pvclust/0000755000175100001440000000000012612441324011767 5ustar hornikuserspvclust/NAMESPACE0000644000175100001440000000125512612414703013212 0ustar hornikusersexport(pvclust) export(pvpick) export(msfit) export(msplot) export(pvrect) export(seplot) export(parPvclust) S3method(plot, pvclust) S3method(lines, pvclust) S3method(text, pvclust) S3method(print, pvclust) S3method(summary, pvclust) S3method(plot, msfit) S3method(lines, msfit) S3method(summary, msfit) importFrom("grDevices", "n2mfrow") importFrom("graphics", "curve", "lines", "par", "plot", "rect", "segments", "text") importFrom("stats", "as.dist", "cor", "dist", "dnorm", "hclust", "lsfit", "na.omit", "pchisq", "pnorm", "qnorm", "rmultinom") importFrom("utils", "capture.output", "head", "packageVersion") pvclust/data/0000755000175100001440000000000012612414703012701 5ustar hornikuserspvclust/data/lung.RData0000644000175100001440000025246612612414703014602 0ustar hornikusersBZh91AY&SYSpc|D1!$?x*{9x Q"*3P5P=O@fm4jt+T*Qc%,[Z( M nw吥U%7pE%JIE BN5Z@ (( )!TSAB+153gnJR!TmqҊQ*6[ 5uWj6V/ Yۀ((H(wTH$'-5{⊪}Jiǀ=(LLmXF4Ybՙ{(7}ْJ@)S)TX*JK *F{ ccJQ^m^ )h%HTIRU24h4ODDE@ A@#`?C!"$B@'zxCP?B"E)il4d=M m=6Pjd @P$Jhh=A6S h@2!@ 4M MM<ҍ6=!4y4Lڂ hd=@i#&MĄ {ݾQ-KRή o{kkfo/RW4X"K0ѭsUWk2r,z+] t*IvmDH͵ҵR$el`CȄ1K4ʍ®JȒ0Qi:N{lmPP%ֶ+IQp˥==]N5B^iWV^:=]&7⼊-xiTu3RBAQU2'liAfvk{F b&Pz՞Ӈy~G!!Ăv'Xc1s<{A٫ae5qg6]A1\d*2~ $HI) POL(*4e2&DD.) 3BF&E&"32*Q&$T$D&Ic;wi$,d0֧̒`f$)Q c`fns%$%&f1a0Q„HA$ن30S(ɀ&`  e ڭ"c3&"VK#H6 PJI&vti&`̈3` m6ddD#IaĠEtf)K!A3"XhؚSbQ!E!(S"H2 &,"C 3("A!1) RY dP@PBTيm !)#1E"HHI&f( DDPdML (L$I!$Y L b"4aĒ@1bvڬHR1L E%H%E0R&ebHfle&`da)(E$`1$0Z"1b&@"uu\)&aL]#E$eȲRIB``֯WdMmmU>xUdZTxT&A1jK-J7ߩP.*HŦXRS_ H֫ LMң%G^8QÏ$aAd70Iֳ2x w P"d䡗kHШUե t)\t{6v11))) 6YvejۏpQV.o臌}<+Mvaa]͎[}3ee7wG;ݴ]Z:7`gY?awI8G4iȔ֬٨ņcRH>m٢G;UbMѐnN 9"}[Vѣ-PU9<*jH"Ib9v-'˻$ld!ueZ__%/i6R1)S[Uw`Zə%cMqtk1&` o*5O7?wDZߝVw,\_dk`=&UJ, T}#Hi҃WXhӳІ%d)>(~?Aqh}]{E*`|V/xP[zTe|\~ySJkD5 pTUN;mH_X܊*7Sӡu-n6k"cE m y=ş s+yͷ]D!ܸ1 ;tBIw<&$vRj){xf;.(i;$@Ic ̅&5[+Ʌ5oYjI%eʪANZ+M~eR~T~8g| sϙ\jt<)}ٕ8$+]*6:6:i Z4"eNϓO3w}́Twˬ'N\aэtZ&(;߱=p;roݵds;LNϝ䌃פֿWfF!ŵ0 "A^r{P"3ZeUVqa Gʩ8yJ/,YéVs4O խ;$PdpKl5޻-^*V'~[GL=Rĺ/nb&l)KA=e&iwm<[횭i<4Ikdži ,Y wTM1bIAe}iX匕5vk5Mt>qA9F֝ƻ^;E ب(0]] 3f ^r|BdbKrB,w24j q ٣8z&A䔛]VK%Xo]= '[wH/thF@.̏baPH̺mbۊDaqoWZ(I9}L}y񇊴j TA$cy &?4uo JhEJ nD4`e9)H[Q6(; Q1 h*+j[r( SO݉ܮmO Dr? 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j <- 1 if(is.na(pm <- pmatch(type, c("geq", "leq", "gt", "lt")))) stop("Invalid type argument: see help(pickup)") for(i in (len - 1):1) { if (pm==1) wh <- (x$edges[i,pv] >= alpha) # Greater than or Equals else if(pm==2) wh <- (x$edges[i,pv] <= alpha) # Lower than or Equals else if(pm==3) wh <- (x$edges[i,pv] > alpha) # Greater Than else if(pm==4) wh <- (x$edges[i,pv] < alpha) # Lower Than if(wh) { mi <- member[[i]] ma <- match(mi, order) if(max.only == FALSE || (max.only && sum(match(ma, ht, nomatch=0)) == 0)) { a$clusters[[j]] <- x$hclust$labels[mi] a$edges <- c(a$edges,i) j <- j + 1 } ht <- c(ht, ma) } } a$edges <- a$edges[length(a$edges):1] a$clusters <- a$clusters[length(a$edges):1] return(a) } msfit <- function(bp, r, nboot) { if(length(bp) != length(r)) stop("bp and r should have the same length") nboot <- rep(nboot, length=length(bp)) use <- bp > 0 & bp < 1 p <- se <- c(0,0); names(p) <- names(se) <- c("au", "bp") coef <- c(0,0); names(coef) <- c("v", "c") a <- list(p=p, se=se, coef=coef, df=0, rss=0, pchi=0); class(a) <- "msfit" if(sum(use) < 2) { # if(mean(bp) < .5) a$p[] <- c(0, 0) else a$p[] <- c(1, 1) if(mean(bp) < .5) a$p[] <- c(0, bp[r==1.0]) else a$p[] <- c(1, bp[r==1.0]) return(a) } bp <- bp[use]; r <- r[use]; nboot <- nboot[use] zz <- -qnorm(bp) vv <- ((1 - bp) * bp) / (dnorm(zz)^2 * nboot) a$use <- use; a$r <- r; a$zz <- zz X <- cbind(sqrt(r), 1/sqrt(r)); dimnames(X) <- list(NULL, c("v","c")) fit <- lsfit(X, zz, 1/vv, intercept=FALSE) a$coef <- coef <- fit$coef h.au <- c(1, -1); h.bp <- c(1, 1) z.au <- drop(h.au %*% coef); z.bp <- drop(h.bp %*% coef) a$p["au"] <- pnorm(-z.au); a$p["bp"] <- pnorm(-z.bp) V <- solve(crossprod(X, X/vv)) vz.au <- drop(h.au %*% V %*% h.au); vz.bp <- drop(h.bp %*% V %*% h.bp) a$se["au"] <- dnorm(z.au) * sqrt(vz.au); a$se["bp"] <- dnorm(z.bp) * sqrt(vz.bp) a$rss <- sum(fit$residual^2/vv) if((a$df <- sum(use) - 2) > 0) { a$pchi <- pchisq(a$rss, lower.tail=FALSE, df=a$df) } else a$pchi <- 1.0 return(a) } plot.msfit <- function(x, curve=TRUE, main=NULL, sub=NULL, xlab=NULL, ylab=NULL, ...) { if(is.null(main)) main="Curve fitting for multiscale bootstrap resampling" if(is.null(sub)) { sub <- paste("AU = ", round(x$p["au"], digits=2), ", BP = ", round(x$p["bp"], digits=2), ", v = ", round(x$coef["v"], digits=2), ", c = ", round(x$coef["c"], digits=2), ", pchi = ", round(x$pchi, digits=2)) } if(is.null(xlab)) xlab=expression(sqrt(r)) if(is.null(ylab)) ylab=expression(z-value) a <- sqrt(x$r); b <- x$zz if(!is.null(a) && !is.null(b)) { plot(a, b, main=main, sub=sub, xlab=xlab, ylab=ylab, ...) if(curve) lines(x, ...) } else if (!is.null(a)){ plot(0, 0, main=main, sub=sub, xlab=xlab, ylab=ylab, type="n", xaxt="n", yaxt="n", ...) a <- text(mean(a), 0, "No fitting") } } lines.msfit <- function(x, col=2, lty=1, ...) { v <- x$coef["v"]; c <- x$coef["c"] curve(v * x + c / x, add=TRUE, col=col, lty=lty) } summary.msfit <- function(object, digits=3, ...) { cat("\nResult of curve fitting for multiscale bootstrap resampling:\n\n") cat("Estimated p-values:\n") pv <- data.frame(object$p, object$se) names(pv) <- c("Estimate", "Std. Error"); row.names(pv) <- c("au", "bp") print(pv, digits=digits); cat("\n") cat("Estimated coefficients:\n") coef <- object$coef print(coef, digits=digits); cat("\n") cat(paste("Residual sum of squares: ", round(object$rss,digits=digits)), ", p-value: ", round(object$pchi, digits=digits), " on ", object$df, " DF\n\n", sep="") } seplot <- function(object, type=c("au", "bp"), identify=FALSE, main=NULL, xlab=NULL, ylab=NULL, ...) { if(!is.na(pm <- pmatch(type[1], c("au", "bp")))) { wh <- c("au", "bp")[pm] if(is.null(main)) main <- "p-value vs standard error plot" if(is.null(xlab)) xlab <- c("AU p-value", "BP value")[pm] if(is.null(ylab)) ylab <- "Standard Error" plot(object$edges[,wh], object$edges[,paste("se", wh, sep=".")], main=main, xlab=xlab, ylab=ylab, ...) if(identify) identify(x=object$edges[,wh], y=object$edges[,paste("se", wh, sep=".")], labels=row.names(object$edges)) } else stop("'type' should be \"au\" or \"bp\".") } pvclust/R/pvclust-internal.R0000644000175100001440000003327212612414703015635 0ustar hornikusers### internal function for non-parallel pvclust pvclust.nonparallel <- function(data, method.hclust, method.dist, use.cor, nboot, r, store, weight, iseed, quiet) { # initialize random seed if(!is.null(iseed)) set.seed(seed = iseed) # data: (n,p) matrix, n-samples, p-variables n <- nrow(data); p <- ncol(data) # hclust for original data # METHODS <- c("ward", "single", "complete", "average", "mcquitty", # "median", "centroid") # method.hclust <- METHODS[pmatch(method.hclust, METHODS)] # Use custom distance function if(is.function(method.dist)) { distance <- method.dist(data) } else { distance <- dist.pvclust(data, method=method.dist, use.cor=use.cor) } data.hclust <- hclust(distance, method=method.hclust) # ward -> ward.D # only if R >= 3.1.0 if(method.hclust == "ward" && getRversion() >= '3.1.0') { method.hclust <- "ward.D" } # multiscale bootstrap size <- floor(n*r) rl <- length(size) if(rl == 1) { if(r != 1.0) warning("Relative sample size r is set to 1.0. AU p-values are not calculated\n") r <- list(1.0) } else r <- as.list(size/n) mboot <- lapply(r, boot.hclust, data=data, object.hclust=data.hclust, nboot=nboot, method.dist=method.dist, use.cor=use.cor, method.hclust=method.hclust, store=store, weight=weight, quiet=quiet) result <- pvclust.merge(data=data, object.hclust=data.hclust, mboot=mboot) return(result) } ### internal function for parallel pvclust pvclust.parallel <- function(cl, data, method.hclust, method.dist, use.cor, nboot, r, store, weight, init.rand=NULL, iseed, quiet, parallel.check) { if(parallel.check) { check.result <- check.parallel(cl=cl, nboot=nboot) if(!check.result) { msg <- paste(attr(check.result, "msg"), ". non-parallel version is executed", sep = "") warning(msg) return(pvclust.nonparallel(data=data, method.hclust=method.hclust, method.dist=method.dist, use.cor=use.cor, nboot=nboot, r=r, store=store, weight=weight, iseed=iseed, quiet=quiet)) } } # check package versions pkg.ver <-parallel::clusterCall(cl, packageVersion, pkg = "pvclust") r.ver <- parallel::clusterCall(cl, getRversion) if(length(unique(pkg.ver)) > 1 || length(unique(r.ver)) > 1) { node.name <- parallel::clusterEvalQ(cl, Sys.info()["nodename"]) version.table <- data.frame( node=seq_len(length(node.name)), name=unlist(node.name), R=unlist(lapply(r.ver, as.character)), pvclust=unlist(lapply(pkg.ver, as.character))) if(nrow(version.table) > 10) table.out <- c(capture.output(print(head(version.table, n=10), row.names=FALSE)), " ...") else table.out <- capture.output(print(version.table, row.names=FALSE)) warning("R/pvclust versions are not unique:\n", paste(table.out, collapse="\n")) } if(!is.null(init.rand)) warning("\"init.rand\" option is deprecated. It is available for back compatibility but will be unavailable in the future.\nSpecify a non-NULL value of \"iseed\" to initialize random seed.") # if(init.rand) { # if(is.null(iseed) && !is.null(seed)) { # warning("\"seed\" option is deprecated. It is available for back compatibility but will be unavailable in the future.\nConsider using \"iseed\" instead.") # # if(length(seed) != length(cl)) # stop("seed and cl should have the same length.") # # # setting random seeds # parallel::parLapply(cl, as.list(seed), set.seed) # } else { # parallel::clusterSetRNGStream(cl = cl, iseed = iseed) # } # } if(!is.null(iseed) && (is.null(init.rand) || init.rand)) parallel::clusterSetRNGStream(cl = cl, iseed = iseed) # data: (n,p) matrix, n-samples, p-variables n <- nrow(data); p <- ncol(data) # hclust for original data if(is.function(method.dist)) { # Use custom distance function distance <- method.dist(data) } else { distance <- dist.pvclust(data, method=method.dist, use.cor=use.cor) } data.hclust <- hclust(distance, method=method.hclust) # ward -> ward.D # only if R >= 3.1.0 if(method.hclust == "ward" && getRversion() >= '3.1.0') { method.hclust <- "ward.D" } # multiscale bootstrap size <- floor(n*r) rl <- length(size) if(rl == 1) { if(r != 1.0) warning("Relative sample size r is set to 1.0. AU p-values are not calculated\n") r <- list(1.0) } else r <- as.list(size/n) ncl <- length(cl) nbl <- as.list(rep(nboot %/% ncl,times=ncl)) if((rem <- nboot %% ncl) > 0) nbl[1:rem] <- lapply(nbl[1:rem], "+", 1) if(!quiet) cat("Multiscale bootstrap... ") mlist <- parallel::parLapply(cl, nbl, pvclust.node, r=r, data=data, object.hclust=data.hclust, method.dist=method.dist, use.cor=use.cor, method.hclust=method.hclust, store=store, weight=weight, quiet=quiet) if(!quiet) cat("Done.