effects/0000755000176200001440000000000013157035277011703 5ustar liggesuserseffects/inst/0000755000176200001440000000000013156310725012651 5ustar liggesuserseffects/inst/CITATION0000644000176200001440000000271412713174763014022 0ustar liggesuserscitHeader("To cite effects in publications use:") citEntry(entry = "Article", title = "Effect Displays in {R} for Generalised Linear Models", author = as.person("John Fox"), journal = "Journal of Statistical Software", year = "2003", volume = "8", number = "15", pages = "1--27", url = "http://www.jstatsoft.org/v08/i15/", textVersion = paste("John Fox (2003).", "Effect Displays in R for Generalised Linear Models.", "Journal of Statistical Software, 8(15), 1-27.", "URL http://www.jstatsoft.org/v08/i15/.") ) citEntry(entry = "Article", title = "Effect Displays in {R} for Multinomial and Proportional-Odds Logit Models: Extensions to the {effects} Package", author = personList(as.person("John Fox"), as.person("Jangman Hong")), journal = "Journal of Statistical Software", year = "2009", volume = "32", number = "1", pages = "1--24", url = "http://www.jstatsoft.org/v32/i01/", textVersion = paste("John Fox, Jangman Hong (2009).", "Effect Displays in R for Multinomial and Proportional-Odds Logit Models: Extensions to the effects Package.", "Journal of Statistical Software, 32(1), 1-24.", "URL http://www.jstatsoft.org/v32/i01/."), header = "For usage in multinomial and proportional-odds logit models also cite:" ) effects/inst/doc/0000755000176200001440000000000013157010115013405 5ustar liggesuserseffects/inst/doc/partial-residuals.pdf0000644000176200001440000456566613157030524017567 0ustar liggesusers%PDF-1.5 % 3 0 obj << /Length 2584 /Filter /FlateDecode >> stream xێ}q@ryH14E^?p4E*$"ȷ9CRWbax99H'#B&9G^AD Kc:/2U G.bB_pUZG]-d(p$V t= e0HZ'-JIV ?6H оt>%߯K}O5n@`G$u#Pߗȟ2X 4Ġ@&i!4x䓢#p|y  ppȉYIXb܀ $8)@ *+4" Yϴ\յ=%)`-|Aj$s)xk)! a,G6?0b{XT\"4&?>t Gmq#HZM~Bz!/n-%LUʤn64<-IetZ\[@W浨ox$NnfG.wTI>[72qPx9JC$5OED F>NjE[c]Ų6ng^+JZjӺ(e0 $tndb0emf1 vZ0zpNgay4S]pӄT7 ^FP\@w`I9ըR f:EcڒbzJ[:,Wv+!7Dc2  >aXea<5 LMLYkEpRe%}o͢+X6&8Xr¯EKR5nWa&BY{ E{Q |0fL BI7XU)c+?x@3o,Ug9OV!xA@LRef i#}-tPSOepEXmIYQ"!0G)GF2xv( t3n5FyVDpD`M̯:  I)Gp]U"Iu3_Emb iΘbU2\Lfpur!*kWqMx V;!wH6NH7I-akEMn P.63Ih:d\$?ݾd -xaZRvN'8K j\J٫_(9`}SmzkœH t.:1`fTU"x @րØllkkT">U>@0}[d. 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\newcommand{\lvn}[1]{\mbox{$\log(\mbox{\texttt{#1}})$}} \begin{document} \title{Examples of Effect Displays with Partial Residuals\\ Using Contrived Regression Data} \author{John Fox and Sanford Weisberg} \date{2017-09-13} \maketitle <>= library(knitr) opts_chunk$set( tidy=FALSE,fig.width=5,fig.height=5,cache=FALSE ) @ <>= #options(continue="+ ", prompt="R> ", width=76) options(show.signif.stars=FALSE) options(scipen=3) @ The examples developed in this vignette are meant to supplement \citet{FoxWeisberg17}. \section{Basic Setup} We will analyze contrived data generated according to the following setup: \begin{itemize} \item We sample $n = 5000$ observations from a trivariate distribution for predictors $x_1$, $x_2$, and $x_3$, with uniform margins on the interval $[-2, 2]$, and with a prespecified bivariate correlation $\rho$ between each pair of predictors. The method employed, described by \citet{Schumann15} and traceable to results reported by \citet{Pearson07}, produces predictors that are nearly linearly related. Using 5000 observations allows us to focus on essentially asymptotic behavior of partial residuals in effect plots while still being able to discern individual points in the resulting graphs. \item We then generate the response $y$ according to the model \begin{equation} y = \beta_0 + h\left(\bbeta, \{x_1, x_2, x_3\}\right) + \varepsilon \end{equation} where $\varepsilon \Rtilde \N(0, 1.5^2)$. The regression function $h(\cdot)$ varies from example to example. \end{itemize} The following functions make it convenient to generate data according to this setup. These functions are more general than is strictly necessary so as to encourage further experimentation. <<>>= mvrunif <- function(n, R, min = 0, max = 1){ # method (but not code) from E. Schumann, # "Generating Correlated Uniform Variates" # URL: # # downloaded 2015-05-21 if (!is.matrix(R) || nrow(R) != ncol(R) || max(abs(R - t(R))) > sqrt(.Machine$double.eps)) stop("R must be a square symmetric matrix") if (any(eigen(R, only.values = TRUE)$values <= 0)) stop("R must be positive-definite") if (any(abs(R) - 1 > sqrt(.Machine$double.eps))) stop("R must be a correlation matrix") m <- nrow(R) R <- 2 * sin(pi * R / 6) X <- matrix(rnorm(n * m), n, m) X <- X %*% chol(R) X <- pnorm(X) min + X * (max - min) } gendata <- function(n = 5000, R, min = -2, max = 2, s = 1.5, model = expression(x1 + x2 + x3)){ data <- mvrunif(n = n, min = min, max = max, R = R) colnames(data) <- c("x1", "x2", "x3") data <- as.data.frame(data) data$error <- s * rnorm(n) data$y <- with(data, eval(model) + error) data } R <- function(offdiag = 0, m = 3){ R <- diag(1, m) R[lower.tri(R)] <- R[upper.tri(R)] <- offdiag R } @ \section{Unmodelled Interaction} We begin with uncorrelated predictors and the true regression mean function $\E(y|\x) = x_1 + x_2x_3$, but fit the incorrect additive working model $y \Rtilde x_1 + x_2 + x_3$ to the data. <<>>= set.seed(682626) Data.1 <- gendata(R = R(0), model = expression(x1 + x2 * x3)) round(cor(Data.1), 2) summary(mod.1 <- lm(y ~ x1 + x2 + x3, data = Data.1)) @ For reproducibility, we set a known seed for the pseudo-random number generator; this seed was itself generated pseudo-randomly, and we reuse it in the examples reported below. As well, in this first example, but not for those below, we show the correlation matrix of the randomly generated data along with the fit of the working model to the data. Effect plots with partial residuals corresponding to the terms in the working model are shown in Figure~\ref{fig-contrived-1a}: <>= library(effects) plot(predictorEffects(mod.1, partial.residuals=TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), rows=1, cols=3) @ In these graphs and, unless noted to the contrary, elsewhere in this vignette, the loess smooths are drawn with span 2/3. Because of the large number of points in the graphs, optional arguments to \code{plot} are specified to de-emphasize the partial residuals. To this end, the residuals are plotted as small points (\code{pch="."