“œ:ŠAX3­[ ØýÈÿž0n+ñ)dzPœ9„ññ/c=FBQ'ÍìNËÍ¡AE¿Áß?t/Zí[ö1Úö)$ Ÿ•e£äYð¶ä<àœãÑnqäp`œ(… vi Ä„qîØ$EIȧÕkôB†UŽ3Ê&àtW¹d{ h€tyÑ0:PÑÁ¾·MÇ’'ÙJ 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Bates and Donald G. Watts}, title = {Nonlinear Regression Analysis and Its Applications}, publisher = {Wiley}, year = 1988} @Article{Davis:1996, author = {Tim Davis}, title = {An approximate minimal degree ordering algorithm}, journal = {SIAM J. Matrix Analysis and Applications}, year = 1996, volume = 17, number = 4, pages = {886-905} } @Misc{CSparse, author = {Tim Davis}, title = {{CSparse}: a concise sparse matrix package}, howpublished = {http://www.cise.ufl.edu/research/sparse/CSparse}, year = 2005 } @misc{Cholmod, author = {Tim Davis}, title = {{CHOLMOD}: sparse supernodal {Cholesky} factorization and update/downdate}, howpublished = {http://www.cise.ufl.edu/research/sparse/cholmod}, year = 2005 } @Book{mccullagh89:_gener_linear_model, author = {Peter McCullagh and John Nelder}, title = {Generalized Linear Models}, publisher = {Chapman and Hall}, year = 1989, edition = {2nd}} @Article{mccullough99:_asses_reliab_of_statis_softw, author = {B. D. McCullough}, title = {Assessing the reliability of statistical software: Part II}, journal = {The American Statistician}, year = 1999, volume = 53, number = 2, month = {May}} @Book{davis06:csparse_book, author = {Timothy A. Davis }, title = {Direct Methods for Sparse Linear Systems}, publisher = {SIAM}, year = 2006, series = {Fundamentals of Algorithms} } @Book{pinh:bate:2000, author = {Jos\'{e} C. Pinheiro and Douglas M. Bates}, title = {Mixed-Effects Models in {S} and {S-PLUS}}, year = 2000, pages = {528}, ISBN = {0-387-98957-9}, publisher = {Springer} } @Article{bate:debr:2004, author = {Douglas M. Bates and Saikat DebRoy}, title = {Linear Mixed Models and Penalized Least Squares}, journal = {J. of Multivariate Analysis}, year = 2004, note = {to appear} } @Book{mccullagh:nelder:1989, author = {P. McCullagh and J.A. Nelder}, title = {Generalized Linear Models}, publisher = {Chapman \& Hall}, year = 1989 } @TechReport{Davis:2004, author = {Timothy A. Davis}, title = {Algorithm 8xx: {A} concise sparse {C}holesky factorization package}, institution = {Department of Computer and Information Science and Engineering, University of Florida}, year = 2004 } @Article{tier:kada:1986, journal = JASA, volume = "81", number = "393", pages = "82--86", author = "Luke Tierney and Joseph B. Kadane", title = "Accurate approximations for posterior moments and densities", year = "1986", } @Book{Sing:Will:2003, author = {Judith D. Singer and John B. Willett}, title = {Applied Longitudinal Data Analysis}, publisher = {Oxford University Press}, year = 2003, ISBN = {0-19-515296-4} } @BOOK{R:Chambers+Hastie:1992, author = {John M. Chambers and Trevor J. Hastie}, title = {Statistical Models in {S}}, publisher = {Chapman \& Hall}, year = 1992, address = {London} } lme4/README.md0000644000176000001440000000233412232467515012433 0ustar ripleyuserslme4: Mixed-effects models in R. ==== ## Features * Efficient for large data sets, using algorithms from the [Eigen](http://eigen.tuxfamily.org/index.php?title=Main_Page) linear algebra package via the [RcppEigen](http://cran.r-project.org/web/packages/RcppEigen/index.html) interface layer. * Allows arbitrarily many nested and crossed random effects. * Fits generalized linear mixed models (GLMMs) and nonlinear mixed models (NLMMs) via Laplace approximation or adaptive Gauss-Hermite quadrature; GLMMs allow user-defined families and link functions. * Incorporates likelihood profiling and parametric bootstrapping. ## Installation * From CRAN (note stable version 0.999999-2 will soon be superseded by stable release 1.0.+) * Nearly up-to-date development binaries from `lme4` r-forge repository: ``` install.packages("lme4", repos=c("http://lme4.r-forge.r-project.org/repos", getOption("repos")["CRAN"])) ``` * Development version from github: ``` library("devtools"); install_github("lme4",user="lme4") ``` (The last approach requires that you build from source, i.e. `make` and compilers must be installed on your system -- see the R FAQ for your operating system; you may also need to install dependencies manually.) lme4/MD50000644000176000001440000003066212273504153011464 0ustar ripleyusers2e5b726f280690ac96aa8279ec72c848 *ChangeLog b507d417726f343636dcc2b005b01be7 *DESCRIPTION 627dcf1214645005cc9f1400448976ba *NAMESPACE 87b5eac4f22544cdef41ad1cebd498e8 *R/AllClass.R ce761b8d1b64bee1a1e0a148f80a8997 *R/AllGeneric.R a317d6a549c139a41b858e1d2e4ce224 *R/GHrule.R 3728ee797f912696da14f922fce68adb *R/bootMer.R ba8f8fdce9415b6e71351df549241b5c *R/hooks.r 5d2ed4591c505eee72387657e600d2ca *R/lmList.R d29e8d34e434ef950e8a1c5357307386 *R/lmer.R c063450658f113b8b3afffb27527ecf5 *R/lmerControl.R 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The models and their components are represented using S4 classes and methods. The core computational algorithms are implemented using the Eigen C++ library for numerical linear algebra and RcppEigen "glue". Depends: R (>= 2.14.0), lattice, Matrix (>= 1.1), methods, stats LinkingTo: Rcpp, RcppEigen Imports: graphics, grid, splines, MASS, nlme, minqa (>= 1.1.15), Rcpp Suggests: boot, PKPDmodels, MEMSS, testthat, ggplot2, mlmRev, optimx (>= 2013.8.6), plyr, reshape, RcppEigen (>= 0.3.1.2.3) LazyData: yes License: GPL (>= 2) URL: https://github.com/lme4/lme4/ http://lme4.r-forge.r-project.org/ BuildVignettes: yes Packaged: 2014-02-02 16:13:00 UTC; ripley NeedsCompilation: yes Repository: CRAN Date/Publication: 2014-02-02 19:03:23 lme4/ChangeLog0000644000176000001440000004336712156422372012736 0ustar ripleyusers2012-01-07 Douglas Bates * man/GHrule.Rd, man/NelderMead-class.Rd, man/NelderMead.Rd, man/golden-class.Rd, man/golden.Rd, man/lmer.Rd, man/mkdevfun.Rd: Documentation updates. * R/lmer.R, R/profile.R, tests/lmer-1.R: Nlmer now working (for nAGQ=1). Updated tests and got profile working again. * R/AllClass.R, R/lmer.R, src/external.cpp, src/optimizer.h: Allowed setting options in the NelderMead optimizer. Switched so that this is the default in glmer. * R/AllClass.R, R/lmer.R, src/external.cpp, src/predModule.cpp: More work on nlmer. 2011-12-31 Douglas Bates * src/predModule.cpp: Add an explicit copy of the mapped sparse matrix to a sparse matrix in updateL 2011-12-28 Douglas Bates * R/lmer.R: Change maximum iterations error to a warning. * DESCRIPTION: New version number. * R/lmer.R: Allowed choice of optimizer in glmer at least. * R/AllClass.R: Extended and exported the NelderMead class * src/external.cpp, src/optimizer.cpp, src/optimizer.h: Removed debugging code, added methods to NelderMead to set convergence characteristics and exported same. 2011-12-22 Douglas Bates * NAMESPACE, R/AllClass.R, src/external.cpp, src/optimizer.cpp, src/optimizer.h: Tuned the Nelder-Mead optimizer a bit and got rid of some of the noise. * R/lmer.R, man/lmer.Rd, src/external.cpp: Incorporated aGQ code in the glmer function itself. Eliminated the doFit argument which is redundant when devFunOnly is also used. 2011-12-21 Douglas Bates * src/external.cpp: Added the glmerAGQ function and did some cleanup. * src/predModule.cpp: Delete some debugging code. * R/lmer.R: Updates in glmer, aGQ not yet added but soon will be. * R/GHrule.R: Rename the knots on the z scale as 'z', not 'k' * NAMESPACE: Export the golden class and class generator object 2011-12-16 Douglas Bates * src/respModule.cpp, src/respModule.h: Clean up declarations by declaring a typedef for Eigen::Map 2011-12-15 Douglas Bates * R/lmer.R: Adopt new version of the merPredD$new function that uses S. * R/GHrule.R: Added an R function, based on SparseGrid:::GQN to evaluate a matrix of nodes, weights and log-density for Gauss-Hermite quadrature against a Gaussian density. * R/AllClass.R, src/optimizer.h: Added a reverse-communication golden search algorithm class 2011-12-12 Douglas Bates * src/external.cpp: Fat-finger error. I meant to commit this file and not glmFamily.h * src/glmFamily.h, src/optimizer.cpp, src/optimizer.h: Added simple optimizers (Golden search for bounded 1-dimensional and Nelder-Mead for multidimensional). 2011-12-07 Douglas Bates * R/AllClass.R, R/lmer.R, src/external.cpp, src/predModule.cpp, src/predModule.h: Allow regeneration of pointers in merPredD objects. 2011-12-06 Douglas Bates * R/AllClass.R, R/lmer.R, src/external.cpp, src/predModule.cpp, src/predModule.h: Add many more fields to the merPredD class and allow for serialize/unserialize. Lower the default tolPwrss as per testing by Ben. 2011-12-05 Douglas Bates * DESCRIPTION, R/AllClass.R, R/lmer.R, man/lmer.Rd, man/mkdevfun.Rd, man/refitML.Rd, src/external.cpp, src/predModule.cpp, src/predModule.h, src/respModule.cpp, src/respModule.h: Use mapped objects in C++ structures so that serialize/unserialize will work. 2011-11-29 Douglas Bates * R/profile.R: More fixing of labels. Fix up strip names to reflect sigma, not log(sigma). 2011-11-29 Douglas Bates * R/profile.R: Profiling now works for lmer models with variance components only, including the case of a single fixed-effects parameter. Labeling of some plots needs to be corrected. * src/predModule.cpp, src/predModule.h: Modifications for the 0-column X matrix case used in profiling. * src/Gauss_Hermite.cpp, src/Gauss_Hermite.h, src/external.cpp: Move Gaussian quadrature knot/weight calculations to the Gqr package. 2011-11-28 Douglas Bates * R/profile.R: Got profile working for cases where the number of fixed-effects is greater than 1. Need a "do nothing gracefully" clause for p == 1. * R/lmer.R: Use the value of emptyenv(), not the funtion itself. Add ... to refitML generic and method * R/lmer.R: Use separate mkdevfun in glmer. Switch devFunOnly to an integer value where 1 and 2 refer to different stages. 2011-11-17 Douglas Bates * R/AllClass.R: Correct and extend the allInfo method for the glmResp class. * ToDo: Need to come up with a crafty way of creating a copy of the reference class object with the external pointer. It may work to just copy it then set the PTR to be a NULL pointer. 2011-11-16 Douglas Bates * src/Gauss_Hermite.cpp, src/Gauss_Hermite.h, src/external.cpp: Add another method of creating Gaussian quadrature rules. Export the GHQ class. * tests/lmer-1.R: Comment out enough tests to make R CMD check succeed. * R/lmer.R: Clean up some functions and methods that were causing problems with the tests. * man/glmFamily-class.Rd, man/glmFamily.Rd, man/lmResp-class.Rd, man/lmResp.Rd: Document the class generator object and the reference class (for now). * NAMESPACE: Remove redundant S3 method vcov.summary.mer 2011-11-04 Douglas Bates * src/external.cpp: Correct the argument counts for externally visible glmFamily accessors * man/sleepstudy.Rd: disable a test - need to check what rcond(fm@re) did in lme4a * man/reTrms.Rd: No longer used * man/profile-methods.Rd: Don't run examples until the code has been fixed * man/merPredD-class.Rd, man/merPredD.Rd: Try to document reference class generator object this way * inst/unitTests/runit.compDev.R, inst/unitTests/runit.link.R, inst/unitTests/runit.lmerResp.R: update tests (compDev is inert at present but could be activated for glmer tests) 2011-11-02 Douglas Bates * DESCRIPTION: Bump the version number. * NAMESPACE, man/glmFamily-class.Rd: Export and document the glmFamily reference class and generator object * src/Makevars, src/Makevars.win: Add a Makevars.win file. Make the factory-fresh setting of CPPFLAGS suppress warnings. * man/lmerMod-class.Rd, man/merMod-class.Rd, man/merPredD-class.Rd: incorporate documentation for [gn]lmerMod-class in the merMod-class docs; document merPredD-class * inst/doc/PLSvGLS.Rnw, inst/doc/PLSvGLS.pdf, inst/doc/Theory.Rnw, inst/doc/Theory.pdf, inst/doc/lme4.bib, vignettes, vignettes/PLSvGLS.Rnw, vignettes/PLSvGLS.pdf, vignettes/Theory.Rnw, vignettes/Theory.pdf, vignettes/lme4.bib: Move vignette sources to a vignettes directory per recommendations for R-2.14.0 2011-10-18 Douglas Bates * man/mkdevfun.Rd: Cleanup. * NAMESPACE: Export S3 method for devcomp. * man/lmResp-class.Rd: Document response reference classes and constructor objects * man/chmSp.Rd, man/deFeMod.Rd, man/feModule-class.Rd, man/glmFamily.Rd, man/glmerResp.Rd, man/lmerResp-class.Rd, man/lmerResp.Rd, man/reModule-class.Rd, man/reModule.Rd: Remove documentation of no-longer-used classes from Rcpp modules * man/cbpp.Rd: From R-2.14.0 on getCall is imported from the stats package. * R/lmer.R: Getter and setter for REML field in lmerResp should be reml, not REML. 2011-10-17 Douglas Bates * src/respModule.cpp, src/respModule.h: Change working weights to be a matrix. * src/predModule.cpp: Thinko regarding 0-based indices. 2011-10-14 Douglas Bates * DESCRIPTION, NAMESPACE, R/AllClass.R, R/lmer.R, src/external.cpp, src/predModule.cpp, src/predModule.h, src/respModule.cpp, src/respModule.h: nlmer added. Increment calculation for PWRSS works but not yet incorporated in R code. 2011-10-03 Douglas Bates * R/lmer.R: Small but important change in checking for object weights in environment rho in mkRespMod2 2011-09-30 Douglas Bates * R/AllClass.R, R/lmer.R: Use inheritance on reference classes, being careful about package installation. Create a cleaner version of mkRespMod2. * NAMESPACE: export more classes * DESCRIPTION: new version and date 2011-09-29 Douglas Bates * R/lmer.R: Modify mkRespMod for nlmer response. * src/external.cpp: Add isNullExtPtr .Call'able function for Ptr/ptr formulation. * NAMESPACE, R/AllClass.R: new organization of response classes. Use Ptr/ptr formulation to generate external pointers. 2011-09-27 Douglas Bates * R/lmer.R: More components/slots/etc. available in getME() * R/AllClass.R, R/lmer.R: Reinstitute the Gp slot in merMod objects 2011-09-23 Martin Maechler * R/lmer.R, man/ranef.Rd: *must* use lme4Eigen::: inside devFun() [evaluated from C++] * man/refitML.Rd: add doc. + example * man/Pastes.Rd, man/Penicillin.Rd: reactivate the image(L) plots 2011-09-22 Martin Maechler * NAMESPACE, R/AllClass.R, R/lmer.R: tolPwrss - for now 2011-09-22 Douglas Bates * data/VerbAgg.rda: Correct labels on Gender, use camelCase consistently on item labels. * data/sleepstudy.rda: switch to bzip2 compression for smaller file size. 2011-09-21 Douglas Bates * src/external.cpp: Added a function to check on the SIMD instruction sets in use by Eigen. 2011-09-20 Douglas Bates * R/lmer.R: Do the same number of pwrssUpdate calls with compDev=TRUE or FALSE * src/external.cpp, src/predModule.cpp, src/predModule.h: Remove debugging code. solve and solveU methods for merPredD class now return the numerator of the convergence criterion. * R/lmer.R: Need to pass the pointer, not the reference class object. Modifications to glmer, pwrssUpdate, etc. to get glmer working. 2011-09-20 Martin Maechler * R/lmer.R, man/lmer.Rd, src/external.cpp: non-hardcorded PWRSS tolerance 2011-09-19 Douglas Bates * NAMESPACE, R/AllClass.R, R/lmer.R, src/external.cpp, src/glmFamily.h, src/respModule.cpp: created a glmFamily reference class and allowed for compiled version of glmer update using working residuals and weights (not currently working properly, use compDev=FALSE argument to glmer) * DESCRIPTION: new version and date * src/external.cpp, src/predModule.cpp, src/predModule.h, src/respModule.cpp, src/respModule.h: Moved all externally .Call'able function definitions to external.cpp. external.h and init.cpp are no longer needed. 2011-09-19 Douglas Bates * R/lmer.R: Clean up printMer and summary.merMod * src/external.cpp, src/external.h, src/lmer.cpp, src/lmer.h: rename lmer.[h,cpp] to external.[h,cpp] * R/lmer.R: Initial iterations in glmer (the nAGQ=0L part) now working. Later iterations still need work. 2011-09-16 Douglas Bates * R/lmer.R: Use fac argument in call to sqrL() method. glmer now iterates but does not yet converge. * src/predModule.cpp: Add the - u_i on the rhs of the equation in the solve method. * R/AllClass.R, src/init.cpp, src/predModule.cpp, src/predModule.h: New 'allInfo' method for glmerResp reference class. Added .Call'able extractors for Utr and Vtr to merPredD class. 2011-09-14 Douglas Bates * R/lmer.R: Modified glmer to return a meaningful object when doFit=FALSE. * R/AllClass.R, src/init.cpp, src/respModule.cpp, src/respModule.h: Added methods and new classes for response modules. Modified names of .Call'able functions to make them easier to understand. * src/predModule.cpp: Use d_LamtUt instead of recomputing d_Lambdat * d_Ut * inst/unitTests/runit.link.R: Modified link/muEta/variance unit tests to use a glmerResp object. 2011-09-13 Douglas Bates * src/Makevars: Add more explanation about the -DNDEBUG compiler flag and when to use it. * R/AllClass.R, R/lmer.R, src/init.cpp, src/predModule.cpp, src/predModule.h, src/respModule.cpp, src/respModule.h: Many more methods added to the reference class definitions, as well as documentation. glmer is now working in the sense that it doesn't segfault, however it is not yet performing the calculations correctly. * DESCRIPTION: Remove RcppModule specification. 2011-09-12 Douglas Bates * src/respModule.h: Clean up calculation of working residuals. * R/lmer.R: Still working on glmer, not there yet. * R/AllClass.R, src/init.cpp, src/predModule.cpp, src/predModule.h, src/respModule.cpp, src/respModule.h: Added many more R-callable entry points in respModule and predModule, incorporating them as methods in the reference class definitions. * src/Makevars: Allow for suppression of assert statements with -DNDEBUG (R CMD check complains) 2011-09-12 Martin Maechler * DESCRIPTION, NAMESPACE, R/AllClass.R, R/lmer.R, inst/unitTests/runTests.R, tests/doRunit.R, tests/drop.R, tests/extras.R, tests/lmer-1.Rout.save, tests/lmer.R, tests/lmer2_ex.R, tests/nlmer-conv.R, tests/nlmer.R, tests/throw.R: more updates * man/Dyestuff.Rd, man/VarCorr.Rd, man/cbpp.Rd, man/getME.Rd, man/lmer.Rd, man/lmerMod-class.Rd, man/merMod-class.Rd, man/mkdevfun.Rd, man/ranef.Rd, man/reModule-class.Rd: considerably more documentation 2011-09-11 Douglas Bates * NAMESPACE, R/AllClass.R, R/lmer.R, src/init.cpp, src/respModule.cpp, src/respModule.h: Added glmerResp class and began glmer function. Still needs work. 2011-09-11 Douglas Bates * src/predModule.