\n") mboot <- mlist[[1]] for(i in 2:ncl) { for(j in 1:rl) { mboot[[j]]$edges.cnt <- mboot[[j]]$edges.cnt + mlist[[i]][[j]]$edges.cnt mboot[[j]]$nboot <- mboot[[j]]$nboot + mlist[[i]][[j]]$nboot mboot[[j]]$store <- c(mboot[[j]]$store, mlist[[i]][[j]]$store) } } result <- pvclust.merge( data=data, object.hclust=data.hclust, mboot=mboot) return(result) } hc2axes <- function(x) { A <- x$merge # (n,n-1) matrix n <- nrow(A) + 1 x.axis <- c() y.axis <- x$height x.tmp <- rep(0,2) zz <- match(1:length(x$order),x$order) for(i in 1:(n-1)) { ai <- A[i,1] if(ai < 0) x.tmp[1] <- zz[-ai] else x.tmp[1] <- x.axis[ai] ai <- A[i,2] if(ai < 0) x.tmp[2] <- zz[-ai] else x.tmp[2] <- x.axis[ai] x.axis[i] <- mean(x.tmp) } return(data.frame(x.axis=x.axis,y.axis=y.axis)) } hc2split <- function(x) { A <- x$merge # (n-1,n) matrix n <- nrow(A) + 1 B <- list() for(i in 1:(n-1)){ ai <- A[i,1] if(ai < 0) B[[i]] <- -ai else B[[i]] <- B[[ai]] ai <- A[i,2] if(ai < 0) B[[i]] <- sort(c(B[[i]],-ai)) else B[[i]] <- sort(c(B[[i]],B[[ai]])) } CC <- matrix(rep(0,n*(n-1)),nrow=(n-1),ncol=n) for(i in 1:(n-1)){ bi <- B[[i]] m <- length(bi) for(j in 1:m) CC[i,bi[j]] <- 1 } split <- list(pattern=apply(CC,1,paste,collapse=""), member=B) return(split) } pvclust.node <- function(x, r, ...) { # require(pvclust) mboot.node <- lapply(r, boot.hclust, nboot=x, ...) return(mboot.node) } boot.hclust <- function(r, data, object.hclust, method.dist, use.cor, method.hclust, nboot, store, weight=FALSE, quiet=FALSE) { n <- nrow(data) size <- round(n*r, digits=0) if(size == 0) stop("invalid scale parameter(r)") r <- size/n pattern <- hc2split(object.hclust)$pattern edges.cnt <- table(factor(pattern)) - table(factor(pattern)) st <- list() # bootstrap start rp <- as.character(round(r,digits=2)); if(r == 1) rp <- paste(rp,".0",sep="") if(!quiet) cat(paste("Bootstrap (r = ", rp, ")... ", sep="")) w0 <- rep(1,n) # equal weight na.flag <- 0 for(i in 1:nboot){ if(weight && r>10) { ## <- this part should be improved w1 <- as.vector(rmultinom(1,size,w0)) # resampled weight suppressWarnings(distance <- distw.pvclust(data,w1,method=method.dist,use.cor=use.cor)) } else { smpl <- sample(1:n, size, replace=TRUE) if(is.function(method.dist)) { suppressWarnings(distance <- method.dist(data[smpl,])) } else { suppressWarnings(distance <- dist.pvclust(data[smpl,],method=method.dist,use.cor=use.cor)) } } if(all(is.finite(distance))) { # check if distance is valid x.hclust <- hclust(distance,method=method.hclust) pattern.i <- hc2split(x.hclust)$pattern # split edges.cnt <- edges.cnt + table(factor(pattern.i, levels=pattern)) } else { x.hclust <- NULL na.flag <- 1 } if(store) st[[i]] <- x.hclust } if(!quiet) cat("Done.\n") # bootstrap done if(na.flag == 1) warning(paste("inappropriate distance matrices are omitted in computation: r = ", r), call.=FALSE) boot <- list(edges.cnt=edges.cnt, method.dist=method.dist, use.cor=use.cor, method.hclust=method.hclust, nboot=nboot, size=size, r=r, store=st) class(boot) <- "boot.hclust" return(boot) } pvclust.merge <- function(data, object.hclust, mboot){ pattern <- hc2split(object.hclust)$pattern r <- unlist(lapply(mboot,"[[","r")) nboot <- unlist(lapply(mboot,"[[","nboot")) store <- lapply(mboot,"[[", "store") rl <- length(mboot) ne <- length(pattern) edges.bp <- edges.cnt <- data.frame(matrix(rep(0,ne*rl),nrow=ne,ncol=rl)) row.names(edges.bp) <- pattern names(edges.cnt) <- paste("r", 1:rl, sep="") for(j in 1:rl) { edges.cnt[,j] <- as.vector(mboot[[j]]$edges.cnt) edges.bp[,j] <- edges.cnt[,j] / nboot[j] } ms.fitted <- lapply(as.list(1:ne), function(x, edges.bp, r, nboot){ msfit(as.vector(t(edges.bp[x,])), r, nboot)}, edges.bp, r, nboot) class(ms.fitted) <- "mslist" p <- lapply(ms.fitted,"[[","p") se <- lapply(ms.fitted,"[[","se") coef <- lapply(ms.fitted,"[[","coef") au <- unlist(lapply(p,"[[","au")) bp <- unlist(lapply(p,"[[","bp")) se.au <- unlist(lapply(se,"[[","au")) se.bp <- unlist(lapply(se,"[[","bp")) v <- unlist(lapply(coef,"[[","v")) cc <- unlist(lapply(coef,"[[","c")) pchi <- unlist(lapply(ms.fitted,"[[","pchi")) edges.pv <- data.frame(au=au, bp=bp, se.au=se.au, se.bp=se.bp, v=v, c=cc, pchi=pchi) row.names(edges.pv) <- row.names(edges.cnt) <- 1:ne result <- list(hclust=object.hclust, edges=edges.pv, count=edges.cnt, msfit=ms.fitted, nboot=nboot, r=r, store=store) class(result) <- "pvclust" return(result) } dist.pvclust <- function(x, method="euclidean", use.cor="pairwise.complete.obs") { if(!is.na(pmatch(method,"correlation"))){ res <- as.dist(1 - cor(x, method="pearson", use=use.cor)) attr(res,"method") <- "correlation" return(res) } else