}) and in a translucent magenta color (\code{col="\#FF00FF80"}). \begin{figure}[tbp] \caption{Effect displays with partial residuals for the individual predictors $x_1$, $x_2$, and $x_3$ in the incorrect model $y \captilde x_1 + x_2 + x_3$ fit to data generated with the mean function $\E(y|\x) = x_1 + x_2x_3$, with uncorrelated predictors.\label{fig-contrived-1a}} \centering \includegraphics[width=1\textwidth]{figure/fig-contrived-1a-1.pdf} \end{figure} The failure of the model is not apparent in these traditional partial residual plots, but it is clear in the term effect plot for $\{x_2, x_3\}$, corresponding to the unmodelled interaction \inter{x_2}{x_3}, and shown in the top panel of Figure~\ref{fig-contrived-1b}, generated using <>= plot(Effect(c("x2", "x3"), mod.1, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ Moreover, the effect plot in the bottom panel of the figure for $\{x_1, x_2\}$, corresponding to a term \emph{not} in the true mean function, correctly indicates lack of interaction between these two predictors: <>= plot(Effect(c("x1", "x2"), mod.1, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ \begin{figure}[tbp] \caption{Term effect displays with partial residuals for $\{x_2, x_3 \}$, corresponding to the missing interaction \inter{x_2}{x_3}, and for $\{x_1, x_2 \}$, corresponding to an interaction not present in the model that generated the data.\label{fig-contrived-1b}} \centering \includegraphics[width=1\textwidth]{figure/fig-contrived-1b-1.pdf} \\ \includegraphics[width=1\textwidth]{figure/fig-contrived-1c-1.pdf} \end{figure} As a partly contrasting example, we turn to a similar data set, generated with the same regression mean function but with moderately correlated predictors, where the pairwise predictor correlations are $\rho = 0.5$: <<>>= set.seed(682626) Data.2 <- gendata(R = R(0.5), model = expression(x1 + x2 * x3)) mod.2 <- lm(y ~ x1 + x2 + x3, data = Data.2) @ Graphs analogous to those from the preceding example appear in Figures~\ref{fig-contrived-2a} and \ref{fig-contrived-2b}: <>= plot(predictorEffects(mod.2, partial.residuals=TRUE), partial.residual=list(pch=".", col="#FF00FF80",fig.show='hide'), axes=list(x=list(rotate=45)), rows=1, cols=3) @ <>= plot(Effect(c("x2", "x3"), mod.2, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ <>= plot(Effect(c("x1", "x2"), mod.2, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80",fig.show='hide'), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ The predictor effect plots for $x_2$ and $x_3$, and to a much lesser extent, for $x_1$, in the incorrect model in Figure~\ref{fig-contrived-2a} show apparent nonlinearity as a consequence of the unmodelled interaction and the correlations among the predictors. A similar phenomenon was noted in our analysis of the Canadian occupational prestige data in \citet[Section~4.2]{FoxWeisberg17}, where the unmodelled interaction between \code{type} and \code{income} induced nonlinearity in the partial relationship of \code{prestige} to \code{income}. The omitted interaction is clear in the effect plot for $\{x_2, x_3\}$, but also, to a lesser extent, contaminates the effect plot for $\{x_1,x_2\}$, which corresponds to an interaction that does not enter the model generating the data. These artifacts become more prominent if we increase the predictor correlations, say to $\rho = 0.9$ (as we invite the reader to do). \begin{figure}[tbp] \caption{Predictor effect displays with partial residuals for the individual predictors $x_1$, $x_2$, and $x_3$ in the incorrect model $y \captilde x_1 + x_2 + x_3$ fit to data generated with the mean function $\E(y|\x) = x_1 + x_2x_3$, with moderately correlated predictors.\label{fig-contrived-2a}} \centering \includegraphics[width=1\textwidth]{figure/fig-contrived-2a-1.pdf} \end{figure} \begin{figure}[tbp] \caption{Term effect displays with partial residuals for $\{x_2, x_3 \}$, corresponding to the missing interaction \inter{x_2}{x_3}, and for $\{x_1, x_2 \}$, corresponding to an interaction not present in the model that generated the data.\label{fig-contrived-2b}} \centering \includegraphics[width=1\textwidth]{figure/fig-contrived-2b-1.pdf}\\ \includegraphics[width=1\textwidth]{figure/fig-contrived-2c-1.pdf} \end{figure} \section{Unmodelled Nonlinearity} We generate data as before, but from the true model $\E(y|\x) = x_1^2 + x_2 + x_3$, where the predictors are moderately correlated, with pairwise correlations $\rho = 0.5$, but fit the incorrect additive working model $y \Rtilde x_1 + x_2 + x_3$ to the data: <<>>= set.seed(682626) Data.3 <- gendata(R = R(0.5), model = expression(x1^2 + x2 + x3)) mod.3 <- lm(y ~ x1 + x2 + x3, data = Data.3) @ Effect plots with residuals for the predictors in the working model appear in Figure~\ref{fig-contrived-3a}. The unmodelled nonlinearity in the partial relationship of $y$ to $x_1$ is clear, but there is some contamination of the plots for $x_2$ and $x_3$. The contamination is much more dramatic if the correlations among the predictors are increased to, say, $\rho = 0.9$ (as the reader may verify). <>= plot(predictorEffects(mod.3, partial.residuals=TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), rows=1, cols=3) @ \begin{figure}[tbp] \caption{Predictor effect displays with partial residuals for the individual predictors $x_1$, $x_2$, and $x_3$ in the incorrect model $y \captilde x_1 + x_2 + x_3$ fit to data generated with the mean function $\E(y|\x) = x_1^2 + x_2 + x_3$, with moderately correlated predictors.\label{fig-contrived-3a}} \centering \includegraphics[width=1\textwidth]{figure/fig-contrived-3a-1.pdf} \end{figure} Effect plots for $\{x_1, x_2 \}$ and $\{x_2, x_3 \}$ are shown in Figure~\ref{fig-contrived-3b}: <>= plot(Effect(c("x2", "x3"), mod.3, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ <>= plot(Effect(c("x1", "x2"), mod.3, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ Neither of these graphs corresponds to a term in the model generating the data nor in the working model, and the effect plots largely confirm the absence of \inter{x_1}{x_2} and \inter{x_2}{x_3} interactions, along with the nonlinearity of the partial effect of $x_1$, apparent in the top panel. \begin{figure}[tbp] \caption{Term effect displays with partial residuals for $\{x_1, x_2 \}$ and for $\{x_2, x_3 \}$, neither of which corresponds to an interaction in the model generating the data.