[h,cpp]: Wrote native C++ L() method. Somehow calling ::M_chm_factor_as_SEXP was messing up memory. * tests/vcov-etc.R, tests/lmer-conv.R: Test using this package not lme4a * R/AllClass.R, src/init.cpp, src/predModule.cpp, src/predModule.h: Add capability of extracting L from a merPredD object 2011-09-10 Martin Maechler * DESCRIPTION, NAMESPACE, R/lmer.R, man/Pastes.Rd, man/Penicillin.Rd, man/bootMer.Rd, man/getL.Rd, man/getME.Rd, man/merMod-class.Rd: getME() for all three(!) versions of lme4 -- deprecate getL() where it exists 2011-09-02 Douglas Bates * src/init.cpp, src/predModule.cpp, src/predModule.h: Drop the check for diagonal Lambda. Cache the value of LamtUt and update it in place with new method updateLamtUt. 2011-08-31 Douglas Bates * src/init.cpp, src/predModule.cpp, src/predModule.h: Finally got a work-around for the "pruning of sparse product" problem. Also added a diagonalLambda method. * R/AllClass.R: Add a "fitted" method to the lmerResp reference class. Re-arrange code. 2011-08-30 Douglas Bates * src/predModule.cpp, src/predModule.h: Drop some debugging code. Isolate the calculation of LamtUt to try to find out what goes wrong in there. * R/lmer.R: Update the ranef method for merPredD storing Lambdat, not Lambda 2011-08-29 Douglas Bates * R/AllGeneric.R, R/utilities.R, src/init.cpp, src/predModule.cpp, src/respModule.cpp, src/respModule.h: Code cleanup - remove exception declarations, use specific 'using' statements within blocks instead of 'using namespace' statements. 2011-08-28 Douglas Bates * NAMESPACE, src/eigen.h, src/glmFamily.cpp, src/glmFamily.h, src/lmer.cpp, src/predModule.cpp, src/predModule.h, src/respModule.cpp, src/respModule.h: Initialize d_delb and d_delu in predModule to zero (not doing so was causing hard-to-debug errors). Remove exception declarations as suggested for Rcpp. Make declaration of short names for Eigen classes and constants namespace-specific. 2011-08-11 Douglas Bates * src/predModule.cpp: Using solveInPlace to try to avoid memory problems. 2011-07-31 Douglas Bates * src/lmer.cpp, src/predModule.cpp, src/predModule.h: Trying to track dowm memory problem in the evaluation of delu. It looks like it is in the CholmodSupport.h file in Eigen but my intended fix apparently didn't succeed. * R/AllClass.R: Modified according to John Chambers' suggestions. 2011-07-29 Douglas Bates * src/respModule.cpp, src/respModule.h: Flailing around trying to find the source of the memory problems. valgrind claims there is an uninitialized value being used in a log call within the Laplace method but I can't find it. * src/predModule.h: Remove unneeded template keywords (caught by an old compiler). * R/AllClass.R: Realized that the initialize method should return an object. * R/AllClass.R, R/lmer.R, src/init.cpp, src/predModule.cpp, src/predModule.h: Switch to Lambdat and Zt and the CHOLMOD factorization. Special code for diagonal Lambda. * src/predModule.cpp: One more attempt. Still failing on the boundary when the number of nonzeros changes. 2011-07-28 Douglas Bates * src/init.cpp, src/predModule.cpp, src/predModule.h: First shot at a version with the Eigen/Matrix/CHOLMOD links. Compiles and loads but not yet tested. Checking in so I can access it from home. * src/init.cpp, src/lmer.cpp, src/predModule.cpp, src/predModule.h: More getter methods defined in C++ code and reference class. Remove some debugging code. * R/AllClass.R, R/AllGeneric.R, R/lmList.R, R/lmer.R: More changes to use S3 methods when dispatching on the first argument only. * DESCRIPTION, NAMESPACE: Change dependencies and imports. 2011-07-26 Douglas Bates * * initialize this archive. lme4/man/0000755000176000001440000000000012273467214011726 5ustar ripleyuserslme4/man/fortify.Rd0000644000176000001440000000122712204271665013676 0ustar ripleyusers\name{fortify} \alias{fortify} \alias{fortify.lmerMod} \title{add information to data based on a fitted model} \usage{ fortify(model, data, ...) \method{fortify}{lmerMod} (model, data = getData(model), ...) } \arguments{ \item{model}{fitted model} \item{data}{original data set, if needed} \item{\dots}{additional arguments} } \description{ add information to data based on a fitted model } \details{ \code{fortify} is a function defined in the \code{ggplot2} package, q.v. for more details; the S3 generic is just defined here to avoid inducing an additional \code{Imports:} dependency. This is currently an experimental feature. } lme4/man/densityplot.thpr.Rd0000644000176000001440000000100112156422373015534 0ustar ripleyusers\name{densityplot.thpr} \alias{densityplot.thpr} \title{densityplot from a mixed-effects profile} \usage{ \method{densityplot}{thpr} (x, data, ...) } \arguments{ \item{x}{a mixed-effects profile} \item{data}{not used - for compatibility with generic} \item{...}{optional arguments to \code{\link[lattice]{densityplot}()} from package \pkg{lattice}.} } \value{ a density plot } \description{ Densityplot method for a mixed-effects model profile } \examples{ ## see example("profile.merMod") } lme4/man/getME.Rd0000644000176000001440000001215012273465601013213 0ustar ripleyusers\name{getME} \alias{getL} \alias{getL,merMod-method} \alias{getME} \title{Extract or Get Generalized Components from a Fitted Mixed Effects Model} \usage{ getME(object, name = c("X", "Z", "Zt", "Ztlist", "y", "mu", "u", "b", "Gp", "Tp", "L", "Lambda", "Lambdat", "Lind", "A", "RX", "RZX", "sigma", "flist", "beta", "theta", "ST", "REML", "is_REML", "n_rtrms", "n_rfacs", "cnms", "devcomp", "offset", "lower")) } \arguments{ \item{object}{a fitted mixed-effects model of class \code{"\linkS4class{merMod}"}, i.e., typically the result of \code{\link{lmer}()}, \code{\link{glmer}()} or \code{\link{nlmer}()}.} \item{name}{a character vector specifying the name(s) of the \dQuote{component}. If \code{length(name)}>1, a named list of components will be returned. Possible values are:\cr \describe{ \item{X}{fixed-effects model matrix} \item{Z}{random-effects model matrix} \item{Zt}{transpose of random-effects model matrix. Note that the structure of \code{Zt} has changed since \code{lme4.0}; to get a backward-compatible structure, use \code{do.call(Matrix::rBind,getME(.,"Ztlist"))}} \item{Ztlist}{list of components of the transpose of the random-effects model matrix, separated by individual variance component} \item{y}{response vector} \item{mu}{conditional mean of the response} \item{u}{conditional mode of the \dQuote{spherical} random effects variable} \item{b}{conditional mode of the random effects variable} \item{Gp}{groups pointer vector. A pointer to the beginning of each group of random effects corresponding to the random-effects terms, beginning with 0 and including a final element giving the total number of random effects} \item{Tp}{theta pointer vector. A pointer to the beginning of the theta sub-vectors corresponding to the random-effects terms, beginning with 0 and including a final element giving the number of thetas.} \item{L}{sparse Cholesky factor of the penalized random-effects model.} \item{Lambda}{relative covariance factor \eqn{\Lambda}{Lambda} of the random effects.} \item{Lambdat}{transpose \eqn{\Lambda'}{Lambda'} of \eqn{\Lambda}{Lambda} above.} \item{Lind}{index vector for inserting elements of \eqn{\theta}{theta} into the nonzeros of \eqn{\Lambda}{Lambda}.} \item{A}{Scaled sparse model matrix (class \code{"\link[Matrix:dgCMatrix-class]{dgCMatrix}"}) for the unit, orthogonal random effects, \eqn{U}, equal to \code{getME(.,"Zt") \%*\% getME(.,"Lambdat")}} \item{RX}{Cholesky factor for the fixed-effects parameters} \item{RZX}{cross-term in the full Cholesky factor} \item{sigma}{residual standard error; note that \code{sigma(object)} is preferred.} \item{flist}{a list of the grouping variables (factors) involved in the random effect terms} \item{beta}{fixed-effects parameter estimates (identical to the result of \code{\link{fixef}}, but without names)} \item{theta}{random-effects parameter estimates: these are parameterized as the relative Cholesky factors of each random effect term} \item{ST}{a list of matrices giving the relative Cholesky factors for each random effect term} \item{n_rtrms}{number of random-effects terms} \item{n_rfacs}{number of distinct random-effects grouping factors} \item{cnms}{the \dQuote{component names}, a \code{\link{list}}.} \item{REML}{restricted maximum likelihood} \item{is_REML}{same as the result of \code{\link{isREML}(.)}} \item{devcomp}{a list consisting of a named numeric vector, \dQuote{cmp}, and a named integer vector, \dQuote{dims}, describing the fitted model} \item{offset}{model offset} \item{lower}{lower bounds on model parameters (random effects parameters only).} } } } \value{ Unspecified, as very much depending on the \code{\link{name}}. } \description{ Extract (or \dQuote{get}) \dQuote{components} -- in a generalized sense -- from a fitted mixed-effects model, i.e., (in this version of the package) from an object of class \code{"\linkS4class{merMod}"}. } \details{ The goal is to provide \dQuote{everything a user may want} from a fitted \code{"merMod"} object \emph{as far} as it is not available by methods, such as \code{\link{fixef}}, \code{\link{ranef}}, \code{\link{vcov}}, etc. } \seealso{ \code{\link{getCall}()}. More standard methods for \code{"merMod"} objects, such as \code{\link{ranef}}, \code{\link{fixef}}, \code{\link{vcov}}, etc.: see \code{methods(class="merMod")} } \examples{ ## shows many methods you should consider *before* using getME(): methods(class = "merMod") (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) Z <- getME(fm1, "Z") stopifnot(is(Z, "CsparseMatrix"), c(180,36) == dim(Z), all.equal(fixef(fm1), getME(fm1, "beta"), check.attributes = FALSE, tol = 0)) ## A way to get *all* getME()s : getME.all <- function(obj) { sapply(eval(formals(getME)$name), getME, object=fm1, simplify=FALSE) } ## internal consistency check ensuring that all work: parts <- getME.all(fm1) str(parts, max=2) } \keyword{utilities} lme4/man/VarCorr.Rd0000644000176000001440000000314312232467515013573 0ustar ripleyusers\name{VarCorr} \title{Extract Variance and Correlation Components} \alias{VarCorr} \alias{VarCorr.merMod} \usage{ \method{VarCorr}{merMod} (x, sigma, rdig) } \arguments{ \item{x}{a fitted model object, usually an object inheriting from class \code{\linkS4class{merMod}}.} \item{sigma}{an optional numeric value used as a multiplier for the standard deviations. Default is \code{1}.} \item{rdig}{an optional integer value specifying the number of digits used to represent correlation estimates. Default is \code{3}.} } \description{ This function calculates the estimated variances, standard deviations, and correlations between the random-effects terms in a mixed-effects model, of class \code{\linkS4class{merMod}} (linear, generalized or nonlinear). The within-group error variance and standard deviation are also calculated. } \value{ a list of matrices, one for each random effects grouping term. For each grouping term, the standard deviations and correlation matrices for each grouping term are stored as attributes \code{"stddev"} and \code{"correlation"}, respectively, of the variance-covariance matrix, and the residual standard deviation is stored as attribute \code{"sc"} (for \code{glmer} fits, this attribute stores the scale parameter of the model). } \author{ This is modeled after \code{\link[nlme]{VarCorr}} from package \pkg{nlme}, by Jose Pinheiro and Douglas Bates. } \seealso{ \code{\link{lmer}}, \code{\link{nlmer}} } \examples{ data(Orthodont, package="nlme") fm1 <- lmer(distance ~ age + (age|Subject), data = Orthodont) VarCorr(fm1) } \keyword{models} lme4/man/grouseticks.Rd0000644000176000001440000000412612156422373014557 0ustar ripleyusers\name{grouseticks} \alias{grouseticks} \alias{grouseticks_agg} \docType{data} \title{ Data on red grouse ticks from Elston et al. 2001 } \description{ Number of ticks on the heads of red grouse chicks sampled in the field (\code{grouseticks}) and an aggregated version (\code{grouseticks_agg}); see original source for more details } \usage{data(grouseticks)} \format{ \describe{ \item{\code{INDEX}}{(factor) chick number (observation level)} \item{\code{TICKS}}{number of ticks sampled} \item{\code{BROOD}}{(factor) brood number} \item{\code{HEIGHT}}{height above sea level (meters)} \item{\code{YEAR}}{year (-1900)} \item{\code{LOCATION}}{(factor) geographic location code} \item{\code{cHEIGHT}}{centered height, derived from \code{HEIGHT}} \item{\code{meanTICKS}}{mean number of ticks by brood} \item{\code{varTICKS}}{variance of number of ticks by brood} } } \source{ Robert Moss, via David Elston } \details{\code{grouseticks_agg} is just a brood-level aggregation of the data} \references{ Elston, D. A., R. Moss, T. Boulinier, C. Arrowsmith, and X. Lambin. 2001. "Analysis of Aggregation, a Worked Example: Numbers of Ticks on Red Grouse Chicks." Parasitology 122 (05): 563-569. doi:10.1017/S0031182001007740. \url{http://journals.cambridge.org/action/displayAbstract?fromPage=online&aid=82701}. } \examples{ data(grouseticks) ## Figure 1a from Elston et al par(las=1,bty="l") tvec <- c(0,1,2,5,20,40,80) pvec <- c(4,1,3) with(grouseticks_agg,plot(1+meanTICKS~HEIGHT, pch=pvec[factor(YEAR)], log="y",axes=FALSE, xlab="Altitude (m)", ylab="Brood mean ticks")) axis(side=1) axis(side=2,at=tvec+1,label=tvec) box() abline(v=405,lty=2) ## Figure 1b with(grouseticks_agg,plot(varTICKS~meanTICKS, pch=4, xlab="Brood mean ticks", ylab="Within-brood variance")) curve(1*x,from=0,to=70,add=TRUE) ## Model fitting form <- TICKS~YEAR+HEIGHT+(1|BROOD)+(1|INDEX)+(1|LOCATION) (full_mod1 <- glmer(form, family="poisson",data=grouseticks)) } \keyword{datasets} lme4/man/isNested.Rd0000644000176000001440000000106412156422373013771 0ustar ripleyusers\name{isNested} \alias{isNested} \title{Is f1 nested within f2?} \usage{ isNested(f1, f2) } \arguments{ \item{f1}{factor 1} \item{f2}{factor 2} } \value{ TRUE if factor 1 is nested within factor 2 } \description{ Does every level of f1 occur in conjunction with exactly one level of f2? The function is based on converting a triplet sparse matrix to a compressed column-oriented form in which the nesting can be quickly evaluated. } \examples{ with(Pastes, isNested(cask, batch)) ## => FALSE with(Pastes, isNested(sample, batch)) ## => TRUE } lme4/man/merPredD.Rd0000644000176000001440000000244112204271665013715 0ustar ripleyusers\name{merPredD} \alias{merPredD} \title{Generator object for the \code{\linkS4class{merPredD}} class} \usage{ merPredD(...) } \arguments{ \item{...}{List of arguments (see Note).} } \description{ The generator object for the \code{\linkS4class{merPredD}} reference class. Such an object is primarily used through its \code{new} method. } \note{ Arguments to the \code{new} methods must be named arguments: \itemize{ \item{X}{ dense model matrix for the fixed-effects parameters, to be stored in the \code{X} field.} \item{Zt}{ transpose of the sparse model matrix for the random effects. It is stored in the \code{Zt} field.} \item{Lambdat}{ transpose of the sparse lower triangular relative variance factor (stored in the \code{Lambdat} field).} \item{Lind}{ integer vector of the same length as the \code{"x"} slot in the \code{Lambdat} field. Its elements should be in the range 1 to the length of the \code{theta} field.} \item{theta}{ numeric vector of variance component parameters (stored in the \code{theta} field).} \item{n}{ sample size, usually \code{nrow(X)}.} } } \section{Methods}{ \describe{ \item{new(X, Zt, Lambdat, Lind, theta, n):}{Create a new \code{\linkS4class{merPredD}} object} } } \seealso{ \code{\linkS4class{merPredD}} } \keyword{classes} lme4/man/confint.merMod.Rd0000644000176000001440000000516612232467515015106 0ustar ripleyusers\name{confint.merMod} \alias{confint.merMod} \title{Compute confidence intervals on the parameters of an lme4 fit} \usage{ \method{confint}{merMod}(object, parm, level = 0.95, method = c("profile", "Wald", "boot"), zeta, nsim = 500, boot.type = "perc", quiet = FALSE, oldNames = TRUE, ...) } \arguments{ \item{object}{a fitted [ng]lmer model} \item{parm}{parameters (specified by integer position)} \item{level}{confidence level \eqn{< 1}, typically above 0.90.} \item{method}{a \code{\link{character}} string determining the method for computing the confidence intervals.} \item{zeta}{(for \code{method = "profile"} only:) likelihood cutoff (if not specified, as by default, computed from \code{level}).} \item{nsim}{number of simulations for parametric bootstrap intervals.} \item{boot.type}{bootstrap confidence interval type.} \item{quiet}{(logical) suppress messages about computationally intensive profiling?} \item{oldNames}{(logical) use old-style names for \code{method="profile"}? (See \code{signames} argument to \code{\link{profile}}).} \item{\dots}{additional parameters to be passed to \code{\link{profile.merMod}} or \code{\link{bootMer}}, respectively.} } \value{ a numeric table of confidence intervals } \description{ Compute confidence intervals on the parameters of a \code{*lmer()} model fit (of class\code{"\linkS4class{merMod}"}). } \details{ Depending on the \code{method} specified, \code{confint()} computes confidence intervals by \describe{ \item{\code{"profile"}:}{computing a likelihood profile and finding the appropriate cutoffs based on the likelihood ratio test;} \item{\code{"Wald"}:}{approximate the confidence intervals (of fixed-effect parameters only) based on the estimated local curvature of the likelihood surface;} \item{\code{"boot"}:}{perform parametric bootstrapping with confidence intervals computed from the bootstrap distribution according to \code{boot.type} (see \code{\link{boot.ci}}).} } } \examples{ fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) fm1W <- confint(fm1, method="Wald")# very fast, but .... fm1W testLevel <- if (nzchar(s <- Sys.getenv("LME4_TEST_LEVEL"))) as.numeric(s) else 1 if(interactive() || testLevel >= 3) { ## ~20 seconds, MacBook Pro laptop system.time(fm1P <- confint(fm1, method="profile", ## default oldNames = FALSE)) ## ~ 40 seconds system.time(fm1B <- confint(fm1,method="boot", .progress="txt", PBargs=list(style=3))) } else load(system.file("testdata","confint_ex.rda",package="lme4")) fm1P fm1B } lme4/man/glmer.nb.Rd0000644000176000001440000000073212156422373013720 0ustar ripleyusers\name{glmer.nb} \alias{glmer.nb} \title{glmer() for Negative Binomial} \usage{ glmer.nb(..., interval = log(th) + c(-3, 3), verbose = FALSE) } \arguments{ \item{...