if(!is.na(pmatch(method,"abscor"))){ res <- as.dist(1 - abs(cor(x,method="pearson",use=use.cor))) attr(res,"method") <- "abscor" return(res) } else if(!is.na(pmatch(method,"uncentered"))){ if(sum(is.na(x)) > 0){ x <- na.omit(x) warning("Rows including NAs were omitted") } x <- as.matrix(x) P <- crossprod(x) qq <- matrix(diag(P),ncol=ncol(P)) Q <- sqrt(crossprod(qq)) res <- as.dist(1 - P/Q) attr(res,"method") <- "uncentered" return(res) } else dist(t(x),method) } corw <- function(x,w, use=c("all.obs","complete.obs","pairwise.complete.obs") ) { if(is.data.frame(x)) x <- as.matrix(x) x <- x[w>0,,drop=F] w <- w[w>0] n <- nrow(x) # sample size m <- ncol(x) # number of variables if(missing(w)) w <- rep(1,n) r <- matrix(0,m,m,dimnames=list(colnames(x),colnames(x))) diag(r) <- 1 use <- match.arg(use) pairu <- F if(use=="all.obs") { u <- rep(T,n) } else if(use=="complete.obs") { u <- apply(x,1,function(y) !any(is.na(y))) } else if(use=="pairwise.complete.obs") { pairu <- T ux <- is.finite(x) } else stop("unknown use") for(i in 1+seq(length=m-1)) { for(j in seq(length=i-1)) { if(pairu) u <- ux[,i] & ux[,j] wu <- w[u]; xi <- x[u,i]; xj <- x[u,j] ws <- sum(wu) if(ws > 1e-8) { xi <- xi - sum(wu*xi)/ws xj <- xj - sum(wu*xj)/ws vxi <- sum(wu*xi*xi)/ws vxj <- sum(wu*xj*xj)/ws if(min(vxi,vxj) > 1e-8) { vxij <- sum(wu*xi*xj)/ws rij <- vxij/sqrt(vxi*vxj) } else { rij <- 0 } } else { rij <- 0 } r[i,j] <- r[j,i] <- rij } } r } ### calculate distance by weight distw.pvclust <- function(x,w,method="correlation", use.cor="pairwise.complete.obs") { if(!is.na(pmatch(method,"correlation"))){ res <- as.dist(1 - corw(x,w, use=use.cor)) attr(res,"method") <- "correlation" return(res) } else if(!is.na(pmatch(method,"abscor"))){ res <- as.dist(1 - abs(corw(x,w, use=use.cor))) attr(res,"method") <- "abscor" return(res) } stop("wrong method") } ### check whether parallel computation is appropriate check.parallel <- function(cl, nboot) { res <- FALSE ### will be used when defaultCluster(cl) becomes publicly available # # check whether cl is a cluster, or a default cluster is available # if(!inherits(cl, "cluster")) { # try_result <- try(cl <- parallel:::defaultCluster(cl), silent=TRUE) # if(class(try_result) == "try-error") { # attr(res, "msg" <- "cl is not a cluster") # return(res) # } # } ncl <- length(cl) if(ncl < 2) { attr(res, "msg") <- "Cluster size is too small (or NULL)" } else if (ncl > nboot) { attr(res, "msg") <- "nboot is too small for cluster size" } else { res <- TRUE } return(res) }pvclust/MD50000644000175100001440000000117412612441324012302 0ustar hornikusersa8cfdf20f013a1782f62540de8c31cc7 *DESCRIPTION 5a968cf1c9f480e1f6ddf26248f54b56 *NAMESPACE 7a00ed1667f341eaa681f8d9d3d058d9 *R/pvclust-internal.R 75d16c5ca32ecd0a133b5764479989e1 *R/pvclust.R 4ce445fdf8be068ed0f770d3c7bafd17 *data/lung.RData 4dd897fecd4b0175cf3318af419bebab *man/lung.Rd 25915dafebbf8ed6fb9234776ae7349c *man/msfit.Rd f2b43095e239bcd8edd83cb0d6b6352c *man/msplot.Rd c2c1b6dbd2843eeccbee5ec24da11b49 *man/plot.pvclust.Rd 1a64c1ea3c377fad1a8d81c67c34f0a7 *man/print.pvclust.Rd 4c4235b3d254542f783980bcde9b03d2 *man/pvclust.Rd cfa83769e276f25bdb5c83b430553af5 *man/pvpick.Rd 3ab1fb9eebdc5f8b3915d71258bee39c *man/seplot.Rd pvclust/DESCRIPTION0000644000175100001440000000150512612441324013476 0ustar hornikusersPackage: pvclust Version: 2.0-0 Date: 2015-10-23 Title: Hierarchical Clustering with P-Values via Multiscale Bootstrap Resampling Author: Ryota Suzuki , Hidetoshi Shimodaira Maintainer: Ryota Suzuki Depends: R (>= 2.10.0) Suggests: MASS, parallel Description: An implementation of multiscale bootstrap resampling for assessing the uncertainty in hierarchical cluster analysis. It provides AU (approximately unbiased) p-value as well as BP (bootstrap probability) value for each cluster in a dendrogram. License: GPL (>= 2) URL: http://www.sigmath.es.osaka-u.ac.jp/shimo-lab/prog/pvclust/ NeedsCompilation: no Packaged: 2015-10-23 11:28:03 UTC; suzuki Repository: CRAN Date/Publication: 2015-10-23 16:23:16 pvclust/man/0000755000175100001440000000000012612414703012543 5ustar hornikuserspvclust/man/msfit.Rd0000644000175100001440000000567512612414703014171 0ustar hornikusers\name{msfit} \alias{msfit} \alias{plot.msfit} \alias{lines.msfit} \alias{summary.msfit} \title{Curve Fitting for Multiscale Bootstrap Resampling} \description{\code{msfit} performs curve fitting for multiscale bootstrap resampling. It generates an object of class \code{msfit}. Several generic methods are available. } \usage{ msfit(bp, r, nboot) \method{plot}{msfit}(x, curve=TRUE, main=NULL, sub=NULL, xlab=NULL, ylab=NULL, ...) \method{lines}{msfit}(x, col=2, lty=1, ...) \method{summary}{msfit}(object, digits=3, ...) } \arguments{ \item{bp}{numeric vector of bootstrap probability values.} \item{r}{numeric vector of relative sample size of bootstrap samples defined as \eqn{r=n'/n} for original sample size \eqn{n} and bootstrap sample size \eqn{n'}.} \item{nboot}{numeric value (vector) of the number of bootstrap replications.} \item{x}{object of class \code{msfit}.} \item{curve}{logical. If \code{TRUE}, the fitted curve is drawn.} \item{main, sub, xlab, ylab, col, lty}{generic graphic parameters.