\label{fig-contrived-3b}} \centering \includegraphics[width=1\textwidth]{figure/fig-contrived-3c-1.pdf} \\ \includegraphics[width=1\textwidth]{figure/fig-contrived-3b-1.pdf} \end{figure} \section{Simultaneous Unmodelled Nonlinearity and Interaction} This last example also appears in \citet[Section~4.3]{FoxWeisberg17}. We consider a true model that combines nonlinearity and interaction, $\E(y|\x) = x_1^2 + x_2 x_3$; the predictors are moderately correlated, with $\rho = 0.5$. We then fit the incorrect working model $y \Rtilde x_1 + x_2 + x_3$ to the data, producing the predictor effect displays with partial residuals in Figure~\ref{fig-contrived-4a}, for the predictors $x_1$, $x_2$, and $x_3$, which appear additively in the working model, and the term effect displays in Figure~\ref{fig-contrived-4b} for $\{x_2, x_3 \}$ and $\{x_1, x_2 \}$, corresponding respectively to the incorrectly excluded \inter{x_2}{x_3} term and the correctly excluded \inter{x_1}{x_2} interaction. <<>>= set.seed(682626) Data.4 <- gendata(R = R(0.5), model = expression(x1^2 + x2 * x3)) mod.4 <- lm(y ~ x1 + x2 + x3, data = Data.4) @ <>= plot(predictorEffects(mod.4, partial.residuals=TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), rows=1, cols=3) @ <>= plot(Effect(c("x2", "x3"), mod.4, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ <>= plot(Effect(c("x1", "x2"), mod.4, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ The nonlinearity in the partial relationship of $y$ to $x_1$ shows up clearly. The nonlinearity apparent in the plots for $x_2$ and $x_3$ is partly due to contamination with $x_1$, but largely to the unmodelled interaction between $x_2$ and $x_3$, coupled with the correlation between these predictors. The plot corresponding to the missing \inter{x_2}{x_3} term (in the top panel of Figure~\ref{fig-contrived-4b}) does a good job of detecting the unmodelled interaction, and curvature in this plot is slight. The plot for the \inter{x_1}{x_2} term (in the bottom panel of Figure~\ref{fig-contrived-4b}), a term neither in the true model nor in the working model, primarily reveals the unmodelled nonlinearity in the partial relationship of $y$ to $x_1$. \begin{figure}[tbp] \caption{Effect displays with partial residuals for the predictors $x_1$, $x_2$, and $x_3$ in the incorrect model $y \captilde x_1 + x_2 + x_3$ fit to data generated with the mean function $\E(y|\x) = x_1^2 + x_2x_3$, with moderately correlated predictors.\label{fig-contrived-4a}} \centering \includegraphics[width=1\textwidth]{figure/fig-contrived-4a-1.pdf} \end{figure} \begin{figure}[tbp] \caption{Term effect displays with partial residuals for $\{x_2, x_3 \}$ (top) and for $\{x_1, x_2 \}$ (bottom), the first of which corresponds to the missing \inter{x_2}{x_3} interaction in the model generating the data.\label{fig-contrived-4b}} \centering \includegraphics[width=1\textwidth]{figure/fig-contrived-4b-1.pdf} \\ \includegraphics[width=1\textwidth]{figure/fig-contrived-4c-1.pdf} \end{figure} If we fit the correct model, $y \Rtilde{} x_1^2 + x_2*x_3$, to the data, we obtain the plots shown in Figure~\ref{fig-contrived-5}. As theory suggests, the partial residuals in these effect displays validate the model, supporting the exclusion of the \inter{x_1}{x_2} interaction, the linear-by-linear interaction between $x_1$ and $x_2$, and the quadratic partial relationship of $y$ to $x_1$. <>= mod.5 <- lm(y ~ poly(x1, 2) + x2*x3, data=Data.4) plot(Effect("x1", mod.5, partial.residuals=TRUE), partial.residual=list(pch=".", col="#FF00FF80", span=0.2)) @ <>= plot(Effect(c("x2", "x3"), mod.5, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1)), span=0.5) @ <>= plot(Effect(c("x1", "x2"), mod.5, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80", span=0.35), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ \noindent In these graphs, we adjust the span of the loess smoother to the approximately smallest value that produces a smooth fit to the partial residuals in each case. \begin{figure}[tbp] \caption{Effect displays with partial residuals for $x_1$ and $\{x_2, x_3 \}$, which correspond to terms in the model generating \emph{and} fitted to the data, $y \captilde x_1^2 + x_2 * x_3$, and for $\{x_1, x_2 \}$, which corresponds to an interaction that is not in the model.\label{fig-contrived-5}} \centering \includegraphics[width=0.45\textwidth]{figure/fig-contrived-5a-1.pdf} \\ \includegraphics[width=1\textwidth]{figure/fig-contrived-5b-1.pdf} \\ \includegraphics[width=1\textwidth]{figure/fig-contrived-5c-1.pdf} \end{figure} \bibliography{partial-residuals} \end{document} effects/inst/doc/partial-residuals.R0000644000176200001440000001465613157030522017175 0ustar liggesusers## ----include=FALSE------------------------------------------------------- library(knitr) opts_chunk$set( tidy=FALSE,fig.width=5,fig.height=5,cache=FALSE ) ## ----echo=FALSE, results='hide', include=FALSE--------------------------- #options(continue="+ ", prompt="R> ", width=76) options(show.signif.stars=FALSE) options(scipen=3) ## ------------------------------------------------------------------------ mvrunif <- function(n, R, min = 0, max = 1){ # method (but not code) from E. Schumann, # "Generating Correlated Uniform Variates" # URL: # # downloaded 2015-05-21 if (!is.matrix(R) || nrow(R) != ncol(R) || max(abs(R - t(R))) > sqrt(.Machine$double.eps)) stop("R must be a square symmetric matrix") if (any(eigen(R, only.values = TRUE)$values <= 0)) stop("R must be positive-definite") if (any(abs(R) - 1 > sqrt(.Machine$double.eps))) stop("R must be a correlation matrix") m <- nrow(R) R <- 2 * sin(pi * R / 6) X <- matrix(rnorm(n * m), n, m) X <- X %*% chol(R) X <- pnorm(X) min + X * (max - min) } gendata <- function(n = 5000, R, min = -2, max = 2, s = 1.5, model = expression(x1 + x2 + x3)){ data <- mvrunif(n = n, min = min, max = max, R = R) colnames(data) <- c("x1", "x2", "x3") data <- as.data.frame(data) data$error <- s * rnorm(n) data$y <- with(data, eval(model) + error) data } R <- function(offdiag = 0, m = 3){ R <- diag(1, m) R[lower.tri(R)] <- R[upper.tri(R)] <- offdiag R } ## ------------------------------------------------------------------------ set.seed(682626) Data.1 <- gendata(R = R(0), model = expression(x1 + x2 * x3)) round(cor(Data.1), 