}{formula, data, etc: the arguments for \code{\link{glmer}(..)} (apart from \code{family}!).} \item{interval}{interval in which to start the optimization} \item{verbose}{logical indicating how much progress information should be printed.} } \description{ glmer() for Negative Binomial } lme4/man/predict.merMod.Rd0000644000176000001440000000420212204271301015047 0ustar ripleyusers\name{predict.merMod} \alias{predict.merMod} \title{Predictions from a model at new data values} \usage{ \method{predict}{merMod} (object, newdata = NULL, REform = NULL, terms = NULL, type = c("link", "response"), allow.new.levels = FALSE, na.action = na.pass, ...) } \arguments{ \item{object}{a fitted model object} \item{newdata}{data frame for which to evaluate predictions} \item{REform}{formula for random effects to include. If NULL, include all random effects; if NA, include no random effects} \item{terms}{a \code{\link{terms}} object - not used at present} \item{type}{character string - either \code{"link"}, the default, or \code{"response"} indicating the type of prediction object returned} \item{allow.new.levels}{(logical) if FALSE (default), then any new levels (or NA values) detected in \code{newdata} will trigger an error; if TRUE, then the prediction will use the unconditional (population-level) values for data with previously unobserved levels (or NAs)} \item{na.action}{function determining what should be done with missing values for fixed effects in \code{newdata}. The default is to predict \code{NA}: see \code{\link{na.pass}}.} \item{...}{optional additional parameters. None are used at present.} } \value{ a numeric vector of predicted values } \description{ \code{\link{predict}} method for \code{\linkS4class{merMod}} objects } \note{ There is no option for computing standard errors of predictions because it is difficult to define an efficient method that incorporates uncertainty in the variance parameters; we recommend \code{\link{bootMer}} for this task. } \examples{ (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 |herd), cbpp, binomial)) str(p0 <- predict(gm1)) # fitted values str(p1 <- predict(gm1,REform=NA)) # fitted values, unconditional (level-0) newdata <- with(cbpp, expand.grid(period=unique(period), herd=unique(herd))) str(p2 <- predict(gm1,newdata)) # new data, all RE str(p3 <- predict(gm1,newdata,REform=NA)) # new data, level-0 str(p4 <- predict(gm1,newdata,REform=~(1|herd))) # explicitly specify RE } lme4/man/NelderMead-class.Rd0000644000176000001440000000134712156422373015322 0ustar ripleyusers\docType{class} \name{NelderMead-class} \alias{NelderMead-class} \title{Class \code{"NelderMead"}} \description{ A reference class for a Nelder-Mead simplex optimizer allowing box constraints on the parameters and using reverse communication. } \note{ This is the default optimizer for the second stage of \code{\link{glmer}} and \code{\link{nlmer}} fits. We found that it was more reliable and often faster than more sophisticated optimizers. } \section{Extends}{ All reference classes extend and inherit methods from \code{"\linkS4class{envRefClass}"}. } \examples{ showClass("NelderMead") } \references{ Based on code in the NLopt collection. } \seealso{ \code{\link{glmer}}, \code{\link{nlmer}} } \keyword{classes} lme4/man/simulate.merMod.Rd0000644000176000001440000000227712232467515015271 0ustar ripleyusers\name{simulate.merMod} \alias{simulate.merMod} \title{Simulate responses from a \code{\linkS4class{merMod}} object} \usage{ \method{simulate}{merMod} (object, nsim = 1, seed = NULL, use.u = FALSE, ...) } \arguments{ \item{object}{a fitted model object} \item{nsim}{positive integer scalar - the number of responses to simulate} \item{seed}{an optional seed to be used in \code{set.seed} immediately before the simulation so as to generate a reproducible sample.} \item{use.u}{(logical) if \code{TRUE}, generate a simulation conditional on the current random-effects estimates; if \code{FALSE} generate new Normally distributed random-effects values} \item{...}{optional additional arguments, none are used at present} } \description{ Simulate responses from the model represented by a fitted model object } \examples{ ## test whether fitted models are consistent with the ## observed number of zeros in CBPP data set: gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) gg <- simulate(gm1,1000) zeros <- sapply(gg,function(x) sum(x[,"incidence"]==0)) plot(table(zeros)) abline(v=sum(cbpp$incidence==0),col=2) } lme4/man/lmResp.Rd0000644000176000001440000000263012156422373013455 0ustar ripleyusers\name{lmResp} \alias{glmResp} \alias{lmerResp} \alias{lmResp} \alias{nlsResp} \title{Generator objects for the response classes} \usage{ lmResp(...) } \arguments{ \item{...}{List of arguments (see Note).} } \description{ The generator objects for the \code{\linkS4class{lmResp}}, \code{\linkS4class{lmerResp}}, \code{\linkS4class{glmResp}} and \code{\linkS4class{nlsResp}} reference classes. Such objects are primarily used through their \code{new} methods. } \note{ Arguments to the \code{new} methods must be named arguments. \itemize{ \item{y}{ the numeric response vector} \item{family}{ a \code{\link{family}} object} \item{nlmod}{ the nonlinear model function} \item{nlenv}{ an environment holding data objects for evaluation of \code{nlmod}} \item{pnames}{ a character vector of parameter names} \item{gam}{ a numeric vector - the initial linear predictor} } } \section{Methods}{ \describe{ \item{\code{new(y=y)}:}{Create a new \code{\linkS4class{lmResp}} or \code{\linkS4class{lmerResp}} object.} \item{\code{new(family=family, y=y)}:}{Create a new \code{\linkS4class{glmResp}} object.} \item{\code{new(y=y, nlmod=nlmod, nlenv=nlenv, pnames=pnames, gam=gam)}:}{Create a new \code{\linkS4class{nlsResp}} object.} } } \seealso{ \code{\linkS4class{lmResp}}, \code{\linkS4class{lmerResp}}, \code{\linkS4class{glmResp}}, \code{\linkS4class{nlsResp}} } \keyword{classes} lme4/man/lmList.Rd0000644000176000001440000000517112172000024013441 0ustar ripleyusers\name{lmList} \alias{lmList} \title{List of lm Objects with a Common Model} \usage{ lmList(formula, data, family, subset, weights, na.action, offset, pool, ...) } \arguments{ \item{formula}{a linear formula object of the form \code{y ~ x1+...+xn | g}. In the formula object, \code{y} represents the response, \code{x1,...,xn} the covariates, and \code{g} the grouping factor specifying the partitioning of the data according to which different \code{lm} fits should be performed.} \item{family}{an optional family specification for a generalized linear model.} \item{pool}{logical scalar, should the variance estimate pool the residual sums of squares} \item{...}{additional, optional arguments to be passed to the model function or family evaluation.} \item{data}{an optional data frame containing the variables named in \code{formula}. By default the variables are taken from the environment from which \code{lmer} is called. While \code{data} is optional, the package authors \emph{strongly} recommend its use, especially when later applying methods such as \code{update} and \code{drop1} to the fitted model (\emph{such methods are not guaranteed to work properly if \code{data} is omitted}). If \code{data} is omitted, variables will be taken from the environment of \code{formula} (if specified as a formula) or from the parent frame (if specified as a character vector).} \item{subset}{an optional expression indicating the subset of the rows of \code{data} that should be used in the fit. This can be a logical vector, or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default.} \item{weights}{an optional vector of \sQuote{prior weights} to be used in the fitting process. Should be \code{NULL} or a numeric vector.} \item{na.action}{a function that indicates what should happen when the data contain \code{NA}s. The default action (\code{na.omit}, inherited from the 'factory fresh' value of \code{getOption("na.action")}) strips any observations with any missing values in any variables.} \item{offset}{this can be used to specify an \emph{a priori} known component to be included in the linear predictor during fitting. This should be \code{NULL} or a numeric vector of length equal to the number of cases. One or more \code{\link{offset}} terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See \code{\link{model.offset}}.} } \description{ List of lm Objects with a Common Model } lme4/man/GQN.Rd0000644000176000001440000000163512156422373012644 0ustar ripleyusers\docType{data} \name{GQN} \alias{GQN} \title{Sparse Gauss-Hermite quadrature grids} \format{A list of lists.} \description{ \code{GQN} contains the non-redundant quadrature nodes and weights for integration of a scalar function of a \code{d}-dimensional argument with respect to the density function of the \code{d}-dimensional Gaussian density function. These are stored in a list of lists. The outer list is indexed by the dimension, \code{d}, in the range of 1 to 20. The inner list is indexed by \code{k}, the order of the quadrature. } \note{ These are only the non-redundant nodes. To regenerate the whole array of nodes, all possible permutations of axes and all possible combinations of \eqn{\pm 1}{+/- 1} must be applied to the axes. The function \code{\link{GQdk}} reproduces the entire array of nodes. } \examples{ GQN[[3]][[5]] } \seealso{ \code{\link{GQdk}} } \keyword{datasets} lme4/man/subbars.Rd0000644000176000001440000000164612156422373013662 0ustar ripleyusers\name{subbars} \alias{subbars} \title{"Sub[stitute] Bars"} \usage{ subbars(term) } \arguments{ \item{term}{a mixed-model formula} } \value{ the formula with all | operators replaced by + } \description{ Substitute the '+' function for the '|' function in a mixed-model formula. This provides a formula suitable for the current model.frame function. } \section{Note}{ This function is called recursively on individual terms in the model, which is why the argument is called \code{term} and not a name like \code{form}, indicating a formula. } \examples{ subbars(Reaction ~ Days + (Days|Subject)) ## => Reaction ~ Days + (Days + Subject) } \seealso{ \code{\link{formula}}, \code{\link{model.frame}}, \code{\link{model.matrix}}. Other utilities: \code{\link{findbars}}, \code{\link{mkRespMod}}, \code{\link{mkReTrms}}, \code{\link{nlformula}}, \code{\link{nobars}} } \keyword{models} \keyword{utilities} lme4/man/NelderMead.Rd0000644000176000001440000000634612204271665014223 0ustar ripleyusers\name{NelderMead} \alias{Nelder_Mead} \alias{NelderMead} \title{Generator object for the Nelder-Mead optimizer class.} \usage{ NelderMead(...) Nelder_Mead(fn, par, lower = rep.int(-Inf, n), upper = rep.int(Inf, n), control = list()) } \arguments{ \item{\dots}{Argument list (see Note below).} \item{fn}{a function of a single numeric vector argument returning a numeric scalar} \item{par}{numeric vector of starting values for the parameters.} \item{lower}{numeric vector of lower bounds (elements may be \code{-Inf}).} \item{upper}{numeric vector of upper bounds (elements may be \code{Inf}).} \item{control}{a named list of control settings. Possible settings are \describe{ \item{iprint}{numeric scalar - frequency of printing evaluation information. Defaults to 0 indicating no printing.} \item{maxfun}{numeric scalar - maximum number of function evaluations allowed (default:10000).} \item{FtolAbs}{numeric scalar - absolute tolerance on change in function values (default: 1e-5)} \item{FtolRel}{numeric scalar - relative tolerance on change in function values (default:1e-15)} \item{XtolRel}{numeric scalar - relative tolerance on change in parameter values (default: 1e-7)} \item{MinfMax}{numeric scalar - maximum value of the minimum (default: .Machine$double.xmin)} \item{xst}{numeric vector of initial step sizes to establish the simplex - all elements must be non-zero (default: rep(0.02,length(par)))} \item{xt}{numeric vector of tolerances on the parameters (default: xst*5e-4)} \item{verbose}{numeric value: 0=no printing, 1=print every 20 evaluations, 2=print every 10 evalutions, 3=print every evaluation. Sets \sQuote{iprint}, if specified, but does not override it.} }} } \value{ a list with 4 components \item{fval}{numeric scalar - the minimum function value achieved} \item{par}{numeric vector - the value of \code{x} providing the minimum} \item{ierr}{integer scalar - error code (see below)} \item{control}{list - the list of control settings after substituting for defaults} } \description{ The generator objects for the \code{\linkS4class{NelderMead}} class of optimizers subject to box constraints and using reverse communications. Nelder-Mead optimization of parameters, possibly with box constraints } \note{ Arguments to the \code{new} method must be named arguments: \describe{ \item{lower}{numeric vector of lower bounds - elements may be \code{-Inf}.} \item{upper}{numeric vector of upper bounds - elements may be \code{Inf}.} \item{xst}{numeric vector of initial step sizes to establish the simplex - all elements must be non-zero.} \item{x0}{numeric vector of starting values for the parameters.} \item{xt}{numeric vector of tolerances on the parameters.} } Return error codes (\code{ierr}): \describe{ \item{-4}{\code{nm_evals}: maximum evaluations reached} \item{-3}{\code{nm_forced}: ?} \item{-2}{\code{nm_nofeasible}: cannot generate a feasible simplex} \item{-1}{\code{nm_x0notfeasible}: initial x is not feasible (?)} } } \section{Methods}{ \describe{\code{NelderMead$new(lower, upper, xst, x0, xt)}}{Create a new \code{\linkS4class{NelderMead}} object} } \seealso{ \code{\linkS4class{NelderMead}} } \keyword{classes} lme4/man/profile-methods.Rd0000644000176000001440000000746112204271665015323 0ustar ripleyusers\docType{methods} \name{profile-methods} \alias{as.data.frame.thpr} \alias{profile-methods} \alias{profile.merMod} \title{Profile method for merMod objects} \usage{ \method{profile}{merMod}(fitted, which = 1:nptot, alphamax = 0.01, maxpts = 100, delta = cutoff/8, verbose = 0, devtol = 1e-09, maxmult = 10, startmethod = "prev", optimizer = "bobyqa", signames = TRUE, ...) \method{as.data.frame}{thpr} (x, ...) } \arguments{ \item{fitted}{a fitted model, e.g., the result of \code{\link{lmer}(..)}.} \item{which}{integer indicating which parameters to profile: default is all parameters. The parameters are ordered as follows: \describe{ \item{(1)}{random effects (theta) parameters;} \item{(2)}{residual standard deviation (or scale parameter for GLMMs where appropriate);} \item{(3)}{fixed effect parameters. Random effects parameters are ordered as in \code{getME(.,"theta")}, i.e., as the lower triangle of a matrix with standard deviations on the diagonal and correlations off the diagonal.} } }%% FIXME: allow parameter names. \item{alphamax}{maximum alpha value for likelihood ratio confidence regions; used to establish the range of values to be profiled.} \item{maxpts}{maximum number of points (in each direction, for each parameter) to evaluate in attempting to construct the profile.} \item{delta}{stepping scale for deciding on next point to profile.} \item{verbose}{level of output from internal calculations.} \item{devtol}{tolerance for fitted deviances less than baseline (supposedly minimum) deviance.} \item{maxmult}{maximum multiplier of the original step size allowed, defaults to 10.} \item{startmethod}{method for picking starting conditions for optimization (STUB).} \item{optimizer}{(character or function) optimizer to use (see \code{\link{lmer}} for details).} \item{signames}{logical indicating if abbreviated names of the form \code{.sigNN} should be used; otherwise, names are more meaningful (but longer) of the form \code{(sd|cor)_(effects)|(group)}. Note that some code for profile transformations (e.g., \code{\link{varianceProf}}) depends on \code{signames==TRUE}.} \item{\dots}{potential further arguments for \code{profile} methods.} \item{x}{the result of \code{\link{profile}} (or very similar structure)} } \description{ Methods for profile() of [ng]lmer fitted models } \details{ Methods for function \code{\link{profile}} (package \pkg{stats}), here for profiling (fitted) mixed effect models. } \section{Methods}{ \describe{ \item{signature(fitted = \"merMod\")}{ ... } } } \seealso{ For (more expensive) alternative confidence intervals: \code{\link{bootMer}}. } \examples{ fm01ML <- lmer(Yield ~ 1|Batch, Dyestuff, REML = FALSE) system.time( tpr <- profile(fm01ML, optimizer="Nelder_Mead") ) ## ~2.6s (on a 2010 Macbook Pro) system.time( tpr <- profile(fm01ML)) ## ~1s, + possible warning about bobyqa convergence (confint(tpr) -> CIpr) \donttest{% too much precision (etc). but just FYI: stopifnot(all.equal(CIpr, array(c(12.1985292, 38.2299848, 1486.4515, 84.0630513, 67.6576964, 1568.54849), dim = 3:2, dimnames = list(c(".sig01", ".sigma", "(Intercept)"), c("2.5 \%", "97.5 \%"))), tol= 1e-07))# 1.37e-9 {64b} } xyplot(tpr) densityplot(tpr, main="densityplot( profile(lmer(..)) )") splom(tpr) \donttest{% for time constraint ## Batch and residual variance only system.time(tpr2 <- profile(fm01ML, which=1:2, optimizer="Nelder_Mead")) ## GLMM example (running time ~11 seconds on a modern machine) gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) system.time(pr4 <- profile(gm1)) xyplot(pr4,layout=c(5,1),as.table=TRUE) splom(pr4) }%donttest } \keyword{methods} lme4/man/rePos-class.Rd0000644000176000001440000000064512156422373014412 0ustar ripleyusers\docType{class} \name{rePos-class} \alias{rePos-class} \title{Class \code{"rePos"}} \description{ A reference class for determining the positions in the random-effects vector that correspond to particular random-effects terms in the model formula } \section{Extends}{ All reference classes extend and inherit methods from \code{"\linkS4class{envRefClass}"}. } \examples{ showClass("rePos") } \keyword{classes} lme4/man/varianceProf.Rd0000644000176000001440000000045012156422373014630 0ustar ripleyusers\name{varianceProf} \alias{varianceProf} \title{Transform to the variance scale} \usage{ varianceProf(pr) } \arguments{ \item{pr}{a mixed-effects model profile} } \value{ a transformed mixed-effects model profile } \description{ Transform a mixed-effects profile to the variance scale } lme4/man/ranef.Rd0000644000176000001440000000703712204271665013314 0ustar ripleyusers\name{ranef} \alias{ranef} \alias{ranef.merMod} \title{Extract the modes of the random effects} \usage{ \method{ranef}{merMod} (object, condVar = FALSE, drop = FALSE, whichel = names(ans), postVar=FALSE, ...) } \arguments{ \item{object}{an object of a class of fitted models with random effects, typically an \code{"\linkS4class{merMod}"} object.