} \item{object}{object of class \code{msfit}.} \item{digits}{integer indicating the precision to be used in rounding.} \item{...}{other parameters to be used in the functions.} } \details{ function \code{msfit} performs the curve fitting for multiscale bootstrap resampling. In package \code{pvclust} this function is only called from the function \code{pvclust} (or \code{parPvclust}), and may never be called from users. However one can access a list of \code{msfit} objects by \code{x$msfit}, where \code{x} is an object of class \code{pvclust}. } \value{\code{msfit} returns an object of class \code{msfit}. It contains the following objects: \item{p}{numeric vector of \eqn{p}-values. \code{au} is AU (Approximately Unbiased) \eqn{p}-value computed by multiscale bootstrap resampling, which is more accurate than BP value (explained below) as unbiased \eqn{p}-value. \code{bp} is BP (Bootstrap Probability) value, which is simple but tends to be unbiased when the absolute value of \code{c} (a value in \code{coef} vector, explained below) is large.} \item{se}{numeric vector of estimated standard errors of \eqn{p}-values.} \item{coef}{numeric vector related to geometric aspects of hypotheses. \code{v} is signed distance and \code{c} is curvature of the boundary.} \item{df}{numeric value of the degree of freedom in curve fitting.} \item{rss}{residual sum of squares.} \item{pchi}{\eqn{p}-value of chi-square test based on asymptotic theory.} } \references{ Shimodaira, H. (2004) "Approximately unbiased tests of regions using multistep-multiscale bootstrap resampling", \emph{Annals of Statistics}, 32, 2616-2641. Shimodaira, H. (2002) "An approximately unbiased test of phylogenetic tree selection", \emph{Systematic Biology}, 51, 492-508. } \author{Ryota Suzuki \email{suzuki@ef-prime.com}} \keyword{htest}pvclust/man/print.pvclust.Rd0000644000175100001440000000312012612414703015661 0ustar hornikusers\name{print.pvclust} \alias{print.pvclust} \title{Print Function for Pvclust Object} \description{print clustering method and distance measure used in hierarchical clustering, \eqn{p}-values and related statistics for a \code{pvclust} object. } \usage{ \method{print}{pvclust}(x, which=NULL, digits=3, ...) } \arguments{ \item{x}{object of class \code{pvclust}.} \item{which}{numeric vector which specifies the numbers of edges (clusters) of which the values are printed. If \code{NULL} is given, it prints the values of all edges. The default is \code{NULL}.} \item{digits}{integer indicating the precision to be used in rounding.} \item{...}{other parameters used in the function.} } \value{this function prints \eqn{p}-values and some related statistics. \item{au}{AU (Approximately Unbiased) \eqn{p}-value, which is more accurate than BP value as unbiased \eqn{p}-value. It is computed by multiscale bootstrap resampling.} \item{bp}{BP (Bootstrap Probability) value, which is a simple statistic computed by bootstrap resampling. This value tends to be biased as \eqn{p}-value when the absolute value of \code{c} (explained below) is large.} \item{se.au, se.bp}{estimated standard errors for \code{au} and \code{bp}, respectively.} \item{v, c}{values related to geometric aspects of hypotheses. \code{v} is signed distance and \code{c} is curvature of the boundary.} \item{pchi}{\eqn{p}-values of chi-square test based on asymptotic theory.} } \author{Ryota Suzuki \email{suzuki@ef-prime.com}} \keyword{print}pvclust/man/seplot.Rd0000644000175100001440000000155412612414703014345 0ustar hornikusers\name{seplot} \alias{seplot} \title{Diagnostic Plot for Standard Error of p-value} \description{draws diagnostic plot for standard error of \eqn{p}-value for pvclust object. } \usage{ seplot(object, type=c("au", "bp"), identify=FALSE, main=NULL, xlab=NULL, ylab=NULL, ...) } \arguments{ \item{object}{object of class \code{pvclust}.} \item{type}{the type of \eqn{p}-value to be plotted, one of \code{"au"} or \code{"bp"}.} \item{identify}{logical. If \code{TRUE}, edge numbers can be identified interactively. See \code{\link{identify}} for basic usage.} \item{main, xlab, ylab}{generic graphic parameters. See \code{\link[graphics]{par}} for details.} \item{...}{other graphical parameters to be passed to generic \code{plot} or \code{identify} function.} } \author{Ryota Suzuki \email{suzuki@ef-prime.com}} \keyword{hplot} pvclust/man/msplot.Rd0000644000175100001440000000076012612414703014353 0ustar hornikusers\name{msplot} \alias{msplot} \title{Drawing the Results of Curve Fitting for Pvclust Object} \description{draws the results of curve fitting for pvclust object. } \usage{ msplot(x, edges=NULL, ...) } \arguments{ \item{x}{object of class \code{pvclust}.} \item{edges}{numeric vector of edge numbers to be plotted.} \item{...}{other parameters to be used in the function.} } \seealso{\code{\link{plot.msfit}}} \author{Ryota Suzuki \email{suzuki@ef-prime.com}} \keyword{hplot} pvclust/man/lung.Rd0000644000175100001440000000310512612414703013776 0ustar hornikusers\name{lung} \docType{data} \alias{lung} \title{DNA Microarray Data of Lung Tumors} \description{ DNA Microarray data of 73 lung tissues including 67 lung tumors. There are 916 observations of genes for each lung tissue. } \usage{data(lung)} \format{data frame of size \eqn{916 \times 73}{(916, 73)}.