2) summary(mod.1 <- lm(y ~ x1 + x2 + x3, data = Data.1)) ## ----fig-contrived-1a,include=TRUE,fig.width=12,fig.height=4,fig.show='hide'---- library(effects) plot(predictorEffects(mod.1, partial.residuals=TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), rows=1, cols=3) ## ----fig-contrived-1b,include=TRUE, fig.width=12,fig.height=4,fig.show='hide'---- plot(Effect(c("x2", "x3"), mod.1, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) ## ----fig-contrived-1c,include=TRUE,fig.width=12,fig.height=4,fig.show='hide'---- plot(Effect(c("x1", "x2"), mod.1, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) ## ------------------------------------------------------------------------ set.seed(682626) Data.2 <- gendata(R = R(0.5), model = expression(x1 + x2 * x3)) mod.2 <- lm(y ~ x1 + x2 + x3, data = Data.2) ## ----fig-contrived-2a,include=TRUE,fig.width=12,fig.height=4,fig.show='hide'---- plot(predictorEffects(mod.2, partial.residuals=TRUE), partial.residual=list(pch=".", col="#FF00FF80",fig.show='hide'), axes=list(x=list(rotate=45)), rows=1, cols=3) ## ----fig-contrived-2b,include=TRUE,fig.width=12,fig.height=4,fig.show='hide'---- plot(Effect(c("x2", "x3"), mod.2, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) ## ----fig-contrived-2c,include=TRUE,fig.width=12,fig.height=4,fig.show='hide'---- plot(Effect(c("x1", "x2"), mod.2, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80",fig.show='hide'), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) ## ------------------------------------------------------------------------ set.seed(682626) Data.3 <- gendata(R = R(0.5), model = expression(x1^2 + x2 + x3)) mod.3 <- lm(y ~ x1 + x2 + x3, data = Data.3) ## ----fig-contrived-3a,include=TRUE,fig.width=12,fig.height=4,fig.show='hide'---- plot(predictorEffects(mod.3, partial.residuals=TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), rows=1, cols=3) ## ----fig-contrived-3b,include=TRUE,fig.width=12,fig.height=4,fig.show='hide'---- plot(Effect(c("x2", "x3"), mod.3, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) ## ----fig-contrived-3c,include=TRUE,fig.width=12,fig.height=4,fig.show='hide'---- plot(Effect(c("x1", "x2"), mod.3, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) ## ------------------------------------------------------------------------ set.seed(682626) Data.4 <- gendata(R = R(0.5), model = expression(x1^2 + x2 * x3)) mod.4 <- lm(y ~ x1 + x2 + x3, data = Data.4) ## ----fig-contrived-4a,include=TRUE,fig.width=12,fig.height=4,fig.show='hide'---- plot(predictorEffects(mod.4, partial.residuals=TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), rows=1, cols=3) ## ----fig-contrived-4b,include=TRUE,fig.width=12,fig.height=4,fig.show='hide'---- plot(Effect(c("x2", "x3"), mod.4, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) ## ----fig-contrived-4c,include=TRUE,fig.width=12,fig.height=4,fig.show='hide'---- plot(Effect(c("x1", "x2"), mod.4, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) ## ----fig-contrived-5a,include=TRUE,fig.width=5,fig.height=4,fig.show='hide'---- mod.5 <- lm(y ~ poly(x1, 2) + x2*x3, data=Data.4) plot(Effect("x1", mod.5, partial.residuals=TRUE), partial.residual=list(pch=".", col="#FF00FF80", span=0.2)) ## ----fig-contrived-5b,include=TRUE,fig.width=12,fig.height=4,fig.show='hide'---- plot(Effect(c("x2", "x3"), mod.5, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1)), span=0.5) ## ----fig-contrived-5c,include=TRUE,fig.width=12,fig.height=4,fig.show='hide'---- plot(Effect(c("x1", "x2"), mod.5, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80", span=0.35), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) effects/inst/CHANGES0000644000176200001440000001147012713174763013657 0ustar liggesusersVersion 0.9-0 initial release to CRAN Version 1.0-0 o Rewrote summary.effect method and added print.summary.effect method. Version 1.0-1 o Blanks can be inserted into or removed from effect names without causing an error; thus, e.g., "poly(education,3)" is equivalent to "poly(education, 3)". o Name spaces of lattice and grid packages are imported, as required in R 1.8.0. Version 1.0-2 o Added ask argument to plot.effect.list, and row, col, nrow, ncol, and more arguments to plot.effect, to support graphing an array of effect plots. o Fixed bug in plot.effect that caused xlab argument to be ignored in certain circumstances. Version 1.0-3 o effect function now works if na.action is na.exclude. Version 1.0-4 o Fixed small bug introduced in version 1.0-3. Version 1.0-5 o x.var and z.var arguments to plot.effect now take names as well as indices. Version 1.0-6 o A variable specified in xlevels can be fixed to a single value. Version 1.0-7 o Made effect() generic, with a method for lm objects that handles glm objects as well. Version 1.0-8 o Small fixes to the help files. Version 1.0-9 o Small change to compile without a warning in R 2.4.0. Version 1.0-10 o Standard errors of effects are computed using t rather than standard-normal distribution for models with estimated dispersion (as suggested by Brian Ripley). o Small fixes. o Objects are now named "eff" and "eff.list" rather than "effect" and "effect.list". o Data sets now provided by lazy data. Version 1.0-11 o Replaced obsolete \non_function{} markup in Rd files (reported by Kurt Hornik). Version 1.0-12 o key.args argument added to plot.eff() (coutesy of Michael Friendly), to allow conrol over, e.g., placement of legend. Version 2.0-0 o Jangman Hong joins project. o support added for multinomial and proportional-odds logit models, as fit by multinom() (in nnet package) and polr() (in MASS) package, following results in Fox and Andersen (2006). o added the argument given.values to effect() methods for finer-grain control of displays. Version 2.0-1 o Fixed bug in effect.polr() that prevented computation for a model with a single term (reported by Paul Prew). Version 2.0-2 o Fixed bug in print(), summary(), and plot() methods for polytomous logit models with a response with numbered levels (reported by Paul Prew). Version 2.0-3 o Fixed bug in all effect() methods that caused error when na.action="na.exclude" (reported by Tracy Lightcap and Rob Goedman). Version 2.0-4 o Palettes from the colorspace package are used by default for stacked plots. o Fixed bug in handling of typical= argument to effect() (argument was effectively