} \item{condVar}{an optional logical argument indicating if the conditional variance-covariance matrices of the random effects should be added as an attribute.} \item{drop}{an optional logical argument indicating components of the return value that would be data frames with a single column, usually a column called \sQuote{\code{(Intercept)}}, should be returned as named vectors.} \item{whichel}{an optional character vector of names of grouping factors for which the random effects should be returned. Defaults to all the grouping factors.} \item{postVar}{a (deprecated) synonym for \code{condVar}} \item{\dots}{some methods for this generic function require additional arguments.} } \value{ A list of data frames, one for each grouping factor for the random effects. The number of rows in the data frame is the number of levels of the grouping factor. The number of columns is the dimension of the random effect associated with each level of the factor. If \code{condVar} is \code{TRUE} each of the data frames has an attribute called \code{"postVar"} which is a three-dimensional array with symmetric faces. (The name of this attribute is a historical artifact.) When \code{drop} is \code{TRUE} any components that would be data frames of a single column are converted to named numeric vectors. } \description{ A generic function to extract the conditional modes of the random effects from a fitted model object. For linear mixed models the conditional modes of the random effects are also the conditional means. } \details{ If grouping factor i has k levels and j random effects per level the ith component of the list returned by \code{ranef} is a data frame with k rows and j columns. If \code{condVar} is \code{TRUE} the \code{"postVar"} attribute is an array of dimension j by j by k. The kth face of this array is a positive definite symmetric j by j matrix. If there is only one grouping factor in the model the variance-covariance matrix for the entire random effects vector, conditional on the estimates of the model parameters and on the data will be block diagonal and this j by j matrix is the kth diagonal block. With multiple grouping factors the faces of the \code{"postVar"} attributes are still the diagonal blocks of this conditional variance-covariance matrix but the matrix itself is no longer block diagonal. } \note{ To produce a \dQuote{caterpillar plot} of the random effects apply \code{\link[lattice:xyplot]{dotplot}} to the result of a call to \code{ranef} with \code{condVar = TRUE}. } \examples{ fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy) fm3 <- lmer(diameter ~ (1|plate) + (1|sample), Penicillin) ranef(fm1) str(rr1 <- ranef(fm1, condVar = TRUE)) dotplot(rr1) ## default ## specify free scales in order to make Day effects more visible dotplot(rr1,scales = list(x = list(relation = 'free')))[["Subject"]] if(FALSE) { ##-- condVar=TRUE is not yet implemented for multiple terms -- FIXME str(ranef(fm2, condVar = TRUE)) } op <- options(digits = 4) ranef(fm3, drop = TRUE) options(op) } \keyword{methods} \keyword{models} lme4/man/GHrule.Rd0000644000176000001440000000225712156422373013406 0ustar ripleyusers\name{GHrule} \alias{GHrule} \title{Univariate Gauss-Hermite quadrature rule} \usage{ GHrule(ord, asMatrix = TRUE) } \arguments{ \item{ord}{scalar integer between 1 and 25 - the order, or number of nodes and weights, in the rule. When the function being multiplied by the standard normal density is a polynomial of order 2k-1 the rule of order k integrates the product exactly.} \item{asMatrix}{logical scalar - should the result be returned as a matrix. If \code{FALSE} a data frame is returned. Defaults to \code{TRUE}.} } \value{ a matrix with \code{ord} rows and three columns which are \code{z} the node positions, \code{w} the weights and \code{ldnorm}, the logarithm of the normal density evaluated at the nodes. } \description{ Create a univariate Gauss-Hermite quadrature rule } \details{ This version of Gauss-Hermite quadrature provides the node positions and weights for a scalar integral of a function multiplied by the standard normal density. } \examples{ (r5 <- GHrule(5, asMatrix=FALSE)) ## second, fourth, sixth, eighth and tenth central moments of the ## standard Gaussian density with(r5, sapply(seq(2, 10, 2), function(p) sum(w * z^p))) } lme4/man/Dyestuff.Rd0000644000176000001440000000675412156422373014017 0ustar ripleyusers\docType{data} \name{Dyestuff} \alias{Dyestuff} \alias{Dyestuff2} \title{Yield of dyestuff by batch} \format{Data frames, each with 30 observations on the following 2 variables. \describe{ \item{\code{Batch}}{a factor indicating the batch of the intermediate product from which the preparation was created.} \item{\code{Yield}}{the yield of dyestuff from the preparation (grams of standard color).} }} \source{ O.L. Davies and P.L. Goldsmith (eds), \emph{Statistical Methods in Research and Production, 4th ed.}, Oliver and Boyd, (1972), section 6.4 G.E.P. Box and G.C. Tiao, \emph{Bayesian Inference in Statistical Analysis}, Addison-Wesley, (1973), section 5.1.2 } \description{ The \code{Dyestuff} data frame provides the yield of dyestuff (Naphthalene Black 12B) from 5 different preparations from each of 6 different batchs of an intermediate product (H-acid). The \code{Dyestuff2} data were generated data in the same structure but with a large residual variance relative to the batch variance. } \details{ The \code{Dyestuff} data are described in Davies and Goldsmith (1972) as coming from \dQuote{an investigation to find out how much the variation from batch to batch in the quality of an intermediate product (H-acid) contributes to the variation in the yield of the dyestuff (Naphthalene Black 12B) made from it. In the experiment six samples of the intermediate, representing different batches of works manufacture, were obtained, and five preparations of the dyestuff were made in the laboratory from each sample. The equivalent yield of each preparation as grams of standard colour was determined by dye-trial.} The \code{Dyestuff2} data are described in Box and Tiao (1973) as illustrating \dQuote{ the case where between-batches mean square is less than the within-batches mean square. These data had to be constructed for although examples of this sort undoubtably occur in practice, they seem to be rarely published.} } \examples{ \dontshow{ # useful for the lme4-authors --- development, debugging, etc: commandArgs()[-1] if(FALSE) ## R environment variables: local({ ne <- names(e <- Sys.getenv()) list(R = e[grep("^R", ne)], "_R" = e[grep("^_R",ne)]) }) Sys.getenv("R_ENVIRON") Sys.getenv("R_PROFILE") cat("R_LIBS:\\n"); (RL <- strsplit(Sys.getenv("R_LIBS"), ":")[[1]]) nRL <- normalizePath(RL) cat("and extra(:= not in R_LIBS) .libPaths():\\n") .libPaths()[is.na(match(.libPaths(), nRL))] sessionInfo() searchpaths() pkgI <- function(pkgname) { pd <- tryCatch(packageDescription(pkgname), error=function(e)e, warning=function(w)w) if(inherits(pd, "error") || inherits(pd, "warning")) cat(sprintf("packageDescription(\\"\%s\\") \%s: \%s\\n", pkgname, class(pd)[2], pd$message)) else cat(sprintf("\%s -- built: \%s\\n\%*s -- dir : \%s\\n", pkgname, pd$Built, nchar(pkgname), "", dirname(dirname(attr(pd, "file"))))) } pkgI("Matrix") pkgI("Rcpp") ## 2012-03-12{MM}: fails with --as-cran pkgI("RcppEigen") pkgI("minqa") pkgI("lme4") } str(Dyestuff) dotplot(reorder(Batch, Yield) ~ Yield, Dyestuff, ylab = "Batch", jitter.y = TRUE, aspect = 0.3, type = c("p", "a")) dotplot(reorder(Batch, Yield) ~ Yield, Dyestuff2, ylab = "Batch", jitter.y = TRUE, aspect = 0.3, type = c("p", "a")) (fm1 <- lmer(Yield ~ 1|Batch, Dyestuff)) (fm2 <- lmer(Yield ~ 1|Batch, Dyestuff2)) } \keyword{datasets} lme4/man/merPredD-class.Rd0000644000176000001440000000272112156422373015021 0ustar ripleyusers\name{merPredD-class} \alias{merPredD-class} \title{Class \code{"merPredD"} - a dense predictor reference class} \description{ A reference class for a mixed-effects model predictor module with a dense model matrix for the fixed-effects parameters. The reference class is associated with a C++ class of the same name. As is customary, the generator object, \code{\link{merPredD}}, for the class has the same name as the class. } \note{ Objects from this reference class correspond to objects in a C++ class. Methods are invoked on the C++ class object using the external pointer in the \code{Ptr} field. When saving such an object the external pointer is converted to a null pointer, which is why there are redundant fields containing enough information as R objects to be able to regenerate the C++ object. The convention is that a field whose name begins with an upper-case letter is an R object and the corresponding field, whose name begins with the lower-case letter is a method. References to the external pointer should be through the method, not directly through the \code{Ptr} field. } \section{Extends}{ All reference classes extend and inherit methods from \code{"\linkS4class{envRefClass}"}. } \examples{ showClass("merPredD") str(slot(lmer(Yield ~ 1|Batch, Dyestuff), "pp")) } \seealso{ \code{\link{lmer}}, \code{\link{glmer}}, \code{\link{nlmer}}, \code{\link{merPredD}}, \code{\linkS4class{merMod}}. } \keyword{classes} lme4/man/modular.Rd0000644000176000001440000002442512211706636013663 0ustar ripleyusers\name{modular} \alias{glFormula} \alias{lFormula} \alias{mkGlmerDevfun} \alias{mkLmerDevfun} \alias{modular} \alias{optimizeGlmer} \alias{optimizeLmer} \alias{updateGlmerDevfun} \title{Modular functions for mixed model fits} \usage{ lFormula(formula, data = NULL, REML = TRUE, subset, weights, na.action, offset, contrasts = NULL, control = lmerControl(), ...) mkLmerDevfun(fr, X, reTrms, REML = TRUE, start = NULL, verbose = 0, control = lmerControl(), ...) optimizeLmer(devfun, optimizer = "Nelder_Mead", restart_edge = FALSE, start = NULL, verbose = 0L, control = list()) glFormula(formula, data = NULL, family = gaussian, subset, weights, na.action, offset, contrasts = NULL, mustart, etastart, control = glmerControl(), ...) mkGlmerDevfun(fr, X, reTrms, family, nAGQ = 1L, verbose = 0L, control = glmerControl(), ...) optimizeGlmer(devfun, optimizer = "bobyqa", restart_edge = FALSE, verbose = 0L, control = list(), nAGQ = 1L, stage = 1, start = NULL) updateGlmerDevfun(devfun, reTrms, nAGQ = 1L) } \arguments{ \item{\dots}{other potential arguments.} \item{control}{a list giving (for \code{[g]lFormula}) all options (see \code{\link{lmerControl}} for running the model; (for \code{mkLmerDevfun,mkGlmerDevfun}) options for inner optimization step; (for \code{optimizeLmer} and \code{optimize[Glmer}) control parameters for nonlinear optimizer (typically inherited from the \dots argument to \code{lmerControl})} \item{fr}{A model frame containing the variables needed to create an \code{\link{lmerResp}} or \code{\link{glmResp}} instance} \item{X}{fixed-effects design matrix} \item{reTrms}{information on random effects structure (see \code{\link{mkReTrms}})} \item{REML}{(logical) fit restricted maximum likelihood model?} \item{start}{starting values} \item{verbose}{print output?} \item{devfun}{a deviance function, as generated by \code{\link{mkLmerDevfun}}} \item{nAGQ}{number of Gauss-Hermite quadrature points} \item{stage}{optimization stage (1: nAGQ=0, optimize over theta only; 2: nAGQ possibly >0, optimize over theta and beta)} \item{formula}{a two-sided linear formula object describing both the fixed-effects and fixed-effects part of the model, with the response on the left of a \code{~} operator and the terms, separated by \code{+} operators, on the right. Random-effects terms are distinguished by vertical bars (\code{"|"}) separating expressions for design matrices from grouping factors.} \item{data}{an optional data frame containing the variables named in \code{formula}. By default the variables are taken from the environment from which \code{lmer} is called. While \code{data} is optional, the package authors \emph{strongly} recommend its use, especially when later applying methods such as \code{update} and \code{drop1} to the fitted model (\emph{such methods are not guaranteed to work properly if \code{data} is omitted}). If \code{data} is omitted, variables will be taken from the environment of \code{formula} (if specified as a formula) or from the parent frame (if specified as a character vector).} \item{subset}{an optional expression indicating the subset of the rows of \code{data} that should be used in the fit. This can be a logical vector, or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default.} \item{weights}{an optional vector of \sQuote{prior weights} to be used in the fitting process. Should be \code{NULL} or a numeric vector.} \item{na.action}{a function that indicates what should happen when the data contain \code{NA}s. The default action (\code{na.omit}, inherited from the 'factory fresh' value of \code{getOption("na.action")}) strips any observations with any missing values in any variables.} \item{offset}{this can be used to specify an \emph{a priori} known component to be included in the linear predictor during fitting. This should be \code{NULL} or a numeric vector of length equal to the number of cases. One or more \code{\link{offset}} terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See \code{\link{model.offset}}.} \item{contrasts}{an optional list. See the \code{contrasts.arg} of \code{model.matrix.default}.} \item{optimizer}{character - name of optimizing function(s). A character vector or list of functions: length 1 for \code{lmer} or \code{glmer}, possibly length 2 for \code{glmer}). The built-in optimizers are \code{\link{Nelder_Mead}} and \code{\link[minqa]{bobyqa}} (from the \pkg{minqa} package). Any minimizing function that allows box constraints can be used provided that it (1) takes input parameters \code{fn} (function to be optimized), \code{par} (starting parameter values), \code{lower} (lower bounds) and \code{control} (control parameters, passed through from the \code{control} argument) and (2) returns a list with (at least) elements \code{par} (best-fit parameters), \code{fval} (best-fit function value), \code{conv} (convergence code) and (optionally) \code{message} (informational message, or explanation of convergence failure). Special provisions are made for \code{\link{bobyqa}}, \code{\link{Nelder_Mead}}, and optimizers wrapped in the \pkg{optimx} package; to use \pkg{optimx} optimizers (including \code{L-BFGS-B} from base \code{\link{optim}} and \code{\link{nlminb}}), pass the \code{method} argument to \code{optim} in the \code{control} argument. For \code{glmer}, if \code{length(optimizer)==2}, the first element will be used for the preliminary (random effects parameters only) optimization, while the second will be used for the final (random effects plus fixed effect parameters) phase. See \code{\link{modular}} for more information on these two phases.} \item{restart_edge}{logical - should the optimizer attempt a restart when it finds a solution at the boundary (i.e. zero random-effect variances or perfect +/-1 correlations)?} \item{family}{a GLM family, see \code{\link[stats]{glm}} and \code{\link[stats]{family}}.} \item{mustart}{optional starting values on the scale of the conditional mean, as in \code{\link[stats]{glm}}; see there for details.} \item{etastart}{optional starting values on the scale of the unbounded predictor as in \code{\link[stats]{glm}}; see there for details.} } \value{ \bold{lFormula, glFormula}: A list containing components, \item{fr}{model frame} \item{X}{fixed-effect design matrix} \item{reTrms}{list containing information on random effects structure: result of \code{\link{mkReTrms}}} \item{REML}{(lFormula only): logical flag: use restricted maximum likelihood? (Copy of argument.)