} \details{ This dataset has been modified from original data. Each one observation of duplicate genes has been removed. See \code{source} section in this help for original data source. } \examples{ \donttest{ ## Reading the data data(lung) ## Multiscale Bootstrap Resampling lung.pv <- pvclust(lung, nboot=100) ## CAUTION: nboot=100 may be too small for actual use. ## We suggest nboot=1000 or larger. ## plot/print functions will be useful for diagnostics. ## Plot the result plot(lung.pv, cex=0.8, cex.pv=0.7) ask.bak <- par()$ask par(ask=TRUE) pvrect(lung.pv, alpha=0.9) msplot(lung.pv, edges=c(51,62,68,71)) par(ask=ask.bak) ## Print a cluster with high p-value lung.pp <- pvpick(lung.pv, alpha=0.9) lung.pp$clusters[[2]] ## Print its edge number lung.pp$edges[2] } ## We recommend parallel computing for large dataset as this one \dontrun{ library(snow) cl <- makeCluster(10, type="MPI") lung.pv <- parPvclust(cl, lung, nboot=1000) } } \source{\url{http://genome-www.stanford.edu/lung_cancer/adeno/}} \references{ Garber, M. E. et al. (2001) "Diversity of gene expression in adenocarcinoma of the lung", \emph{Proceedings of the National Academy of Sciences}, 98, 13784-13789. } \keyword{datasets} pvclust/man/pvpick.Rd0000644000175100001440000000434512612414703014334 0ustar hornikusers\name{pvpick} \alias{pvpick} \alias{pvrect} \alias{lines.pvclust} \title{Find Clusters with High/Low P-values} \description{find clusters with relatively high/low \eqn{p}-values. \code{pvrect} and \code{lines} (S3 method for class \code{pvclust}) highlight such clusters in existing plot, and \code{pvpick} returns a list of such clusters. } \usage{ pvpick(x, alpha=0.95, pv="au", type="geq", max.only=TRUE) pvrect(x, alpha=0.95, pv="au", type="geq", max.only=TRUE, border=2, ...) \method{lines}{pvclust}(x, alpha=0.95, pv="au", type="geq", col=2, lwd=2, ...) } \arguments{ \item{x}{object of class \code{pvclust}.} \item{alpha}{threshold value for \eqn{p}-values.} \item{pv}{character string which specifies the \eqn{p}-value to be used. It should be either of \code{"au"} or \code{"bp"}, corresponding to AU \eqn{p}-value or BP value, respectively. See \code{plot.pvclust} for details.} \item{type}{one of \code{"geq"}, \code{"leq"}, \code{"gt"} or \code{"lt"}. If \code{"geq"} is specified, clusters with \eqn{p}-value \emph{greater than or equals} the threshold given by \code{"alpha"} are returned or displayed. Likewise \code{"leq"} stands for \emph{lower than or equals}, \code{"gt"} for \emph{greater than} and \code{"lt"} for \emph{lower than} the threshold value. The default is \code{"geq"}.} \item{max.only}{logical. If some of clusters with high/low \eqn{p}-values have inclusion relation, only the largest cluster is returned (or displayed) when \code{max.only=TRUE}.} \item{border}{numeric value which specifies the color of borders of rectangles.} \item{col}{numeric value which specifies the color of lines.} \item{lwd}{numeric value which specifies the width of lines.} \item{...}{other graphic parameters to be used.} } \value{\code{pvpick} returns a list which contains the following values. \item{clusters}{a list of character string vectors. Each vector corresponds to the names of objects in each cluster.} \item{edges}{numeric vector of edge numbers. The \eqn{i}'th element (number) corresponds to the \eqn{i}'th name vector in \code{clusters}.} } \author{Ryota Suzuki \email{suzuki@ef-prime.com}} \keyword{aplot}pvclust/man/pvclust.Rd0000644000175100001440000002325212612414703014536 0ustar hornikusers\name{pvclust} \alias{pvclust} \alias{parPvclust} \title{Calculating P-values for Hierchical Clustering} \description{ calculates \eqn{p}-values for hierarchical clustering via multiscale bootstrap resampling. Hierarchical clustering is done for given data and \eqn{p}-values are computed for each of the clusters. } \usage{ pvclust(data, method.hclust="average", method.dist="correlation", use.cor="pairwise.complete.obs", nboot=1000, parallel=FALSE, r=seq(.5,1.4,by=.1), store=FALSE, weight=FALSE, iseed=NULL, quiet=FALSE) parPvclust(cl=NULL, data, method.hclust="average", method.dist="correlation", use.cor="pairwise.complete.obs", nboot=1000, r=seq(.5,1.4,by=.1), store=FALSE, weight=FALSE, init.rand=NULL, iseed=NULL, quiet=FALSE) } \arguments{ \item{data}{numeric data matrix or data frame.} \item{method.hclust}{ the agglomerative method used in hierarchical clustering. This should be (an abbreviation of) one of \code{"average"}, \code{"ward.D"}, \code{"ward.D2"}, \code{"single"}, \code{"complete"}, \code{"mcquitty"}, \code{"median"} or \code{"centroid"}. The default is \code{"average"}. See \code{method} argument in \code{\link[stats]{hclust}}. } \item{method.dist}{the distance measure to be used. This should be a character string, or a function which returns a \code{dist} object. A character string should be (an abbreviation of) one of \code{"correlation"}, \code{"uncentered"}, \code{"abscor"} or those which are allowed for \code{method} argument in \code{dist} function. The default is \code{"correlation"}. See \emph{details} section in this help and \code{method} argument in \code{\link[stats]{dist}}. } \item{use.cor}{character string which specifies the method for computing correlation with data including missing values. This should be (an abbreviation of) one of \code{"all.obs"}, \code{"complete.obs"} or \code{"pairwise.complete.obs"}. See the \code{use} argument in \code{\link[stats]{cor}} function. } \item{nboot}{the number of bootstrap replications. The default is \code{1000}.