ignored). o Added Titanic and Wells data sets. o Small changes. Version 2.0-5 o Added examples for Titanic, BEPS, and WVS data sets. o Arguments ... (e.g., digits) passed through in print() methods. Version 2.0-6 o Fixed small bugs in print.efflist(), summary.efflist(), and plot.effpoly() methods. o Corrected error in missing-data handling that sometimes surfaced in effect.multinom(). o Added .Rd file for package. Version 2.0-7 o Fixed bug in handling of given.values argument to effect(). Version 2.0-8 o The S3 method print.summary.eff is now declared in NAMESPACE (as it should have been all along). o Added CITATION.txt file (courtesy of Achim Zeileis). o Version corresponding to John Fox, Jangman Hong (2009), Effect Displays in R for Multinomial and Proportional-Odds Logit Models: Extensions to the effects Package. Journal of Statistical Software, 32(1), 1-24 . O Fixed [pgk] markup in .Rd file cross-references. Version 2.0-9 o Applied patches contributed by Ian Fellows to allow logical predictors and various coercions in model formulas to work properly. o Fixed name of CITATION file (was CITATION.txt). o Small changes to docs. Version 2.0-10 o Backed out Ian Fellows's patches because of errors. Version 2.0-11 o Small change to eliminate warnings produced in R 2.12.0. o Added nrows and ncols argument to plot.efflist() (following suggstion by Michael Friendly). o Small fix to docs. Version 2.0-12 o plot.eff() and plot.effpoly now return an object, printed by print.plot.eff() (after a question by Michael Friendly). o New effect.gls() method, various changes for compatibility (after a question by Oriol Verdeny Vilalta). o effect.lm() now stores the covariance matrix of the effects (after a question by Bernhard Kaess). Version 2.0-13 o effect.multinom() and effect.polr() now use update() to refit the model rather than calling multinom() or polr() directly; update for effect.multinom() sets trace=FALSE (after suggestions by David Armstrong). o Added [.efflist method (after a question by Andreas Roesch). effects/tests/0000755000176200001440000000000013153263341013034 5ustar liggesuserseffects/tests/effect-tests-1.R0000644000176200001440000001310313153263341015707 0ustar liggesusers if (requireNamespace("carData") && require("effects")){ data(Duncan, package="carData") mi <- with(Duncan, mean(income)) me <- with(Duncan, mean(education)) med <- with(Duncan, median(education)) # (1) focal: factor, constant: polynomial mod.1 <- lm(prestige ~ type + poly(income, degree=2, raw=TRUE), data=Duncan) X <- matrix(c(1, 0, 0, mi, mi^2, 1, 1, 0, mi, mi^2, 1, 0, 1, mi, mi^2), nrow=3, ncol=5, byrow=TRUE) if (!isTRUE(all.equal(as.vector(matrix(X %*% coef(mod.1))), as.vector(Effect("type", mod.1)$fit)))) stop("failed Test 1-1") # (2) focal: polynomial, constant: factor X <- matrix(c(1, 0.4, 2/15, 10, 10^2, 1, 0.4, 2/15, 40, 40^2, 1, 0.4, 2/15, 70, 70^2), nrow=3, ncol=5, byrow=TRUE) if (!isTRUE(all.equal(as.vector(Effect("income", mod.1, xlevels=list(income=c(10, 40, 70)))$fit), as.vector(matrix(X %*% coef(mod.1)))))) stop("failed test 1-2") # (2a) As in (2), but without specifying xlevels X <- matrix(c(1, 0.4, 2/15, 7, 7^2, 1, 0.4, 2/15, 30, 30^2, 1, 0.4, 2/15, 40, 40^2, 1, 0.4, 2/15, 60, 60^2, 1, 0.4, 2/15, 80, 80^2), nrow=5, ncol=5, byrow=TRUE) if (!isTRUE(all.equal(as.vector(Effect("income", mod.1)$fit), as.vector(matrix(X %*% coef(mod.1)))))) stop("failed test 1-2a") # (3) focal: factor*polynomial, constant: polynomial mod.2 <- lm(prestige ~ type*poly(income, degree=2, raw=TRUE) + poly(education, degree=2, raw=TRUE), data=Duncan) X <- matrix(c(1, 0, 0, 10, 10^2, me, me^2, 0, 0, 0, 0, 1, 1, 0, 10, 10^2, me, me^2, 10, 0, 10^2, 0, 1, 0, 1, 10, 10^2, me, me^2, 0, 10, 0, 10^2, 1, 0, 0, 70, 70^2, me, me^2, 0, 0, 0, 0, 1, 1, 0, 70, 70^2, me, me^2, 70, 0, 70^2, 0, 1, 0, 1, 70, 70^2, me, me^2, 0, 70, 0, 70^2), nrow=6, ncol=11, byrow=TRUE) if (!isTRUE(all.equal(as.vector(Effect(c("type", "income"), mod.2, xlevels=list(income=c(10, 70)))$fit), as.vector(matrix(X %*% coef(mod.2), 3, 2))))) stop("failed test 1-3") # (4) focal: polynomial, constant: factor*polynomial X <- matrix(c(1, 0.4, 2/15, mi, mi^2, 10, 10^2, 0.4*mi, 2/15*mi, 0.4*mi^2, 2/15*mi^2, 1, 0.4, 2/15, mi, mi^2, 40, 40^2, 0.4*mi, 2/15*mi, 0.4*mi^2, 2/15*mi^2, 1, 0.4, 2/15, mi, mi^2, 70, 70^2, 0.4*mi, 2/15*mi, 0.4*mi^2, 2/15*mi^2), nrow=3, ncol=11, byrow=TRUE) if (!isTRUE(all.equal(as.vector(Effect("education", mod.2, xlevels=list(education=c(10, 40, 70)))$fit), as.vector(X %*% coef(mod.2))))) stop("failed test 1-4") # (5) repeat of (3) with medians rather than means X <- matrix(c(1, 0, 0, 10, 10^2, med, med^2, 0, 0, 0, 0, 1, 1, 0, 10, 10^2, med, med^2, 10, 0, 10^2, 0, 1, 0, 1, 10, 10^2, med, med^2, 0, 10, 0, 10^2, 1, 0, 0, 70, 70^2, med, med^2, 0, 0, 0, 0, 1, 1, 0, 70, 70^2, med, med^2, 70, 0, 70^2, 0, 1, 0, 1, 70, 70^2, med, med^2, 0, 70, 0, 70^2), nrow=6, ncol=11, byrow=TRUE) if (!isTRUE(all.equal(as.vector(Effect(c("type", "income"), mod.2, xlevels=list(income=c(10, 70)), typical=median)$fit), as.vector(X %*% coef(mod.2))))) stop("failed test 1-5") # (6) focal: factor*polynomial, constant: polynomial, using predict() & orthog. polys. mod.3 <- lm(prestige ~ type*poly(income, degree=2) + poly(education, degree=2), data=Duncan) if (!isTRUE(all.equal(as.vector(predict(mod.3, newdata=data.frame(income=c(10, 10, 10, 70, 70, 70), type=factor(c("bc", "prof", "wc", "bc", "prof", "wc")), education=mean(Duncan$education)))), as.vector(Effect(c("type", "income"), mod.3, xlevels=list(income=c(10, 70)))$fit)))) stop("failed test 1-6") # (7) focal: factor, constant: poly*poly mod.4 <- lm(prestige ~ type + poly(income, 2)*poly(education, 2), data=Duncan) if (!isTRUE(all.equal(as.vector(Effect("type", mod.4)$fit), as.vector(predict(mod.4, newdata=data.frame(type=c("bc", "prof", "wc"), income=rep(mi, 3), education=rep(me, 3))))))) stop("failed test 1-7") # (8) focal: factor, constant: 2nd deg polynomial in 2 Xs mod.5 <- lm(prestige ~ type + poly(income, education, degree=2), data=Duncan) if (!isTRUE(all.equal(as.vector(Effect("type", mod.5)$fit), as.vector(predict(mod.5, newdata=data.frame(type=c("bc", "prof", "wc"), income=rep(mi, 3), education=rep(me, 3))))))) stop("failed test 1-8") # (9) focal: covariate, constant: 2 factors and 1 covariate, 3-way interaction data(Mroz, package="carData") mod.6 <- lm(lwg ~ inc + age*hc*wc, data=Mroz) mage <- with(Mroz, mean(age)) mhc <- with(Mroz, mean(hc == "yes")) mwc <- with(Mroz, mean(wc == "yes")) hc <- rep(mhc, 3) wc <- rep(mwc, 3) age <- rep(mage, 3) X <- cbind(1, c(10, 40, 80), age, hc, wc, age*hc, age*wc, hc*wc, age*hc*wc) if (!isTRUE(all.equal(as.vector(Effect("inc", mod.6, xlevels=list(inc=c(10, 40, 80)))$fit), as.vector(X %*% coef(mod.6))))) stop("failed test 1-8") } effects/tests/effect-tests-2.R0000644000176200001440000001225113153263341015713 0ustar liggesusers if (requireNamespace("carData") && require("effects")){ # plots should show fitted values directly on plotted effect, and must be checked visually # numbering corresponds to effect-test-1.R data(Duncan, package="carData") mod.1 <- lm(prestige ~ type + poly(income, degree=2, raw=TRUE), data=Duncan) # (2) focal: polynomial, constant: factor print(plot(Effect(c("income"), mod.1, partial.residual=TRUE), show.fitted=TRUE)) if (!isTRUE(all.equal(Effect(c("income"), mod.1, partial.residual=TRUE)$fit, Effect(c("income"), mod.1, xlevels=list(income=seq(7, 81, length.out=100)))$fit))) stop("failed test 2 (2)") # (3) focal: factor*polynomial, constant: polynomial mod.2 <- lm(prestige ~ type*poly(income, degree=2, raw=TRUE) + poly(education, degree=2, raw=TRUE), data=Duncan) print(plot(Effect(c("type", "income"), mod.2, partial.residual=TRUE), show.fitted=TRUE)) if (!isTRUE(all.equal(Effect(c("type", "income"), mod.2, partial.residual=TRUE)$fit, Effect(c("type", "income"), mod.2, xlevels=list(income=seq(7, 81, length.out=100)))$fit))) stop("failed test 2 (3)") # (4) focal: polynomial, constant: factor*polynomial print(plot(Effect(c("education"), mod.2, partial.residual=TRUE), show.fitted=TRUE)) if (!isTRUE(all.equal(Effect(c("education"), mod.2, partial.residual=TRUE)$fit, Effect(c("education"), mod.2, xlevels=list(education=seq(7, 100, length.out=100)))$fit))) stop("failed test 2 (4)") # (6) focal: factor*polynomial, constant: polynomial, using predict() & orthog. polys. mod.3 <- lm(prestige ~ type*poly(income, degree=2) + poly(education, degree=2), data=Duncan) print(plot(Effect(c("type", "income"), mod.3, partial.residual=TRUE), show.fitted=TRUE)) if (!isTRUE(all.equal(Effect(c("type", "income"), mod.3, partial.residual=TRUE)$fit, Effect(c("type", "income"), mod.3, xlevels=list(income=seq(7, 81, length.out=100)))$fit))) stop("failed test 2 (6)") # (7) focal: factor, constant: poly*poly mod.4 <- lm(prestige ~ type + poly(income, 2)*poly(education, 2), data=Duncan) print(plot(Effect(c("income", "education"), mod.4, partial.residuals=TRUE), show.fitted=TRUE)) if (!isTRUE(all.equal(Effect(c("income", "education"), mod.4, partial.residuals=TRUE)$fit, Effect(c("income", "education"), mod.4, xlevels=list(income=seq(7, 81, length.out=100), education=quantile(Duncan$education, probs=seq(0.2, 0.8, by=0.2))))$fit))) stop("failed test 2 (7)") # (9) focal: covariate, constant: 2 factors and 1 covariate, 3-way interaction data(Mroz, package="carData") mod.6 <- lm(lwg ~ inc + age*hc*wc, data=Mroz) inc <- range(Mroz$inc) age <- range(Mroz$age) print(plot(Effect(c("inc"), mod.6, partial.residual=TRUE), show.fitted=TRUE)) if (!isTRUE(all.equal(Effect(c("inc"), mod.6, partial.residual=TRUE)$fit, Effect(c("inc"), mod.6, xlevels=list(inc=seq(inc[1], inc[2], length.out=100)))$fit))) stop("failed test 2 (9-1)") print(plot(Effect(c("age", "hc", "wc"), mod.6, partial.residual=TRUE), show.fitted=TRUE)) if (!isTRUE(all.equal(Effect(c("age", "hc", "wc"), mod.6, partial.residual=TRUE)$fit, Effect(c("age", "hc", "wc"), mod.6, xlevels=list(age=seq(age[1], age[2], length.out=100)))$fit))) stop("failed test 2 (9-2)") # additional tests of partial residuals income <- range(na.omit(Prestige)$income) mod.7 <- lm(prestige ~ income*type + education, data=Prestige) print(plot(Effect(c("income", "type"), mod.7, partial.residuals=TRUE), show.fitted=TRUE)) if (!isTRUE(all.equal(Effect(c("income", "type"), mod.7, partial.residuals=TRUE)$fit, Effect(c("income", "type"), mod.7, xlevels=list(income=seq(income[1], income[2], length.out=100)))$fit))) stop("failed test 2 (additional-1)") Mroz2 <- Mroz Mroz2$hc <- as.numeric(Mroz$hc) - 1 Mroz2$wc <- as.numeric(Mroz$wc) - 1 mod.8 <- lm(lwg ~ inc*age*k5 + hc*wc, data=Mroz2) print(plot(Effect(c("inc", "age", "k5"), mod.8, partial.residuals=TRUE, xlevels=list(k5=0:1)), show.fitted=TRUE)) if (!isTRUE(all.equal(Effect(c("inc", "age", "k5"), mod.8, partial.residuals=TRUE, xlevels=list(k5=0:1))$fit, Effect(c("inc", "age", "k5"), mod.8, partial.residuals=TRUE, xlevels=list(k5=0:1, inc=100, age=quantile(Mroz2$age, seq(.2, .8, by=.2))))$fit))) stop("failed test 2 (additional-2)") print(plot(Effect(c("hc", "wc"), mod.8, partial.residuals=TRUE, xlevels=list(hc=0:1, wc=0:1)), show.fitted=TRUE, smooth.residuals=FALSE, residuals.pch=".")) } effects/NAMESPACE0000644000176200001440000000512213155534521013114 0ustar liggesusers# last modified 2017-09-11 by J. Fox import(carData) importFrom(lattice, barchart, current.panel.limits, densityplot, larrows, llines, lpoints, ltext, panel.abline, panel.barchart, #panel.grid, panel.polygon, panel.text, strip.default, trellis.par.get, trellis.par.set, xyplot) importFrom(colorspace, rainbow_hcl, sequential_hcl) importFrom(grid, grid.pretty, grid.segments, unit) importFrom(lme4, fixef) importFrom(nnet, multinom) importFrom(graphics, plot) importFrom(grDevices, gray, palette, rgb) importFrom(survey, svymean) importFrom(stats, as.formula, binomial, coef, coefficients, cov, delete.response, family, fitted, formula, glm, glm.control, lm, lm.fit, loess.smooth, mahalanobis, model.frame, model.matrix, model.offset, model.response, na.exclude, na.omit, nlm, predict, qf, qnorm, qt, quantile, residuals, spline, terms, update, vcov, weights, xtabs) importFrom(utils, menu) export(effect, allEffects, Effect, effectsTheme) export(predictorEffect, predictorEffects) S3method(plot, predictoreff) S3method(plot, predictorefflist) S3method(Effect, default) S3method(Effect, lm) S3method(Effect, mer) S3method(Effect, merMod) S3method(Effect, lme) S3method(Effect, clm2) S3method(Effect, clm) S3method(Effect, clmm) S3method(Effect, gls) S3method(Effect, multinom) S3method(Effect, polr) S3method(Effect, poLCA) S3method(Effect, mlm) S3method(Effect, svyglm) S3method(predictorEffect, default) S3method(predictorEffect, svyglm) S3method(print, eff) S3method(print, efflist) S3method(print, mlm.efflist) S3method(print, summary.eff) S3method(print, predictoreff) S3method(print, predictorefflist) S3method(summary, eff) S3method(summary, efflist) S3method(summary, mlm.efflist) S3method(summary, predictorefflist) S3method(as.data.frame, eff) S3method(as.data.frame, effpoly) S3method(as.data.frame, efflatent) S3method(plot, eff) S3method(print, plot.eff) S3method(plot, efflist) S3method(plot, mlm.efflist) S3method(print, effpoly) S3method(summary, effpoly) S3method(plot, effpoly) S3method(print, efflatent) S3method(summary, efflatent) S3method(allEffects, default) S3method(allEffects, gls) S3method(allEffects, mer) S3method(allEffects, merMod) S3method(allEffects, lme) S3method(allEffects, clm2) S3method(allEffects, clmm) S3method(allEffects, clm) S3method(allEffects, poLCA) S3method(allEffects, mlm) S3method(effect,default) S3method(effect,mer) S3method(effect, merMod) S3method(effect, clm2) S3method(effect, clmm) S3method(effect, clm) S3method(vcov, eff) S3method(`[`, efflist) effects/NEWS0000644000176200001440000002227413156310255012400 0ustar liggesusersVersion 4.0-0 o This is a major update of the effects package. o Moved data sets to the carData package. o Introduced predictor effects. o Reorganized complex arguments to plot() and Effect() into lists; legacy arguments retained as alternatives. o Use lattice theme for plot defaults. o Improve generation of default values for numeric predictors. o Methods for "svyglm" objects. o New vignette on partial residuals with contrived data. o Various small improvements and fixes. Version 3.1-3 o Fixed bug in using multiline=TRUE with effects with 4 or more terms o Fixed a bug in Effect.clmm, Effect.mer, and Effect.lme that caused failure with a data.frame named m o Fixed bug in Effect.clmm and Effect.clmm2 o Improved stability of handling linear and generalized linear mixed effects models from lme4 and nlme o Fixed bug in plot.eff() affecting multiline displays with four or more predictors in the effect. o Fixed warnings (new in R 3.4.0) produced by use of 1 x 1 arrays in computing SEs of effects for multinom and polr model (problem reported by Stefan Th. Gries). Version 3.1-2 o Fixed bug handling 'start' argument in glmm's. Reported by Mariano Devoto; fix by Ben Bolker o Modified internal function make.ticks() so that it doesn't fail due to floating-point inaccuracy (following error reported by Joe Clayton Ford). o Check formula for presence of factor(), etc. (suggestion of Ulrike Gromping). o Fixed bug in Effect.clm() and some other methods (reported by David Barron), which didn't pass ... argument. o A warning is now printed if partial residuals are requested in a multiline plot. o Corrected plotting of partial residuals with various scalings of the y-axis and x-axis. o Added show.strip.values argument to plot.eff() and plot.effpoly(). Version 3.1-1 o Requires R >= 3.2.0 (requested by CRAN). Version 3.1-0 o Corrected and improved computation of partial residuals, fixing bug introduced by bug fix in 3.0-7. Version 3.0-7 o Extends to logistic ordinal response models fit using 'clm' and 'clmm' in the 'ordinal package. o Fixed bug in handling of terms like polynomials in non-focal covariates (reported by Urs Kalbitzer). o Added package tests. Version 3.0-6 o Fix bug in Effect for mer objects with 'poly' in the formula (and related issues). o Allow "||" in variance formulae in lmer4 models. o Minor bug in handling class=="array" in the Effect() method. Version 3.0-5 o Fixed bug when the name of the data frame is the name of function like "sort" in mixed-effects models with lme4 (problem originally reported by Saudi Sadiq). o Fixed bug in predictor-name matching that could occur in names with periods (reported by Trevor Avery). o Fixed package imports to conform to new CRAN rules. o Added residuals.cex argument to plot.eff(). o Changes to work with pbkrtest 0.4-4. Version 3.0-4 o New default of KR=FALSE because KR=TRUE can be very slow. o KR argument now works correctly with allEffects(). o Mixed models with negative binomial did not work and now they do. o Added methods for ordinal mixed models using 'clmm2' for the ordinal package. o Moved pbkrtest to Suggests (wasn't done properly previously). o Tweak to handling key.args (suggestion of Michael Friendly). o Use non-robust loess smooth for partial residuals from non-Gaussian GLMs. o Rationalized type and rescale.axis arguments to plot.eff(); scale.axis argument is deprecated. o Added setStrip() and restoreStrip() to control colors of lattice strips and make beginning and ending conditioning lines clearer. o Added residuals.smooth.color argument to plot.eff(). o Cleaned up sources to conform to CRAN requirements. Version 3.0-3 o Made key.args argument to plot.eff() and plot.effpoly() more flexible (suggestion of Ian Kyle). o Moved pbkrtest package to Suggests and adjusted code for mixed models accordingly, to accomodate Debian (request of Dirk Eddelbuettel). o Fixed \dont-test{} examples. Version 3.0-2 o plot.eff() honors rescale.axis=FALSE when plotting partial residuals (bug reported by Alexander Wietzke). o Effect.mer() can use KR coefficient covariances to construct CIs for effects in LMMs. o Modernized package dependencies/namespace. Version 3.0-1 o Added an argument vcov. to Effect and effect (and allEffects) to select a function for computing the variance covariance matrix of the coefficient estimates. The default is the usual `vcov` fucntion. o Added a warning to the documentation for effect for using predictors of class "dates" or "times". o Fixed bug in Effect.gls() when var or cor function depends on variables in the data set (reported by Felipe Albornoz). o Small fixes/improvements. Version 3.0-0 o Added partial residuals for multidimensional component+residual plots to Effect.lm(). o Small bug fixes. Version 2.3-0 o removed stray character in a the mixed models file o ci.style="bands" is now the default for variates on the horizontal axis and can also be used with multiline=TRUE o Added ci.style='bands', band.transparency, band.colors, and lwd to plot.effpoly() for line plots to give filled confidence bands and control line width o Added Effect.mlm() for multivariate linear models o Interpolating splines are now used by default when drawing lines in effects plots unless the argument use.splines=FALSE