} \bold{mkLmerDevfun, mkGlmerDevfun}: A function to calculate deviance (or restricted deviance) as a function of the theta (random-effect) parameters (for GlmerDevfun, of beta (fixed-effect) parameters as well). These deviance functions have an environment containing objects required for their evaluation. CAUTION: The output object of \code{mk(Gl|L)merDevfun} is an \code{\link{environment}} containing reference class objects (see \code{\link{ReferenceClasses}}, \code{\link{merPredD-class}}, \code{\link{lmResp-class}}), which behave in ways that may surprise many users. For example, if the output of \code{mk(Gl|L)merDevfun} is naively copied, then modifications to the original will also appear in the copy (and vice versa). To avoid this behavior one must make a deep copy (see \code{\link{ReferenceClasses}} for details). \cr \cr \bold{optimizeLmer}: Results of an optimization. \cr \cr } \description{ Modular functions for mixed model fits } \details{ These functions make up the internal components of a(n) [gn]lmer fit. \itemize{ \item \code{[g]lFormula} takes the arguments that would normally be passed to \code{[g]lmer}, checking for errors and processing the formula and data input to create \item \code{mk(Gl|L)merDevfun} takes the output of the previous step (minus the \code{formula} component) and creates a deviance function \item \code{optimize(Gl|L)mer} takes a deviance function and optimizes over \code{theta} (or over \code{theta} and \code{beta}, if \code{stage} is set to 2 for \code{optimizeGlmer} \item \code{updateGlmerDevfun} takes the first stage of a GLMM optimization (with \code{nAGQ=0}, optimizing over \code{theta} only) and produces a second-stage deviance function \item \code{\link{mkMerMod}} takes the \emph{environment} of a deviance function, the results of an optimization, a list of random-effect terms, a model frame, and a model all and produces a \code{[g]lmerMod} object } } \examples{ ### Fitting a linear mixed model in 4 modularized steps ## 1. Parse the data and formula: lmod <- lFormula(Reaction ~ Days + (Days|Subject), sleepstudy) names(lmod) ## 2. Create the deviance function to be optimized: (devfun <- do.call(mkLmerDevfun, lmod)) ls(environment(devfun)) # the environment of devfun contains objects required for its evaluation ## 3. Optimize the deviance function: opt <- optimizeLmer(devfun) opt[1:3] ## 4. Package up the results: mkMerMod(environment(devfun), opt, lmod$reTrms, fr = lmod$fr) ### Same model in one line lmer(Reaction ~ Days + (Days|Subject), sleepstudy) ### Fitting a generalized linear mixed model in six modularized steps ## 1. Parse the data and formula: glmod <- glFormula(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) names(glmod) ## 2. Create the deviance function for optimizing over theta: (devfun <- do.call(mkGlmerDevfun, glmod)) ls(environment(devfun)) # the environment of devfun contains lots of info ## 3. Optimize over theta using a rough approximation (i.e. nAGQ = 0): (opt <- optimizeGlmer(devfun)) ## 4. Update the deviance function for optimizing over theta and beta: (devfun <- updateGlmerDevfun(devfun, glmod$reTrms)) ## 5. Optimize over theta and beta: opt <- optimizeGlmer(devfun, stage=2) opt[1:3] ## 6. Package up the results: mkMerMod(environment(devfun), opt, glmod$reTrms, fr = glmod$fr) ### Same model in one line glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) } lme4/man/lmList-class.Rd0000644000176000001440000000071712156422373014566 0ustar ripleyusers\docType{class} \name{lmList-class} \alias{lmList-class} \alias{show,lmList-method} \title{Class "lmList" of 'lm' Objects on Common Model} \description{ Class \code{"lmList"} is an S4 class with basically a list of objects of class \code{\link{lm}} with a common model. } \section{Objects from the Class}{ Objects can be created by calls of the form \code{new("lmList", ...)} or, more commonly, by a call to \code{\link{lmList}}. } \keyword{classes} lme4/man/lmResp-class.Rd0000644000176000001440000000341612156422373014563 0ustar ripleyusers\name{lmResp-class} \alias{glmResp-class} \alias{lmerResp-class} \alias{lmResp-class} \alias{nlsResp-class} \title{Classes \code{"lmResp"}, \code{"glmResp"}, \code{"nlsResp"} and \code{"lmerResp"}} \description{ Reference classes for response modules, including linear models, \code{"lmResp"}, generalized linear models, \code{"glmResp"}, nonlinear models, \code{"nlsResp"} and linear mixed-effects models, \code{"lmerResp"}. Each reference class is associated with a C++ class of the same name. As is customary, the generator object for each class has the same name as the class. } \note{ Objects from these reference classes correspond to objects in C++ classes. Methods are invoked on the C++ classes using the external pointer in the \code{ptr} field. When saving such an object the external pointer is converted to a null pointer, which is why there are redundant fields containing enough information as R objects to be able to regenerate the C++ object. The convention is that a field whose name begins with an upper-case letter is an R object and the corresponding field whose name begins with the lower-case letter is a method. Access to the external pointer should be through the method, not through the field. } \section{Extends}{ All reference classes extend and inherit methods from \code{"\linkS4class{envRefClass}"}. Furthermore, \code{"glmResp"}, \code{"nlsResp"} and \code{"lmerResp"} all extend the \code{"lmResp"} class. } \examples{ showClass("lmResp") str(lmResp$new(y=1:4)) showClass("glmResp") str(glmResp$new(family=poisson(), y=1:4)) showClass("nlsResp") showClass("lmerResp") str(lmerResp$new(y=1:4)) } \seealso{ \code{\link{lmer}}, \code{\link{glmer}}, \code{\link{nlmer}}, \code{\linkS4class{merMod}}. } \keyword{classes} lme4/man/mkReTrms.Rd0000644000176000001440000000234512211706636013761 0ustar ripleyusers\name{mkReTrms} \alias{mkReTrms} \title{Create Z, Lambda, Lind, etc.} \usage{ mkReTrms(bars, fr) } \arguments{ \item{bars}{a list of parsed random-effects terms} \item{fr}{a model frame in which to evaluate these terms} } \value{ a list with components \item{Zt}{transpose of the sparse model matrix for the random effects} \item{Lambdat}{transpose of the sparse relative covariance factor} \item{Lind}{an integer vector of indices determining the mapping of the elements of the \code{theta} to the \code{"x"} slot of \code{Lambdat}} \item{theta}{initial values of the covariance parameters} \item{lower}{lower bounds on the covariance parameters} \item{flist}{list of grouping factors used in the random-effects terms} \item{cnms}{a list of column names of the random effects according to the grouping factors} } \description{ From the result of \code{\link{findbars}} applied to a model formula and and the evaluation frame, create the model matrix, etc. associated with random-effects terms. See the description of the returned value for a detailed list. } \seealso{ Other utilities: \code{\link{findbars}}, \code{\link{mkRespMod}}, \code{\link{nlformula}}, \code{\link{nobars}}, \code{\link{subbars}} } lme4/man/glmFamily-class.Rd0000644000176000001440000000250512156422373015240 0ustar ripleyusers\docType{class} \name{glmFamily-class} \alias{glmFamily-class} \title{Class \code{"glmFamily"} - a reference class for \code{\link{family}}} \description{ This class is a wrapper class for \code{\link{family}} objects specifying a distibution family and link function for a generalized linear model (\code{\link{glm}}). The reference class contains an external pointer to a C++ object representing the class. For common families and link functions the functions in the family are implemented in compiled code so they can be accessed from other compiled code and for a speed boost. } \note{ Objects from this reference class correspond to objects in a C++ class. Methods are invoked on the C++ class using the external pointer in the \code{Ptr} field. When saving such an object the external pointer is converted to a null pointer, which is why there is a redundant field \code{ptr} that is an active-binding function returning the external pointer. If the \code{Ptr} field is a null pointer, the external pointer is regenerated for the stored \code{family} field. } \section{Extends}{ All reference classes extend and inherit methods from \code{"\linkS4class{envRefClass}"}. } \examples{ str(glmFamily$new(family=poisson())) } \seealso{ \code{\link{family}}, \code{\link{glmFamily}} } \keyword{classes} lme4/man/pvalues.Rd0000644000176000001440000000440312232467515013674 0ustar ripleyusers\name{pvalues} \alias{mcmcsamp} \alias{pvalues} \title{Getting p-values for fitted models} \description{ One of the most frequently asked questions about \code{lme4} is "how do I calculate p-values for estimated parameters?" Previous versions of \code{lme4} provided the \code{mcmcsamp} function, which efficiently generated a Markov chain Monte Carlo sample from the posterior distribution of the parameters, assuming flat (scaled likelihood) priors. Due to difficulty in constructing a version of \code{mcmcsamp} that was reliable even in cases where the estimated random effect variances were near zero (e.g. \url{https://stat.ethz.ch/pipermail/r-sig-mixed-models/2009q4/003115.html}), \code{mcmcsamp} has been withdrawn (or more precisely, not updated to work with \code{lme4} versions >=1.0.0). Many users, including users of the \code{aovlmer.fnc} function from the \code{languageR} package which relies on \code{mcmcsamp}, will be deeply disappointed by this lacuna. Users who need p-values have a variety of options: \itemize{ \item likelihood ratio tests via \code{anova} (MC,+) \item profile confidence intervals via \code{\link{profile.merMod}} and \code{\link{confint.merMod}} (CI,+) \item parametric bootstrap confidence intervals and model comparisons via \code{\link{bootMer}} (or \code{PBmodcomp} in the \code{pbkrtest} package) (MC/CI,*,+) \item for random effects, simulation tests via the \code{RLRsim} package (MC,*) \item for fixed effects, F tests via Kenward-Roger approximation using \code{KRmodcomp} from the \code{pbkrtest} package (MC) \item \code{car::Anova} and \code{lmerTest::anova} provide wrappers for \code{pbkrtest}: the latter also provides t tests via the Satterthwaite approximation (P,*) } In the list above, the methods marked \code{MC} provide explicit model comparisons; \code{CI} denotes confidence intervals; and \code{P} denotes parameter-level or sequential tests of all effects in a model. The starred (*) suggestions provide finite-size corrections (important when the number of groups is <50); those marked (+) support GLMMs as well as LMMs. When all else fails, don't forget to keep p-values in perspective: \url{http://www.phdcomics.com/comics/archive.php?comicid=905} } lme4/man/findbars.Rd0000644000176000001440000000251412232467515014006 0ustar ripleyusers\name{findbars} \alias{findbars} \title{Determine random-effects expressions from a formula} \usage{ findbars(term) } \arguments{ \item{term}{a mixed-model formula} } \value{ pairs of expressions that were separated by vertical bars } \description{ From the right hand side of a formula for a mixed-effects model, determine the pairs of expressions that are separated by the vertical bar operator. Also expand the slash operator in grouping factor expressions. } \section{Note}{ This function is called recursively on individual terms in the model, which is why the argument is called \code{term} and not a name like \code{form}, indicating a formula. } \examples{ findbars(f1 <- Reaction ~ Days + (Days|Subject)) ## => list( Days | Subject ) findbars(y ~ Days + (1|Subject) + (0+Days|Subject)) ## => list of length 2: list ( 1 | Subject , 0+Days|Subject) findbars(~ 1 + (1|batch/cask)) ## => list of length 2: list ( 1 | cask:batch , 1 | batch) \dontshow{ stopifnot(identical(findbars(f1), list(expression(Days | Subject)[[1]]))) } } \seealso{ \code{\link{formula}}, \code{\link{model.frame}}, \code{\link{model.matrix}}. Other utilities: \code{\link{mkRespMod}}, \code{\link{mkReTrms}}, \code{\link{nlformula}}, \code{\link{nobars}}, \code{\link{subbars}} } \keyword{models} \keyword{utilities} lme4/man/mkdevfun.Rd0000644000176000001440000000341412160101066014017 0ustar ripleyusers\name{mkdevfun} \alias{mkdevfun} \title{Create a deviance evaluation function from a predictor and a response module} \usage{ mkdevfun(rho, nAGQ = 1L, verbose = 0, control = list()) } \arguments{ \item{rho}{an environment containing \code{pp}, a prediction module, typically of class \code{\linkS4class{merPredD}} and \code{resp}, a response module, e.g., of class \code{\linkS4class{lmerResp}}.} \item{nAGQ}{scalar integer - the number of adaptive Gauss-Hermite quadrature points. A value of 0 indicates that both the fixed-effects parameters and the random effects are optimized by the iteratively reweighted least squares algorithm.} \item{verbose}{Logical: print verbose output?} \item{control}{list of control parameters, a subset of those specified by \code{\link{lmerControl}} (\code{tolPwrss} and \code{compDev} for GLMMs, \code{tolPwrss} for NLMMs)} } \value{ A function of one numeric argument. } \description{ From an merMod object create an R function that takes a single argument, which is the new parameter value, and returns the deviance. } \details{ The function returned by \code{mkdevfun} evaluates the deviance of the model represented by the predictor module, \code{pp}, and the response module, \code{resp}. For \code{\link{lmer}} model objects the argument of the resulting function is the variance component parameter, \code{theta}, with lower bound. For \code{glmer} or \code{nlmer} model objects with \code{nAGQ = 0} the argument is also \code{theta}. However, when nAGQ > 0 the argument is \code{c(theta, beta)}. } \examples{ (dd <- lmer(Yield ~ 1|Batch, Dyestuff, devFunOnly=TRUE)) dd(0.8) minqa::bobyqa(1, dd, 0) } \seealso{ \code{\link{lmer}}, \code{\link{glmer}} and \code{\link{nlmer}} } \keyword{models} lme4/man/sleepstudy.Rd0000644000176000001440000000321212156422373014411 0ustar ripleyusers\docType{data} \name{sleepstudy} \alias{sleepstudy} \title{Reaction times in a sleep deprivation study} \format{A data frame with 180 observations on the following 3 variables. \describe{ \item{\code{Reaction}}{Average reaction time (ms)} \item{\code{Days}}{Number of days of sleep deprivation} \item{\code{Subject}}{Subject number on which the observation was made.} }} \description{ The average reaction time per day for subjects in a sleep deprivation study. On day 0 the subjects had their normal amount of sleep. Starting that night they were restricted to 3 hours of sleep per night. The observations represent the average reaction time on a series of tests given each day to each subject. } \details{ These data are from the study described in Belenky et al. (2003), for the sleep-deprived group and for the first 10 days of the study, up to the recovery period. } \examples{ str(sleepstudy) xyplot(Reaction ~ Days | Subject, sleepstudy, type = c("g","p","r"), index = function(x,y) coef(lm(y ~ x))[1], xlab = "Days of sleep deprivation", ylab = "Average reaction time (ms)", aspect = "xy") (fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy)) (fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) } \references{ Gregory Belenky, Nancy J. Wesensten, David R. Thorne, Maria L. Thomas, Helen C. Sing, Daniel P. Redmond, Michael B. Russo and Thomas J. Balkin (2003) Patterns of performance degradation and restoration during sleep restriction and subsequent recovery: a sleep dose-response study. \emph{Journal of Sleep Research} \bold{12}, 1--12. } \keyword{datasets} lme4/man/lme4-package.Rd0000644000176000001440000000753712204271665014460 0ustar ripleyusers\name{lme4-package} \alias{lme4} \alias{lme4-package} \docType{package} \title{Linear, generalized linear, and nonlinear mixed models} \description{ \code{lme4} provides functions for fitting and analyzing mixed models: linear (\code{\link{lmer}}), generalized linear (\code{\link{glmer}}) and nonlinear (\code{\link{nlmer}}.) } \section{Differences between \code{nlme} and \code{lme4}}{ \code{lme4} covers approximately the same ground as the earlier \code{nlme} package. The most important differences are: \itemize{ \item \code{lme4} uses modern, efficient linear algebra methods as implemented in the \code{Eigen} package, and uses reference classes to avoid undue copying of large objects; it is therefore likely to be faster and more memory-efficient than \code{nlme}. \item \code{lme4} includes generalized linear mixed model (GLMM) capabilities, via the \code{\link{glmer}} function. \item \code{lme4} does \emph{not} currently implement \code{nlme}'s features for modeling heteroscedasticity and correlation of residuals. \item \code{lme4} does not currently offer the same flexibility as \code{nlme} for composing complex variance-covariance structures, but it does implement crossed random effects in a way that is both easier for the user and much faster. \item \code{lme4} offers built-in facilities for likelihood profiling and parametric bootstrapping. \item \code{lme4} is designed to be more modular than \code{nlme}, making it easier for downstream package developers and end-users to re-use its components for extensions of the basic mixed model framework. It also allows more flexibility for specifying different functions for optimizing over the random-effects variance-covariance parameters. \item \code{lme4} is not (yet) as well-documented as \code{nlme}. } } \section{Differences between current (1.0.+) and previous versions of \code{lme4}}{ \itemize{ \item \code{[gn]lmer} now produces objects of class \code{\linkS4class{merMod}} rather than class \code{mer} as before \item the new version uses a combination of S3 and reference classes (see \code{\link{ReferenceClasses}}, \code{\link{merPredD-class}}, and \code{\link{lmResp-class}}) as well as S4 classes; partly for this reason it is more interoperable with \code{nlme} \item The internal structure of [gn]lmer is now more modular, allowing finer control of the different steps of argument checking; construction of design matrices and data structures; parameter estimation; and construction of the final \code{merMod} object (see \code{\link{modular}}) \item profiling and parametric bootstrapping are new in the current version \item the new version of \code{lme4} does \emph{not} provide an \code{mcmcsamp} (post-hoc MCMC sampling) method, because this was deemed to be unreliable. Alternatives for computing p-values include parametric bootstrapping (\code{\link{bootMer}}) or methods implemented in the \code{pbkrtest} package and leveraged by the \code{lmerTest} package and the \code{Anova} function in the \code{car} package (see \code{\link{pvalues}} for more details). } } \section{Caveats and trouble-shooting}{ \itemize{ \item Some users who have previously installed versions of the RcppEigen and minqa packages may encounter segmentation faults (!!); the solution is to make sure to re-install these packages before installing \code{lme4}. (Because the problem is not with the explicit \emph{version} of the packages, but with running packages that were built with different versions of \code{Rcpp} in conjunction with each other, simply making sure you have the latest version, or using \code{update.packages}, will not necessarily solve the problem; you must actually re-install the packages. The problem is most likely with \code{minqa}.) } } lme4/man/glmer.Rd0000644000176000001440000002077412204271665013332 0ustar ripleyusers\name{glmer} \alias{glmer} \title{Fit Generalized Linear Mixed-Effects Models} \usage{ glmer(formula, data = NULL, family = gaussian, control = glmerControl(), start = NULL, verbose = 0L, nAGQ = 1L, subset, weights, na.action, offset, contrasts = NULL, mustart, etastart, devFunOnly = FALSE, ...) } \arguments{ \item{family}{a GLM family, see \code{\link[stats]{glm}} and \code{\link[stats]{family}}.