} \item{parallel}{switch for parallel computation. If \code{FALSE} the computation is done in non-parallel mode. If \code{TRUE} or a positive integer is supplied, parallel computation is done with automatically generated PSOCK cluster. Use \code{TRUE} for default cluster size (\code{parallel::detectCores() - 1}), or specify the size by an integer. If a \code{cluster} object is supplied the cluster is used for parallel computation. Note that \code{NULL} is currently not allowed for using the default cluster. } \item{r}{numeric vector which specifies the relative sample sizes of bootstrap replications. For original sample size \eqn{n} and bootstrap sample size \eqn{n'}, this is defined as \eqn{r=n'/n}.} \item{store}{locical. If \code{store=TRUE}, all bootstrap replications are stored in the output object. The default is \code{FALSE}.} \item{cl}{a cluster object created by package \pkg{parallel} or \pkg{snow}. If NULL, use the registered default cluster.} \item{weight}{logical. If \code{weight=TRUE}, resampling is made by weight vector instead of index vector. Useful for large \code{r} value (\code{r>10}). Currently, available only for distance \code{"correlation"} and \code{"abscor"}.} % \item{init.rand}{logical. If \code{init.rand=TRUE}, random number % generators are initialized at child processes. Random seeds can be % set by \code{seed} argument.} % \item{seed}{integer vector of random seeds. It should have the same % length as \code{cl}. If \code{NULL} is specified, % \code{1:length(cl)} is used as seed vector. The default is \code{NULL}.} \item{init.rand}{logical. If \code{init.rand=TRUE}, random number generators are initialized. Use \code{iseed} argument to achieve reproducible results. \strong{This argument is duplicated and will be unavailable in the future.}} % \item{seed}{integer vector of random seeds. It should have the same % length as \code{cl}. \strong{This argument is duplicated and will be unavailable in the future. Consider using \code{iseed} instead.} } \item{iseed}{An integer. If non-\code{NULL} value is supplied random number generators are initialized. It is passed to \code{set.seed} or \code{clusterSetRNGStream}.} \item{quiet}{logical. If \code{TRUE} it does not report the progress.} } \details{ Function \code{pvclust} conducts multiscale bootstrap resampling to calculate \eqn{p}-values for each cluster in the result of hierarchical clustering. \code{parPvclust} is the parallel version of this procedure which depends on package \pkg{parallel} for parallel computation. For data expressed as \eqn{(n \times p)}{(n, p)} matrix or data frame, we assume that the data is \eqn{n} observations of \eqn{p} objects, which are to be clustered. The \eqn{i}'th row vector corresponds to the \eqn{i}'th observation of these objects and the \eqn{j}'th column vector corresponds to a sample of \eqn{j}'th object with size \eqn{n}. There are several methods to measure the dissimilarities between objects. For data matrix \eqn{X=\{x_{ij}\}}{X}, \code{"correlation"} method takes \deqn{ 1 - \frac{ \sum_{i=1}^n (x_{ij} - \bar{x}_j) (x_{ik} - \bar{x}_k) } { \sqrt{\sum_{i=1}^n (x_{ij} - \bar{x}_j)^2} \sqrt{\sum_{i=1}^n (x_{ik} - \bar{x}_k)^2} } }{% 1 - cor(X)[j,k] } for dissimilarity between \eqn{j}'th and \eqn{k}'th object, where \eqn{\bar{x}_j = \frac{1}{n} \sum_{i=1}^n x_{ij} \mbox{and} \bar{x}_k = \frac{1}{n} \sum_{i=1}^n x_{ik}}{cor is function \code{cor}}. \code{"uncentered"} takes uncentered sample correlation \deqn{ 1 - \frac{ \sum_{i=1}^n x_{ij} x_{ik} } { \sqrt{\sum_{i=1}^n x_{ij}^2} \sqrt{\sum_{i=1}^n x_{ik}^2} } }{% 1 - sum(x[,j] * x[,k]) / (sqrt(sum(x[,j]^2)) * sqrt(sum(x[,k]^2))) } and \code{"abscor"} takes the absolute value of sample correlation \deqn{ 1 - \ \Biggl| \frac{ \sum_{i=1}^n (x_{ij} - \bar{x}_j) (x_{ik} - \bar{x}_k) } { \sqrt{\sum_{i=1}^n (x_{ij} - \bar{x}_j)^2} \sqrt{\sum_{i=1}^n (x_{ik} - \bar{x}_k)^2} } \Biggl|. }{% 1 - abs(cor(X)[j,k]). } } \value{ \item{hclust}{hierarchical clustering for original data generated by function \code{hclust}. See \code{\link[stats]{hclust}} for details.} \item{edges}{data frame object which contains \eqn{p}-values and supporting informations such as standard errors.} \item{count}{data frame object which contains primitive information about the result of multiscale bootstrap resampling.} \item{msfit}{list whose elements are results of curve fitting for multiscale bootstrap resampling, of class \code{msfit}. See \code{\link{msfit}} for details.} \item{nboot}{numeric vector of number of bootstrap replications.} \item{r}{numeric vector of the relative sample size for bootstrap replications.} \item{store}{list contains bootstrap replications if \code{store=TRUE} was given for function \code{pvclust} or \code{parPvclust}.