} \item{nAGQ}{integer scalar - the number of points per axis for evaluating the adaptive Gauss-Hermite approximation to the log-likelihood. Defaults to 1, corresponding to the Laplace approximation. Values greater than 1 produce greater accuracy in the evaluation of the log-likelihood at the expense of speed. A value of zero uses a faster but less exact form of parameter estimation for GLMMs by optimizing the random effects and the fixed-effects coefficients in the penalized iteratively reweighted least squares step.} \item{start}{a named list of starting values for the parameters in the model, or a numeric vector. A numeric \code{start} argument will be used as the starting value of \code{theta}. If \code{start} is a list, the \code{theta} element (a numeric vector) is used as the starting value for the first optimization step (default=1 for diagonal elements and 0 for off-diagonal elements of the lower Cholesky factor); the fitted value of \code{theta} from the first step, plus \code{start[["fixef"]]}, are used as starting values for the second optimization step. If \code{start} has both \code{fixef} and \code{theta} elements, the first optimization step is skipped. For more details or finer control of optimization, see \code{\link{modular}}.} \item{mustart}{optional starting values on the scale of the conditional mean, as in \code{\link[stats]{glm}}; see there for details.} \item{etastart}{optional starting values on the scale of the unbounded predictor as in \code{\link[stats]{glm}}; see there for details.} \item{\dots}{other potential arguments. A \code{method} argument was used in earlier versions of the package. Its functionality has been replaced by the \code{nAGQ} argument.} \item{formula}{a two-sided linear formula object describing both the fixed-effects and fixed-effects part of the model, with the response on the left of a \code{~} operator and the terms, separated by \code{+} operators, on the right. Random-effects terms are distinguished by vertical bars (\code{"|"}) separating expressions for design matrices from grouping factors.} \item{data}{an optional data frame containing the variables named in \code{formula}. By default the variables are taken from the environment from which \code{lmer} is called. While \code{data} is optional, the package authors \emph{strongly} recommend its use, especially when later applying methods such as \code{update} and \code{drop1} to the fitted model (\emph{such methods are not guaranteed to work properly if \code{data} is omitted}). If \code{data} is omitted, variables will be taken from the environment of \code{formula} (if specified as a formula) or from the parent frame (if specified as a character vector).} \item{control}{a list (of correct class, resulting from \code{\link{lmerControl}()} or \code{\link{glmerControl}()} respectively) containing control parameters, including the nonlinear optimizer to be used and parameters to be passed through to the nonlinear optimizer, see the \code{*lmerControl} documentation for details.} \item{verbose}{integer scalar. If \code{> 0} verbose output is generated during the optimization of the parameter estimates. If \code{> 1} verbose output is generated during the individual PIRLS steps.} \item{subset}{an optional expression indicating the subset of the rows of \code{data} that should be used in the fit. This can be a logical vector, or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default.} \item{weights}{an optional vector of \sQuote{prior weights} to be used in the fitting process. Should be \code{NULL} or a numeric vector.} \item{na.action}{a function that indicates what should happen when the data contain \code{NA}s. The default action (\code{na.omit}, inherited from the 'factory fresh' value of \code{getOption("na.action")}) strips any observations with any missing values in any variables.} \item{offset}{this can be used to specify an \emph{a priori} known component to be included in the linear predictor during fitting. This should be \code{NULL} or a numeric vector of length equal to the number of cases. One or more \code{\link{offset}} terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See \code{\link{model.offset}}.} \item{contrasts}{an optional list. See the \code{contrasts.arg} of \code{model.matrix.default}.} \item{devFunOnly}{logical - return only the deviance evaluation function. Note that because the deviance function operates on variables stored in its environment, it may not return \emph{exactly} the same values on subsequent calls (but the results should always be within machine tolerance).} } \value{ An object of class \code{glmerMod}, for which many methods are available (e.g. \code{methods(class="glmerMod")}) } \description{ Fit a generalized linear mixed model (GLMM) } \details{ Fit a generalized linear mixed model, which incorporates both fixed-effects parameters and random effects in a linear predictor, via maximum likelihood. The linear predictor is related to the conditional mean of the response through the inverse link function defined in the GLM \code{family}. The expression for the likelihood of a mixed-effects model is an integral over the random effects space. For a linear mixed-effects model (LMM), as fit by \code{\link{lmer}}, this integral can be evaluated exactly. For a GLMM the integral must be approximated. The most reliable approximation for GLMMs with a single grouping factor for the random effects is adaptive Gauss-Hermite quadrature. The \code{nAGQ} argument controls the number of nodes in the quadrature formula. A model with a single, scalar random-effects term could reasonably use up to 25 quadrature points per scalar integral. With vector-valued random effects the complexity of the Gauss-Hermite quadrature formulas increases dramatically with the dimension. For a 3-dimensional vector-valued random effect \code{nAGQ=5} requires 93 evaluations of the GLM deviance per evaluation of the approximate GLMM deviance. For 20-dimensional evaluations of the GLM deviance per evaluation of the approximate GLMM deviance. The default approximation is the Laplace approximation, corresponding to \code{nAGQ=1}. } \examples{ ## generalized linear mixed model library(lattice) xyplot(incidence/size ~ period|herd, cbpp, type=c('g','p','l'), layout=c(3,5), index.cond = function(x,y)max(y)) (gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial)) ## using nAGQ=0 only gets close to the optimum (gm1a <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), cbpp, binomial, nAGQ = 0)) ## using nAGQ = 9 provides a better evaluation of the deviance ## Currently the internal calculations use the sum of deviance residuals, ## which is not directly comparable with the nAGQ=0 or nAGQ=1 result. (gm1a <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), cbpp, binomial, nAGQ = 9)) ## GLMM with individual-level variability (accounting for overdispersion) ## For this data set the model is the same as one allowing for a period:herd ## interaction, which the plot indicates could be needed. cbpp$obs <- 1:nrow(cbpp) (gm2 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd) + (1|obs), family = binomial, data = cbpp)) anova(gm1,gm2) ## glmer and glm log-likelihoods are consistent gm1Devfun <- update(gm1,devFunOnly=TRUE) gm0 <- glm(cbind(incidence, size - incidence) ~ period, family = binomial, data = cbpp) ## evaluate GLMM deviance at RE variance=theta=0, beta=(GLM coeffs) gm1Dev0 <- gm1Devfun(c(0,coef(gm0))) ## compare stopifnot(all.equal(gm1Dev0,c(-2*logLik(gm0)))) } \seealso{ \code{\link{lmer}} (for details on formulas and parameterization); \code{\link[stats]{glm}} } \concept{ GLMM } \keyword{models} lme4/man/residuals.merMod.Rd0000644000176000001440000000343512172000024015413 0ustar ripleyusers\name{residuals.merMod} \alias{residuals.glmResp} \alias{residuals.lmResp} \alias{residuals.merMod} \title{residuals of merMod objects} \usage{ \method{residuals}{merMod} (object, type = if (isGLMM(object)) "deviance" else "response", scaled = FALSE, ...) \method{residuals}{lmResp} (object, type = c("working", "response", "deviance", "pearson", "partial"), ...) \method{residuals}{glmResp} (object, type = c("deviance", "pearson", "working", "response", "partial"), ...) } \arguments{ \item{object}{a fitted [g]lmer (\code{merMod}) object} \item{type}{type of residuals} \item{scaled}{scale residuals by residual standard deviation (=scale parameter)?} \item{\dots}{additional arguments (ignored: for method compatibility)} } \description{ residuals of merMod objects } \details{ \itemize{ \item The default residual type varies between \code{lmerMod} and \code{glmerMod} objects: they try to mimic \code{\link{residuals.lm}} and \code{\link{residuals.glm}} respectively. In particular, the default \code{type} is \code{"response"}, i.e. (observed-fitted) for \code{lmerMod} objects vs. \code{"deviance"} for \code{glmerMod} objects. \code{type="partial"} is not yet implemented for either type. \item Note that the meaning of \code{"pearson"} residuals differs between \code{\link{residuals.lm}} and \code{\link{residuals.lme}}. The former returns values scaled by the square root of user-specified weights (if any), but \emph{not} by the residual standard deviation, while the latter returns values scaled by the estimated standard deviation (which will include the effects of any variance structure specified in the \code{weights} argument). To replicate \code{lme} behaviour, use \code{type="pearson"}, \code{scaled=TRUE}. } } lme4/man/GQdk.Rd0000644000176000001440000000152512156422373013043 0ustar ripleyusers\name{GQdk} \alias{GQdk} \title{Sparse Gaussian Quadrature grid} \usage{ GQdk(d = 1L, k = 1L) } \arguments{ \item{d}{integer scalar - the dimension of the function to be integrated with respect to the standard \code{d}-dimensional Gaussian density} \item{k}{integer scalar - the order of the grid. A grid of order \code{k} provides an exact result for a polynomial of total order of \code{2k - 1} or less multiplied by the} } \value{ a matrix with \code{d + 1} columns. The first column is the weights and the remaining \code{d} columns are the node coordinates. } \description{ Generate the sparse multidimensional Gaussian quadrature grids } \note{ The number of nodes gets very large very quickly with increasing \code{d} and \code{k}. See the charts at \url{http://www.sparse-grids.de}. } \examples{ GQdk(2,5) } lme4/man/refitML.Rd0000644000176000001440000000142012156422373013551 0ustar ripleyusers\name{refitML} \alias{refitML} \alias{refitML.merMod} \title{Refit a model by maximum likelihood criterion} \usage{ refitML(x, ...) \method{refitML}{merMod} (x, optimizer = "bobyqa", ...) } \arguments{ \item{x}{a fitted model, usually of class \code{"\linkS4class{lmerMod}"}, to be refit according to the maximum likelihood criterion} \item{...}{optional additional parameters. None are used at present.} \item{optimizer}{a string indicating the optimizer to be used.} } \value{ an object like \code{x} but fit by maximum likelihood } \description{ Refit a model using the maximum likelihood criterion } \details{ This function is primarily used to get a maximum likelihood fit of a linear mixed-effects model for an \code{\link{anova}} comparison. } lme4/man/devcomp.Rd0000644000176000001440000000112412156422373013645 0ustar ripleyusers\name{devcomp} \alias{devcomp} \title{Extract the deviance component list} \usage{ devcomp(x) } \arguments{ \item{x}{a fitted model of class \code{\linkS4class{merMod}}} } \value{ a list with components \item{dims}{a named integer vector of various dimensions} \item{cmp}{a named numeric vector of components of the deviance} } \description{ Return the deviance component list } \details{ A fitted model of class \code{\linkS4class{merMod}} has a \code{devcomp} slot as described in the value section. } \note{ This function is deprecated, use \code{getME(., "devcomp")} } lme4/man/golden-class.Rd0000644000176000001440000000054612156422373014572 0ustar ripleyusers\docType{class} \name{golden-class} \alias{golden-class} \title{Class \code{"golden"}} \description{ A reference class for a golden search scalar optimizer using reverse communication. } \section{Extends}{ All reference classes extend and inherit methods from \code{"\linkS4class{envRefClass}"}. } \examples{ showClass("golden") } \keyword{classes} lme4/man/mkRespMod.Rd0000644000176000001440000000165712156422373014124 0ustar ripleyusers\name{mkRespMod} \alias{mkRespMod} \title{Create an lmerResp, glmResp or nlsResp instance} \usage{ mkRespMod(fr, REML = NULL, family = NULL, nlenv = NULL, nlmod = NULL, ...) } \arguments{ \item{fr}{a model frame} \item{REML}{logical scalar, value of REML for an lmerResp instance} \item{family}{the optional glm family (glmResp only)} \item{nlenv}{the nonlinear model evaluation environment (nlsResp only)} \item{nlmod}{the nonlinear model function (nlsResp only)} \item{...}{where to look for response information if \code{fr} is missing. Can contain a model response, \code{y}, offset, \code{offset}, and weights, \code{weights}.} } \value{ an lmerResp or glmResp or nlsResp instance } \description{ Create an lmerResp, glmResp or nlsResp instance } \seealso{ Other utilities: \code{\link{findbars}}, \code{\link{mkReTrms}}, \code{\link{nlformula}}, \code{\link{nobars}}, \code{\link{subbars}} } lme4/man/InstEval.Rd0000644000176000001440000000406712156422373013746 0ustar ripleyusers\docType{data} \name{InstEval} \alias{InstEval} \title{University Lecture/Instructor Evaluations by Students at ETH} \format{A data frame with 73421 observations on the following 7 variables. \describe{ \item{\code{s}}{a factor with levels \code{1:2972} denoting individual students.} \item{\code{d}}{a factor with 1128 levels from \code{1:2160}, denoting individual professors or lecturers.}% ("d": \dQuote{Dozierende} in German) \item{\code{studage}}{an ordered factor with levels \code{2} < \code{4} < \code{6} < \code{8}, denoting student's \dQuote{age} measured in the \emph{semester} number the student has been enrolled.} \item{\code{lectage}}{an ordered factor with 6 levels, \code{1} < \code{2} < ... < \code{6}, measuring how many semesters back the lecture rated had taken place.} \item{\code{service}}{a binary factor with levels \code{0} and \code{1}; a lecture is a \dQuote{service}, if held for a different department than the lecturer's main one.} \item{\code{dept}}{a factor with 14 levels from \code{1:15}, using a random code for the department of the lecture.} \item{\code{y}}{a numeric vector of \emph{ratings} of lectures by the students, using the discrete scale \code{1:5}, with meanings of \sQuote{poor} to \sQuote{very good}.} } Each observation is one student's rating for a specific lecture (of one lecturer, during one semester in the past).} \description{ University lecture evaluations by students at ETH Zurich, anonymized for privacy protection. This is an interesting \dQuote{medium} sized example of a \emph{partially} nested mixed effect model. } \details{ The main goal of the survey is to find \dQuote{the best liked prof}, according to the lectures given. Statistical analysis of such data has been the basis for a (student) jury selecting the final winners. The present data set has been anonymized and slightly simplified on purpose. } \examples{ str(InstEval) head(InstEval, 16) xtabs(~ service + dept, InstEval) } \keyword{datasets} lme4/man/lmer.Rd0000644000176000001440000001466312232467515013165 0ustar ripleyusers\name{lmer} \title{Fit Linear Mixed-Effects Models} \alias{lmer} \concept{ LMM } \description{ Fit a linear mixed-effects model (LMM) to data. } \usage{ lmer(formula, data = NULL, REML = TRUE, control = lmerControl(), start = NULL, verbose = 0L, subset, weights, na.action, offset, contrasts = NULL, devFunOnly = FALSE, ...) } \arguments{ \item{formula}{a two-sided linear formula object describing both the fixed-effects and fixed-effects part of the model, with the response on the left of a \code{~} operator and the terms, separated by \code{+} operators, on the right. Random-effects terms are distinguished by vertical bars (\code{"|"}) separating expressions for design matrices from grouping factors.} \item{data}{an optional data frame containing the variables named in \code{formula}. By default the variables are taken from the environment from which \code{lmer} is called. While \code{data} is optional, the package authors \emph{strongly} recommend its use, especially when later applying methods such as \code{update} and \code{drop1} to the fitted model (\emph{such methods are not guaranteed to work properly if \code{data} is omitted}). If \code{data} is omitted, variables will be taken from the environment of \code{formula} (if specified as a formula) or from the parent frame (if specified as a character vector).} \item{REML}{logical scalar - Should the estimates be chosen to optimize the REML criterion (as opposed to the log-likelihood)?} \item{control}{a list (of correct class, resulting from \code{\link{lmerControl}()} or \code{\link{glmerControl}()} respectively) containing control parameters, including the nonlinear optimizer to be used and parameters to be passed through to the nonlinear optimizer, see the \code{*lmerControl} documentation for details.} \item{start}{a named \code{\link{list}} of starting values for the parameters in the model. For \code{lmer} this can be a numeric vector or a list with one component named \code{"theta"}.} \item{verbose}{integer scalar. If \code{> 0} verbose output is generated during the optimization of the parameter estimates. If \code{> 1} verbose output is generated during the individual PIRLS steps.} \item{subset}{an optional expression indicating the subset of the rows of \code{data} that should be used in the fit. This can be a logical vector, or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default.} \item{weights}{an optional vector of \sQuote{prior weights} to be used in the fitting process. Should be \code{NULL} or a numeric vector.} \item{na.action}{a function that indicates what should happen when the data contain \code{NA}s. The default action (\code{na.omit}, inherited from the 'factory fresh' value of \code{getOption("na.action")}) strips any observations with any missing values in any variables.} \item{offset}{this can be used to specify an \emph{a priori} known component to be included in the linear predictor during fitting. This should be \code{NULL} or a numeric vector of length equal to the number of cases. One or more \code{\link{offset}} terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See \code{\link{model.offset}}.