} } \seealso{\code{\link{lines.pvclust}}, \code{\link{print.pvclust}}, \code{\link{msfit}}, \code{\link{plot.pvclust}}, \code{\link{text.pvclust}}, \code{\link{pvrect}} and \code{\link{pvpick}}.} \references{ Suzuki, R. and Shimodaira, H. (2006) "Pvclust: an R package for assessing the uncertainty in hierarchical clustering", \emph{Bioinformatics}, 22 (12): 1540-1542. Shimodaira, H. (2004) "Approximately unbiased tests of regions using multistep-multiscale bootstrap resampling", \emph{Annals of Statistics}, 32, 2616-2641. Shimodaira, H. (2002) "An approximately unbiased test of phylogenetic tree selection", \emph{Systematic Biology}, 51, 492-508. Suzuki, R. and Shimodaira, H. (2004) "An application of multiscale bootstrap resampling to hierarchical clustering of microarray data: How accurate are these clusters?", \emph{The Fifteenth International Conference on Genome Informatics 2004}, P034. \url{http://www.sigmath.es.osaka-u.ac.jp/shimo-lab/prog/pvclust/} } \examples{ ### example using Boston data in package MASS data(Boston, package = "MASS") ## multiscale bootstrap resampling (non-parallel) boston.pv <- pvclust(Boston, nboot=100, parallel=FALSE) ## CAUTION: nboot=100 may be too small for actual use. ## We suggest nboot=1000 or larger. ## plot/print functions will be useful for diagnostics. ## plot dendrogram with p-values plot(boston.pv) ask.bak <- par()$ask par(ask=TRUE) ## highlight clusters with high au p-values pvrect(boston.pv) ## print the result of multiscale bootstrap resampling print(boston.pv, digits=3) ## plot diagnostic for curve fitting msplot(boston.pv, edges=c(2,4,6,7)) par(ask=ask.bak) ## print clusters with high p-values boston.pp <- pvpick(boston.pv) boston.pp ### Using a custom distance measure ## Define a distance function which returns an object of class "dist". ## The function must have only one argument "x" (data matrix or data.frame). cosine <- function(x) { x <- as.matrix(x) y <- t(x) \%*\% x res <- 1 - y / (sqrt(diag(y)) \%*\% t(sqrt(diag(y)))) res <- as.dist(res) attr(res, "method") <- "cosine" return(res) } result <- pvclust(Boston, method.dist=cosine, nboot=100) plot(result) \dontrun{ ### parallel computation result.par <- pvclust(Boston, nboot=1000, parallel=TRUE) plot(result.par) } } \author{Ryota Suzuki \email{suzuki@ef-prime.com}} \keyword{cluster} pvclust/man/plot.pvclust.Rd0000644000175100001440000000553212612414703015514 0ustar hornikusers\name{plot.pvclust} \alias{plot.pvclust} \alias{text.pvclust} \title{Draws Dendrogram with P-values for Pvclust Object} \description{ plot dendrogram for a \code{pvclust} object and add \eqn{p}-values for clusters. } \usage{ \method{plot}{pvclust}(x, print.pv=TRUE, print.num=TRUE, float=0.01, col.pv=c(2,3,8), cex.pv=0.8, font.pv=NULL, col=NULL, cex=NULL, font=NULL, lty=NULL, lwd=NULL, main=NULL, sub=NULL, xlab=NULL, ...) \method{text}{pvclust}(x, col=c(2,3,8), print.num=TRUE, float=0.01, cex=NULL, font=NULL, ...) } \arguments{ \item{x}{object of class \code{pvclust}, which is generated by function \code{pvclust}. See \code{\link{pvclust}} for details.} \item{print.pv}{logical flag to specify whether print \eqn{p}-values above the edges (clusters).} \item{print.num}{logical flag to specify whether print edge numbers below clusters.} \item{float}{numeric value to adjust the height of \eqn{p}-values from edges.} \item{col.pv}{numeric vector of length three to specify the colors for \eqn{p}-values and edge numbers. From the beginning each value corresponds to the color of AU values, BP values and edge numbers, respectively.} \item{cex.pv}{numeric value which specifies the size of characters for \eqn{p}-values and edge numbers. See \code{cex} argument for \code{\link[graphics]{par}}.} \item{font.pv}{numeric value which specifies the font of characters for \eqn{p}-values and edge numbers. See \code{font} argument for \code{\link[graphics]{par}}.} \item{col, cex, font}{in \code{text} function, they correspond to \code{col.pv}, \code{cex.pv} and \code{font.pv} in \code{plot} function, respectively. In \code{plot} function they are used as generic graphic parameters.} \item{lty, lwd, main, sub, xlab, ...}{ generic graphic parameters. See \code{\link[graphics]{par}} for details.} } \details{ This function plots a dendrogram with \eqn{p}-values for given object of class \code{pvclust}. AU \eqn{p}-value (printed in red color in default) is the abbreviation of "approximately unbiased" \eqn{p}-value, which is calculated by multiscale bootstrap resampling. BP value (printed in green color in default) is "bootstrap probability" value, which is less accurate than AU value as \eqn{p}-value. One can consider that clusters (edges) with high AU values (e.g. 95\%) are strongly supported by data. } \seealso{\code{\link{text.pvclust}}} \references{ Shimodaira, H. (2004) "Approximately unbiased tests of regions using multistep-multiscale bootstrap resampling", \emph{Annals of Statistics}, 32, 2616-2641. Shimodaira, H. (2002) "An approximately unbiased test of phylogenetic tree selection", \emph{Systematic Biology}, 51, 492-508. } \author{Ryota Suzuki \email{suzuki@ef-prime.com}} \keyword{hplot}