} \item{contrasts}{an optional list. See the \code{contrasts.arg} of \code{model.matrix.default}.} \item{devFunOnly}{logical - return only the deviance evaluation function. Note that because the deviance function operates on variables stored in its environment, it may not return \emph{exactly} the same values on subsequent calls (but the results should always be within machine tolerance).} \item{\dots}{other potential arguments. A \code{method} argument was used in earlier versions of the package. Its functionality has been replaced by the \code{REML} argument.} } \value{ An object of class \code{merMod}, for which many methods are available (e.g. \code{methods(class="merMod")}) } \details{ \itemize{ \item{If the \code{formula} argument is specified as a character vector, the function will attempt to coerce it to a formula. However, this is not recommended (users who want to construct formulas by pasting together components are advised to use \code{\link{as.formula}} or \code{\link{reformulate}}); model fits will work but subsequent methods such as \code{\link{drop1}}, \code{\link{update}} may fail.} \item{Unlike some simpler modeling frameworks such as \code{\link{lm}} and \code{\link{glm}} which automatically detect perfectly collinear predictor variables, \code{[gn]lmer} cannot handle design matrices of less than full rank. For example, in cases of models with interactions that have unobserved combinations of levels, it is up to the user to define a new variable (for example creating \code{ab} within the data from the results of \code{interaction(a,b,drop=TRUE)}). } \item{the deviance function returned when \code{devFunOnly} is \code{TRUE} takes a single numeric vector argument, representing the \code{theta} vector. This vector defines the scaled variance-covariance matrices of the random effects, in the Cholesky parameterization. For models with only simple (intercept-only) random effects, \code{theta} is a vector of the standard deviations of the random effects. For more complex or multiple random effects, running \code{getME(.,"theta")} to retrieve the \code{theta} vector for a fitted model and examining the names of the vector is probably the easiest way to determine the correspondence between the elements of the \code{theta} vector and elements of the lower triangles of the Cholesky factors of the random effects.} } } \seealso{ \code{\link[stats]{lm}} } \examples{ ## linear mixed models - reference values from older code (fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy)) summary(fm1)# (with its own print method) fm1_ML <- update(fm1,REML=FALSE) (fm2 <- lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) anova(fm1, fm2) sm2 <- summary(fm2) print(fm2, digits=7, ranef.comp="Var") # the print.merMod() method print(sm2, digits=3, corr=FALSE) # the print.summary.merMod() method (vv <- vcov.merMod(fm2, corr=TRUE)) as(vv, "corMatrix")# extracts the ("hidden") 'correlation' entry in @factors } \keyword{models} lme4/man/Pastes.Rd0000644000176000001440000000536712156422373013464 0ustar ripleyusers\docType{data} \name{Pastes} \alias{Pastes} \title{Paste strength by batch and cask} \format{A data frame with 60 observations on the following 4 variables. \describe{ \item{\code{strength}}{paste strength.} \item{\code{batch}}{delivery batch from which the sample was sample. A factor with 10 levels: \sQuote{A} to \sQuote{J}.} \item{\code{cask}}{cask within the delivery batch from which the sample was chosen. A factor with 3 levels: \sQuote{a} to \sQuote{c}.} \item{\code{sample}}{the sample of paste whose strength was assayed, two assays per sample. A factor with 30 levels: \sQuote{A:a} to \sQuote{J:c}.} }} \source{ O.L. Davies and P.L. Goldsmith (eds), \emph{Statistical Methods in Research and Production, 4th ed.}, Oliver and Boyd, (1972), section 6.5 } \description{ Strength of a chemical paste product; its quality depending on the delivery batch, and the cask within the delivery. } \details{ The data are described in Davies and Goldsmith (1972) as coming from \dQuote{ deliveries of a chemical paste product contained in casks where, in addition to sampling and testing errors, there are variations in quality between deliveries \dots{} As a routine, three casks selected at random from each delivery were sampled and the samples were kept for reference. \dots{} Ten of the delivery batches were sampled at random and two analytical tests carried out on each of the 30 samples}. } \examples{ str(Pastes) dotplot(cask ~ strength | reorder(batch, strength), Pastes, strip = FALSE, strip.left = TRUE, layout = c(1, 10), ylab = "Cask within batch", xlab = "Paste strength", jitter.y = TRUE) ## Modifying the factors to enhance the plot Pastes <- within(Pastes, batch <- reorder(batch, strength)) Pastes <- within(Pastes, sample <- reorder(reorder(sample, strength), as.numeric(batch))) dotplot(sample ~ strength | batch, Pastes, strip = FALSE, strip.left = TRUE, layout = c(1, 10), scales = list(y = list(relation = "free")), ylab = "Sample within batch", xlab = "Paste strength", jitter.y = TRUE) ## Four equivalent models differing only in specification (fm1 <- lmer(strength ~ (1|batch) + (1|sample), Pastes)) (fm2 <- lmer(strength ~ (1|batch/cask), Pastes)) (fm3 <- lmer(strength ~ (1|batch) + (1|batch:cask), Pastes)) (fm4 <- lmer(strength ~ (1|batch/sample), Pastes)) ## fm4 results in redundant labels on the sample:batch interaction head(ranef(fm4)[[1]]) ## compare to fm1 head(ranef(fm1)[[1]]) ## This model is different and NOT appropriate for these data (fm5 <- lmer(strength ~ (1|batch) + (1|cask), Pastes)) L <- getME(fm1, "L") Matrix::image(L, sub = "Structure of random effects interaction in pastes model") } \keyword{datasets} lme4/man/merMod-class.Rd0000644000176000001440000000415112221374125014533 0ustar ripleyusers\docType{class} \name{merMod-class} \title{Class "merMod" of Fitted Mixed-Effect Models} \alias{anova.merMod} \alias{coef.merMod} \alias{deviance.merMod} \alias{fitted.merMod} \alias{formula.merMod} \alias{glmerMod-class} \alias{lmerMod-class} \alias{logLik.merMod} \alias{merMod} \alias{merMod-class} \alias{model.frame.merMod} \alias{model.matrix.merMod} \alias{nlmerMod-class} \alias{print.merMod}%- FIXME: document print.*(), notably 'comps' \alias{print.summary.merMod} \alias{show,merMod-method} \alias{show.merMod} \alias{show.summary.merMod} \alias{summary.merMod} \alias{summary.summary.merMod} \alias{terms.merMod} \alias{update.merMod} \alias{vcov.merMod} \alias{vcov.summary.merMod} \description{ A mixed-effects model is represented as a \code{\linkS4class{merPredD}} object and a response module of a class that inherits from class \code{\linkS4class{lmResp}}. A model with a \code{\linkS4class{lmerResp}} response has class \code{lmerMod}; a \code{\linkS4class{glmResp}} response has class \code{glmerMod}; and a \code{\linkS4class{nlsResp}} response has class \code{nlmerMod}. } \usage{ \S3method{vcov}{merMod}(object, correlation = TRUE, sigm = sigma(object), \dots) } \arguments{ \item{object}{an \R object of class \code{\linkS4class{merMod}}, i.e., as resulting from \code{\link{lmer}()}, or \code{\link{glmer}()}, etc.} \item{correlation}{logical indicating if the correlation matrix is desired in addition to the variance covariance one.} \item{sigm}{the residual standard error; by default \code{\link{sigma}(object)}.} \item{\dots}{potentially further arguments pass from other methods.} } \section{Objects from the Class}{ Objects are created by calls to \code{\link{lmer}}, \code{\link{glmer}} or \code{\link{nlmer}}. } \seealso{ \code{\link{lmer}}, \code{\link{glmer}}, \code{\link{nlmer}}, \code{\linkS4class{merPredD}}, \code{\linkS4class{lmerResp}}, \code{\linkS4class{glmResp}}, \code{\linkS4class{nlsResp}} } \examples{ showClass("merMod") methods(class="merMod")## over 30 (S3) methods available ## -> example(lmer) for an example of vcov.merMod() } \keyword{classes} lme4/man/Penicillin.Rd0000644000176000001440000000435612156422373014310 0ustar ripleyusers\docType{data} \name{Penicillin} \alias{Penicillin} \title{Variation in penicillin testing} \format{A data frame with 144 observations on the following 3 variables. \describe{ \item{\code{diameter}}{diameter (mm) of the zone of inhibition of the growth of the organism.} \item{\code{plate}}{assay plate. A factor with levels \sQuote{a} to \sQuote{x}.} \item{\code{sample}}{penicillin sample. A factor with levels \sQuote{A} to \sQuote{F}.} }} \source{ O.L. Davies and P.L. Goldsmith (eds), \emph{Statistical Methods in Research and Production, 4th ed.}, Oliver and Boyd, (1972), section 6.6 } \description{ Six samples of penicillin were tested using the \emph{B. subtilis} plate method on each of 24 plates. The response is the diameter (mm) of the zone of inhibition of growth of the organism. } \details{ The data are described in Davies and Goldsmith (1972) as coming from an investigation to \dQuote{assess the variability between samples of penicillin by the \emph{B. subtilis} method. I this test method a bulk-innoculated nutrient agar medium is poured into a Petri dish of approximately 90 mm. diameter, known as a plate. When the medium has set, six small hollow cylinders or pots (about 4 mm. in diameter) are cemented onto the surface at equally spaced intervals. A few drops of the penicillin solutions to be compared are placed in the respective cylinders, and the whole plate is placed in an incubator for a given time. Penicillin diffuses from the pots into the agar, and this produces a clear circular zone of inhibition of growth of the organisms, which can be readily measured. The diameter of the zone is related in a known way to the concentration of penicillin in the solution.} } \examples{ str(Penicillin) dotplot(reorder(plate, diameter) ~ diameter, Penicillin, groups = sample, ylab = "Plate", xlab = "Diameter of growth inhibition zone (mm)", type = c("p", "a"), auto.key = list(columns = 3, lines = TRUE, title = "Penicillin sample")) (fm1 <- lmer(diameter ~ (1|plate) + (1|sample), Penicillin)) L <- getME(fm1, "L") Matrix::image(L, main = "L", sub = "Penicillin: Structure of random effects interaction") } \keyword{datasets} lme4/man/mkVarCorr.Rd0000644000176000001440000000114012211706636014113 0ustar ripleyusers\name{mkVarCorr} \title{Make Variance and Correlation Matrices from \code{theta}} \alias{mkVarCorr} \usage{ mkVarCorr(sc, cnms, nc, theta, nms) } \description{ Make variance and correlation matrices from \code{theta} } \arguments{ \item{sc}{scale factor (residual standard deviation).} \item{cnms}{component names.} \item{nc}{numeric vector: number of terms in each RE component.} \item{theta}{theta vector (lower-triangle of Cholesky factors).} \item{nms}{component names (FIXME: nms/cnms redundant: nms=names(cnms)?)} } \value{ A \code{\link{matrix}} } \seealso{ \code{\link{VarCorr}} } lme4/man/refit.Rd0000644000176000001440000000351412232467522013326 0ustar ripleyusers\name{refit} \alias{refit} \alias{refit.merMod} \title{Refit a model with a new response, by maximum likelihood criterion} \usage{ refit(object, newresp, ...) \method{refit}{merMod} (object, newresp = NULL, rename.response=FALSE, ...) } \arguments{ \item{object}{a fitted model, usually of class \code{\linkS4class{lmerMod}}, to be refit with a new response} \item{newresp}{an (optional) numeric vector providing the new response. Must be of the same length as the original response.} \item{rename.response}{when refitting the model, should the name of the response variable in the formula and model frame be replaced with the name of \code{newresp}?} \item{...}{optional additional parameters. None are used at present.} } \value{ an object like \code{x}, but fit by maximum likelihood } \description{ Refit a model with a different response vector } \details{ Refit a model, possibly after modifying the response vector. This could be done using an \code{\link{update}} method but this approach should be faster because it bypasses the creation of the model representation and goes directly to the optimization step. Setting \code{rename.response} to \code{TRUE} may be necessary if one wants to do further operations (such as \code{update}) on the fitted model. However, the refitted model will still be slightly different from the equivalent model fitted via \code{update}; in particular, the \code{terms} component is not updated to reflect the new response variable, if it has a different name from the original. } \examples{ ## using refit() to fit each column in a matrix of responses set.seed(101) Y <- matrix(rnorm(1000),ncol=10) res <- list() d <- data.frame(y=Y[,1],x=rnorm(100),f=rep(1:10,10)) fit1 <- lmer(y~x+(1|f),data=d) res <- c(fit1,lapply(as.data.frame(Y[,-1]), refit,object=fit1)) } lme4/man/nlmer.Rd0000644000176000001440000001142412204271665013331 0ustar ripleyusers\name{nlmer} \alias{nlmer} \title{Fit Nonlinear Mixed-Effects Models} \usage{ nlmer(formula, data = NULL, control = nlmerControl(), start = NULL, verbose = 0L, nAGQ = 1L, subset, weights, na.action, offset, contrasts = NULL, devFunOnly = FALSE, ...) } \arguments{ \item{formula}{a nonlinear mixed model formula (see detailed documentation)} \item{start}{starting estimates for the nonlinear model parameters, as a named numeric vector or as a list with components \describe{ \item{nlpars}{required numeric vector of starting values for the nonlinear model parameters} \item{theta}{optional numeric vector of starting values for the covariance parameters} }} \item{\dots}{other potential arguments. A \code{method} argument was used in earlier versions of the package. Its functionality has been replaced by the \code{nAGQ} argument.} \item{data}{an optional data frame containing the variables named in \code{formula}. By default the variables are taken from the environment from which \code{lmer} is called. While \code{data} is optional, the package authors \emph{strongly} recommend its use, especially when later applying methods such as \code{update} and \code{drop1} to the fitted model (\emph{such methods are not guaranteed to work properly if \code{data} is omitted}). If \code{data} is omitted, variables will be taken from the environment of \code{formula} (if specified as a formula) or from the parent frame (if specified as a character vector).} \item{control}{a list (of correct class, resulting from \code{\link{lmerControl}()} or \code{\link{glmerControl}()} respectively) containing control parameters, including the nonlinear optimizer to be used and parameters to be passed through to the nonlinear optimizer, see the \code{*lmerControl} documentation for details.} \item{verbose}{integer scalar. If \code{> 0} verbose output is generated during the optimization of the parameter estimates. If \code{> 1} verbose output is generated during the individual PIRLS steps.} \item{nAGQ}{integer scalar - the number of points per axis for evaluating the adaptive Gauss-Hermite approximation to the log-likelihood. Defaults to 1, corresponding to the Laplace approximation. Values greater than 1 produce greater accuracy in the evaluation of the log-likelihood at the expense of speed. A value of zero uses a faster but less exact form of parameter estimation for GLMMs by optimizing the random effects and the fixed-effects coefficients in the penalized iteratively reweighted least squares step.} \item{subset}{an optional expression indicating the subset of the rows of \code{data} that should be used in the fit. This can be a logical vector, or a numeric vector indicating which observation numbers are to be included, or a character vector of the row names to be included. All observations are included by default.} \item{weights}{an optional vector of \sQuote{prior weights} to be used in the fitting process. Should be \code{NULL} or a numeric vector.} \item{na.action}{a function that indicates what should happen when the data contain \code{NA}s. The default action (\code{na.omit}, inherited from the 'factory fresh' value of \code{getOption("na.action")}) strips any observations with any missing values in any variables.} \item{offset}{this can be used to specify an \emph{a priori} known component to be included in the linear predictor during fitting. This should be \code{NULL} or a numeric vector of length equal to the number of cases. One or more \code{\link{offset}} terms can be included in the formula instead or as well, and if more than one is specified their sum is used. See \code{\link{model.offset}}.} \item{contrasts}{an optional list. See the \code{contrasts.arg} of \code{model.matrix.default}.} \item{devFunOnly}{logical - return only the deviance evaluation function. Note that because the deviance function operates on variables stored in its environment, it may not return \emph{exactly} the same values on subsequent calls (but the results should always be within machine tolerance).} } \description{ Fit a nonlinear mixed-effects model } \details{ Fit nonlinear mixed-effects models, such as those used in population pharmacokinetics. } \note{ Adaptive Gauss-Hermite quadrature (\code{nAGQ}>1) is not currently implemented for \code{nlmer}. } \examples{ ## nonlinear mixed models --- 3-part formulas --- (nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree, Orange, start = c(Asym = 200, xmid = 725, scal = 350))) (nm1a <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree, Orange, start = c(Asym = 200, xmid = 725, scal = 350), nAGQ = 0L)) } \keyword{models} lme4/man/golden.Rd0000644000176000001440000000136712156422373013471 0ustar ripleyusers\name{golden} \alias{golden} \title{Generator object for the golden search optimizer class.} \usage{ golden(...) } \arguments{ \item{\dots}{additional, optional arguments. None are used at present.} } \description{ The generator objects for the \code{\linkS4class{golden}} class of a scalar optimizer for a parameter within an interval. The optimizer uses reverse communications. } \note{ Arguments to the \code{new} methods must be named arguments. \code{lower} and \code{upper} are the bounds for the scalar parameter; they must be finite. } \section{Methods}{ \describe{ \item{\code{new(lower=lower, upper=upper)}}{Create a new \code{\linkS4class{golden}} object.} } } \seealso{ \code{\linkS4class{golden}} } \keyword{classes} lme4/man/rePos.Rd0000644000176000001440000000134512156422373013305 0ustar ripleyusers\name{rePos} \alias{rePos} \title{Generator object for the rePos (random-effects positions) class} \usage{ rePos(...) } \arguments{ \item{\dots}{Argument list (see Note).} } \description{ The generator object for the \code{\linkS4class{rePos}} class used to determine the positions and orders of random effects associated with particular random-effects terms in the model. } \note{ Arguments to the \code{new} methods must be named arguments. \code{mer}, an object of class \code{"\linkS4class{merMod}"}, is the only required/expected argument. } \section{Methods}{ \describe{ \item{\code{new(mer=mer)}}{Create a new \code{\linkS4class{rePos}} object.} } } \seealso{ \code{\linkS4class{rePos}} } \keyword{classes} lme4/man/glmFamily.Rd0000644000176000001440000000114712156422373014136 0ustar ripleyusers\name{glmFamily} \alias{glmFamily} \title{Generator object for the \code{\linkS4class{glmFamily}} class} \usage{ glmFamily(...) } \arguments{ \item{...}{Named argument (see Note below)} } \description{ The generator object for the \code{\linkS4class{glmFamily}} reference class. Such an object is primarily used through its \code{new} method. } \note{ Arguments to the \code{new} method must be named arguments. } \section{Methods}{ \describe{ \item{\code{new(family=family)}}{Create a new \code{\linkS4class{glmFamily}} object} } } \seealso{ \code{\linkS4class{glmFamily}} } \keyword{classes} lme4/man/nlformula.Rd0000644000176000001440000000344512156422373014217 0ustar ripleyusers\name{nlformula} \alias{nlformula} \title{Manipulate a nonlinear model formula.} \usage{ nlformula(mc) } \arguments{ \item{mc}{matched call from the calling function. Should have arguments named \describe{ \item{formula}{a formula of the form \code{resp ~ nlmod ~ meform} where \code{resp} is an expression for the response, \code{nlmod} is the nonlinear model expression and \code{meform} is the mixed-effects model formula. \code{resp} can be omitted when, e.g., optimizing a design.} \item{data}{a data frame in which to evaluate the model function} \item{start}{either a numeric vector containing initial estimates for the nonlinear model parameters or a list with components \describe{ \item{nlpars}{the initial estimates of the nonlinear model parameters} \item{theta}{the initial estimates of the variance component parameters} } } }} } \value{ a list with components \item{"respMod"}{a response module of class \code{"\linkS4class{nlsResp}"}} \item{"frame"}{the model frame, including a terms attribute} \item{"X"}{the fixed-effects model matrix} \item{"reTrms"}{the random-effects terms object} } \description{ Check and manipulate the formula for a nonlinear model. } \details{ The model formula for a nonlinear mixed-effects model is of the form \code{resp ~ nlmod ~ mixed} where \code{"resp"} is an expression (usually just a name) for the response, \code{nlmod} is the call to the nonlinear model function, and \code{mixed} is the mixed-effects formula defining the linear predictor for the parameter matrix. If the formula is to be used for optimizing designs, the \code{"resp"} part can be omitted. } \seealso{ Other utilities: \code{\link{findbars}}, \code{\link{mkRespMod}}, \code{\link{mkReTrms}}, \code{\link{nobars}}, \code{\link{subbars}} } lme4/man/isREML.Rd0000644000176000001440000000246412156422373013313 0ustar ripleyusers\name{isREML} \alias{isGLMM} \alias{isLMM} \alias{isNLMM} \alias{isREML} \title{Check characteristics of models} \usage{ isREML(x, ...) isLMM(x, ...) isNLMM(x, ...) isGLMM(x, ...) } \arguments{ \item{x}{a fitted model.} \item{...}{additional, optional arguments. (None are used in the merMod methods)} } \value{ a logical value } \description{ Check characteristics of models: whether a model fit corresponds to a linear (LMM), generalized linear (GLMM), or nonlinear (NLMM) mixed model, and whether a linear mixed model has been fitted by REML or not (\code{isREML(x)} is always \code{FALSE} for GLMMs and NLMMs). } \details{ These are generic functions. At present the only methods are for mixed-effects models of class \code{\linkS4class{merMod}}. } \examples{ fm1 <- lmer(Reaction ~ Days + (Days|Subject), sleepstudy) gm1 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), data = cbpp, family = binomial) nm1 <- nlmer(circumference ~ SSlogis(age, Asym, xmid, scal) ~ Asym|Tree, Orange, start = c(Asym = 200, xmid = 725, scal = 350)) isLMM(fm1) isGLMM(gm1) ## check all : is.MM <- function(x) c(LMM = isLMM(x), GLMM= isGLMM(x), NLMM= isNLMM(x)) stopifnot(cbind(is.MM(fm1), is.MM(gm1), is.MM(nm1)) == diag(rep(TRUE,3))) } \seealso{ getME } lme4/man/bootMer.Rd0000644000176000001440000001275312232467515013633 0ustar ripleyusers\name{bootMer} \alias{bootMer} \title{Model-based (Semi-)Parametric Bootstrap for Mixed Models} \usage{ bootMer(x, FUN, nsim = 1, seed = NULL, use.u = FALSE, type = c("parametric", "semiparametric"), verbose = FALSE, .progress = "none", PBargs = list()) } \arguments{ \item{x}{fitted \code{*lmer()} model, see \code{\link{lmer}}, \code{\link{glmer}}, etc.} \item{FUN}{a \code{\link{function}(x)}, computating the \emph{statistic} of interest, which must be a numeric vector, possibly named.} \item{nsim}{number of simulations, positive integer; the bootstrap \eqn{B} (or \eqn{R}).} \item{seed}{optional argument to \code{\link{set.seed}}.} \item{use.u}{logical, indicating whether the spherized random effects should be simulated / bootstrapped as well. If \code{TRUE}, they are not changed, and all inference is conditional on these values. If \code{FALSE}, new normal deviates are drawn (see Details).} \item{type}{character string specifying the type of bootstrap, \code{"parametric"} or \code{"semiparametric"}; partial matching is allowed. Note that the semiparametric bootstrap is currently an experimental feature, and therefore may not be stable.} \item{verbose}{logical indicating if progress should print output} \item{.progress}{character string - type of progress bar to display. Default is \code{"none"}; the function will look for a relevant \code{*ProgressBar} function, so \code{"txt"} will work in general; \code{"tk"} is available if the \code{tcltk} package is loaded; or \code{"win"} on Windows systems.} \item{PBargs}{a list of additional arguments to the progress bar function (the package authors like \code{list(style=3)}).} } \value{ an object of S3 \code{\link{class}} \code{"boot"}, compatible with \pkg{boot} package's \code{\link[boot]{boot}()} result. } \description{ Perform model-based (Semi-)parametric bootstrap for mixed models. } \details{ The semi-parametric variant is not yet implemented, and we only provide a method for \code{\link{lmer}} and \code{\link{glmer}} results. The working name for bootMer() was \dQuote{simulestimate()}, as it is an extension of \code{\link{simulate}}, but we want to emphasize its potential for valid inference. \itemize{ \item If \code{use.u} is \code{FALSE} and \code{type} is \code{"parametric"}, each simulation generates new values of both the \dQuote{\emph{spherized}} random effects \eqn{u} and the i.i.d. errors \eqn{\epsilon}, using \code{\link{rnorm}()} with parameters corresponding to the fitted model \code{x}. \item If \code{use.u} is \code{TRUE} and \code{type=="parametric"}, only the i.i.d. errors (or, for GLMMs, response values drawn from the appropriate distributions) are resampled, with the values of \eqn{u} staying fixed at their estimated values. \item If \code{use.u} is \code{TRUE} and \code{type=="semiparametric"}, the i.i.d. errors are sampled from the distribution of (response) residuals. (For GLMMs, the resulting sample will no longer have the same properties as the original sample, and the method may not make sense; a warning is generated.) Note that the semiparametric bootstrap is currently an experimental feature, and therefore may not be stable. \item The case where \code{use.u} is \code{FALSE} and \code{type=="semiparametric"} is not implemented; Morris (2002) suggests that resampling from the estimated values of \eqn{u} is not good practice. } } \examples{ fm01ML <- lmer(Yield ~ 1|Batch, Dyestuff, REML = FALSE) ## see ?"profile-methods" mySumm <- function(.) { s <- sigma(.) c(beta =getME(., "beta"), sigma = s, sig01 = unname(s * getME(., "theta"))) } (t0 <- mySumm(fm01ML)) # just three parameters ## alternatively: mySumm2 <- function(.) { c(beta=fixef(.),sigma=sigma(.),sig01=unlist(VarCorr(.))) } set.seed(101) ## 3.8s (on a 5600 MIPS 64bit fast(year 2009) desktop "AMD Phenom(tm) II X4 925"): system.time( boo01 <- bootMer(fm01ML, mySumm, nsim = 100) ) ## to "look" at it require("boot") ## a recommended package, i.e. *must* be there boo01 ## note large estimated bias for sig01 ## (~30\% low, decreases _slightly_ for nsim = 1000) ## extract the bootstrapped values as a data frame ... head(as.data.frame(boo01)) ## ------ Bootstrap-based confidence intervals ------------ ## intercept (bCI.1 <- boot.ci(boo01, index=1, type=c("norm", "basic", "perc")))# beta ## Residual standard deviation - original scale: (bCI.2 <- boot.ci(boo01, index=2, type=c("norm", "basic", "perc"))) ## Residual SD - transform to log scale: (bCI.2l <- boot.ci(boo01, index=2, type=c("norm", "basic", "perc"), h = log, hdot = function(.) 1/., hinv = exp)) ## Among-batch variance: (bCI.3 <- boot.ci(boo01, index=3, type=c("norm", "basic", "perc")))# sig01 ## Graphical examination: plot(boo01,index=3) ## Check stored values from a longer (1000-replicate) run: load(system.file("testdata","boo01L.RData",package="lme4")) plot(boo01L,index=3) } \references{ Davison, A.C. and Hinkley, D.V. (1997) \emph{Bootstrap Methods and Their Application}. Cambridge University Press. Morris, J. S. (2002). The BLUPs Are Not \sQuote{best} When It Comes to Bootstrapping. \emph{Statistics & Probability Letters} \bold{56}(4): 425--430. doi:10.1016/S0167-7152(02)00041-X. } \seealso{ \itemize{ \item For inference, including confidence intervals, \code{\link{profile-methods}}. \item \code{\link[boot]{boot}()}, and then \code{\link[boot]{boot.ci}} from package \pkg{boot}. } } \keyword{htest} \keyword{models} lme4/man/fixef.Rd0000644000176000001440000000126312156422373013315 0ustar ripleyusers\docType{methods} \name{fixef} \alias{fixed.effects} \alias{fixef} \alias{fixef.merMod} \title{Extract fixed-effects estimates} \usage{ \method{fixef}{merMod} (object, ...) } \arguments{ \item{object}{any fitted model object from which fixed effects estimates can be extracted.} \item{\dots}{optional additional arguments. Currently none are used in any methods.} } \value{ a named, numeric vector of fixed-effects estimates. } \description{ Extract the fixed-effects estimates } \details{ Extract the estimates of the fixed-effects parameters from a fitted model. } \examples{ fixef(lmer(Reaction ~ Days + (1|Subject) + (0+Days|Subject), sleepstudy)) } \keyword{models} lme4/man/lmerControl.Rd0000644000176000001440000001333112232467515014515 0ustar ripleyusers\name{lmerControl} \title{Control of Mixed Model Fitting} \alias{glmerControl} \alias{lmerControl} \alias{nlmerControl} \description{ Construct a control structure for mixed model fitting. } \usage{ lmerControl(optimizer = "Nelder_Mead", restart_edge = TRUE, sparseX = FALSE, check.nobs.vs.rankZ = "warningSmall", check.nobs.vs.nlev = "stop", check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", optCtrl = list()) glmerControl(optimizer = c("bobyqa", "Nelder_Mead"), restart_edge = FALSE, sparseX = FALSE, check.nobs.vs.rankZ = "warningSmall", check.nobs.vs.nlev = "stop", check.nlev.gtreq.5 = "ignore", check.nlev.gtr.1 = "stop", tolPwrss = 1e-07, compDev = TRUE, optCtrl = list()) nlmerControl(optimizer = "Nelder_Mead", tolPwrss = 1e-10, optCtrl = list()) } \arguments{ \item{optimizer}{character - name of optimizing function(s). A character vector or list of functions: length 1 for \code{lmer} or \code{glmer}, possibly length 2 for \code{glmer}). The built-in optimizers are \code{\link{Nelder_Mead}} and \code{\link[minqa]{bobyqa}} (from the \pkg{minqa} package). Any minimizing function that allows box constraints can be used provided that it \describe{ \item{(1)}{takes input parameters \code{fn} (function to be optimized), \code{par} (starting parameter values), \code{lower} (lower bounds) and \code{control} (control parameters, passed through from the \code{control} argument) and} \item{(2)}{returns a list with (at least) elements \code{par} (best-fit parameters), \code{fval} (best-fit function value), \code{conv} (convergence code) and (optionally) \code{message} (informational message, or explanation of convergence failure).} } Special provisions are made for \code{\link{bobyqa}}, \code{\link{Nelder_Mead}}, and optimizers wrapped in the \pkg{optimx} package; to use \pkg{optimx} optimizers (including \code{L-BFGS-B} from base \code{\link{optim}} and \code{\link{nlminb}}), pass the \code{method} argument to \code{optim} in the \code{control} argument. For \code{glmer}, if \code{length(optimizer)==2}, the first element will be used for the preliminary (random effects parameters only) optimization, while the second will be used for the final (random effects plus fixed effect parameters) phase. See \code{\link{modular}} for more information on these two phases. } \item{sparseX}{logical - should a sparse model matrix be used for the fixed-effects terms? Defaults to \code{FALSE}. Currently inactive.} \item{restart_edge}{logical - should the optimizer attempt a restart when it finds a solution at the boundary (i.e. zero random-effect variances or perfect +/-1 correlations)?} \item{check.nlev.gtreq.5}{character - rules for checking whether all random effects have >= 5 levels. "ignore": skip the test. "warning": warn if test fails. "stop": throw an error if test fails.} \item{check.nlev.gtr.1}{character - rules for checking whether all random effects have > 1 level. As for \code{check.nlevel.gtr.5}.} \item{check.nobs.vs.rankZ}{character - rules for checking whether the number of observations is greater than (or greater than or equal to) the rank of the random effects design matrix (Z), usually necessary for identifiable variances. As for \code{check.nlevel.gtreq.5}, with the addition of \code{"warningSmall"} and \code{"stopSmall"}, which run the test only if the dimensions of \code{Z} are < 1e6. \code{nobs > rank(Z)} will be tested for LMMs and GLMMs with estimated scale parameters; \code{nobs >= rank(Z)} will be tested for GLMMs with fixed scale parameter.} \item{check.nobs.vs.nlev}{ character - rules for checking whether the number of observations is less than (or less than or equal to) the number of levels of every grouping factor, usually necessary for identifiable variances. As for \code{check.nlevel.gtreq.5}. \code{nobs ../tests/glmer-1.R ## GLMM with individual-level variability (accounting for overdispersion) cbpp$obs <- 1:nrow(cbpp) (m2 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd) + (1|obs), family = binomial, data = cbpp)) } \keyword{datasets} lme4/man/nobars.Rd0000644000176000001440000000162312156422373013500 0ustar ripleyusers\name{nobars} \alias{nobars} \title{Omit terms separated by vertical bars in a formula} \usage{ nobars(term) } \arguments{ \item{term}{the right-hand side of a mixed-model formula} } \value{ the fixed-effects part of the formula } \description{ Remove the random-effects terms from a mixed-effects formula, thereby producing the fixed-effects formula. } \section{Note}{ This function is called recursively on individual terms in the model, which is why the argument is called \code{term} and not a name like \code{form}, indicating a formula. } \examples{ nobars(Reaction ~ Days + (Days|Subject)) ## => Reaction ~ Days } \seealso{ \code{\link{formula}}, \code{\link{model.frame}}, \code{\link{model.matrix}}. Other utilities: \code{\link{findbars}}, \code{\link{mkRespMod}}, \code{\link{mkReTrms}}, \code{\link{nlformula}}, \code{\link{subbars}} } \keyword{models} \keyword{utilities} lme4/man/cake.Rd0000644000176000001440000000423212156422373013116 0ustar ripleyusers\docType{data} \name{cake} \alias{cake} \title{Breakage angle of chocolate cakes} \format{A data frame with 270 observations on the following 5 variables. \describe{ \item{\code{replicate}}{a factor with levels \code{1} to \code{15}} \item{\code{recipe}}{a factor with levels \code{A}, \code{B} and \code{C}} \item{\code{temperature}}{an ordered factor with levels \code{175} < \code{185} < \code{195} < \code{205} < \code{215} < \code{225}} \item{\code{angle}}{a numeric vector giving the angle at which the cake broke.} \item{\code{temp}}{numeric value of the baking temperature (degrees F).} }} \source{ Original data were presented in Cook (1938), and reported in Cochran and Cox (1957, p. 300). Also cited in Lee, Nelder and Pawitan (2006). } \description{ Data on the breakage angle of chocolate cakes made with three different recipes and baked at six different temperatures. This is a split-plot design with the recipes being whole-units and the different temperatures being applied to sub-units (within replicates). The experimental notes suggest that the replicate numbering represents temporal ordering. } \details{ The \code{replicate} factor is nested within the \code{recipe} factor, and \code{temperature} is nested within \code{replicate}. } \examples{ str(cake) ## 'temp' is continuous, 'temperature' an ordered factor with 6 levels fm1 <- lmer(angle ~ recipe * temperature + (1|recipe:replicate), cake, REML= FALSE) print(fm1, corr=FALSE) fm2 <- lmer(angle ~ recipe + temperature + (1|recipe:replicate), cake, REML= FALSE) print(fm2, corr=FALSE) fm3 <- lmer(angle ~ recipe + temp + (1|recipe:replicate), cake, REML= FALSE) fm3 ## and now "choose" : anova(fm3, fm2, fm1) } \references{ Cook, F. E. (1938) \emph{Chocolate cake, I. Optimum baking temperature}. Master's Thesis, Iowa State College. Cochran, W. G., and Cox, G. M. (1957) \emph{Experimental designs}, 2nd Ed. New York, John Wiley \& Sons. Lee, Y., Nelder, J. A., and Pawitan, Y. (2006) \emph{Generalized linear models with random effects. Unified analysis via H-likelihood}. Boca Raton, Chapman and Hall/CRC. } \keyword{datasets}