o effect() now calls Effect(); all effect() methods are removed, but effect() will continue to work as before. o Various methods for effect objects now handle factors with a "valid" NA level (fixing bug reported by Joseph Larmarange). o Further bug fixes in effects.mer() and effects.lme() (following bug report by Felipe E. Albornoz). Version 2.2-6 o bug fixes in effects.mer and effects.lme. o added terms.gls() to make effect.gls() and Effect.gls() work again. o plot.eff() gains an lwd= option to control the width of fitted lines. o Added ci.style='bands' and alpha= to plot.eff() for non-multiline plots to give filled confidence bands. Version 2.2-5 o Added support for polytomous latent class analysis based on the poLCA package. o Modified mixed-model methods to all use in user-functions. o Changed the default method for determining number of levels for a continuous predictor; see help page for 'effect' and discussion of the 'xlevels' argument for details. Argument 'default.levels', while still included for compatibility, is depricated. o Added .merMod methods for development version of lme4. o Added support for lme4.0. o Fixed bug preventing restoration of warn option (reported by Kenneth Knoblauch). o Fixed handling of ticks.x argument to plot.eff() and plot.effpoly(), now works as advertized. o Adjusted package dependencies, imports for CRAN checks. o Changed name of Titanic dataset to TitanicSurvival to avoid name clash (request of Michael Friendly). o Minor fixes. Version 2.2-4 o Add argument 'ci.style' to plot.eff() and plot.eff() to allow confidence intervals to be displayed as lines or using error bars. Confidence bars are permitted on multiline plots (after suggestion by Steve Taylor). o Allow empty cells with crossed factors for lm, glm and multinom. o Added warning about logical predictors (suggestion of Kazuki Yoshida). Version 2.2-3 o Fixed bugs in axis scaling and xlim specification (reported by Achim Zeileis). o Small changes for compatability with R 2.16.0. Version 2.2-2 o Use asymptotic normal to get confidence limits for mer and lme objects o Correct effects.lme to work with weights o Added Effect.mer(), Effect.lme(), Effect.gls(), Effect.multinom(), and Effect.polr() methods. o Safe predictions simplified in effect.multinom() and effect.polr(). o plot() methods for eff and effpoly objects permit predictor transformations. o Added as.data.frame.eff(), as.data.frame.effpoly(), and as.data.frame.efflatent (suggestion of Steve Taylor). o Small bug fixes. Version 2.2-1 o Some examples wrapped in \donttest{} to decrease package check time. Version 2.2-0 o Introduced more flexible Effect() generic, along with Effect.lm() method for linear and generalized linear models. o Default is now ask=FALSE for plot.efflist(). o globalVariables("wt") declared for R 2.15.1 and above. o Small bug fixes. Version 2.1-2 o Offsets for linear and generalized linear (and mixed) models are now supported. o cbind(successes, failures) now supported for binomial generalized linear (and mixed) models. Version 2.1-1 o plot.effpoly() now honors ylim argument when no confidence intervals are plotted (fixing problem reported by Achim Zeileis). o safe predictions simplified in effect.lm(), producing correct results for mixed models (other methods to follow). o plot.eff() now honors type argument. o nlme and lme4 moved to Suggests. o effect() now works when options(OutDec= ',') (suggestion of Guomundur Arnkelsson). Version 2.1-0 o added support for 'mer' objects from lme4 and 'lme' objects from 'nlme'. Added 'rotx', 'roty' and 'grid' arguments to the plot methods. o See CHANGES file for changes to older versions. effects/R/0000755000176200001440000000000013156515040012072 5ustar liggesuserseffects/R/effectspoLCA.R0000644000176200001440000000341013156517176014525 0ustar liggesusers# 2013-07-31: extend effects to poLCA objects. S. Weisberg # 2013-10-15: removed effect.poLCA. J. Fox #The next two functions should be exported to the namespace allEffects.poLCA <- function(mod, ...){ allEffects(poLCA.to.fake(mod), ...) } Effect.poLCA <- function(focal.predictors, mod, ...) { result <- Effect(focal.predictors, poLCA.to.fake(mod), ...) result$formula <- as.formula(formula(mod)) result } # this function makes a 'fake' multinom object or 'glm' object so # effect.mulitnom or effect.glm can be used. # effect.multinom requires at least 3 classes, so if classes=2 use # effect.glm poLCA.to.fake <- function(mod) { dta <- eval(mod$call$data) form <- as.formula(eval(mod$call$formula)) # find the missing data: omit <- attr(model.frame(form, dta), "na.action") if(length(omit) == 0) dta$.class <- factor(mod$predclass) else{ dta$.class <- rep(NA, dim(dta)[1]) dta$.class[-omit] <- mod$predclass dta$.class <- factor(dta$.class) } # end of missing data correction formula1 <- update(form, .class ~ .) if(length(mod$P) == 2L){ mod1 <- glm(formula1, family=binomial, data=dta) mod1$call$data <- dta mod1$call$formula <- formula1 mod1$coef <- mod$coeff[, 1] mod1$vcov <- mod$coeff.V class(mod1) <- c("fakeglm", class(mod1)) } else { mod1 <- multinom(formula1, dta, Hess=TRUE, trace=FALSE, maxit=1) mod1$call$data <- dta mod1$call$formula <- formula1 mod1$coeff <- mod$coeff mod1$coeff.V <- mod$coeff.V class(mod1) <- c("fakemultinom", class(mod1)) } mod1 } coef.fakemultinom <- function(mod){ coef <- t(mod$coeff) dimnames(coef) <- list(mod$lab[-1L], mod$vcoefnames) coef } vcov.fakemultinom <- function(mod){mod$coeff.V} effects/R/effectsclmm.R0000644000176200001440000001362313156517111014513 0ustar liggesusers# 2014-12-11 Effects plots for ordinal and ordinal mixed models from the 'ordinal' package # 2014-12-11 effect.clm built from effect.mer as modified 2014-12-07, by S. Weisberg # 2015-06-10: requireNamespace("MASS") rather than require("MASS") # 2016-02-12: added support for clmm and clm objects from 'ordinal' S. Weisberg # 2016-08-16: added ... argument to effect() and Effect() methods. J. Fox # 2017-03-28: fixed bug to allow data=m S. Weisberg ### ### clm2 ### clm2.to.polr <- function(mod) { if (requireNamespace("MASS", quietly=TRUE)){ polr <- MASS::polr } else stop("The MASS package is needed for this function") cl <- mod$call present <- match(c("scale", "nominal", "link", "threshold"), names(cl), 0L) if(any(present != 0)) { if(present[3] != 0){if(cl$link != "logit") stop("'link' must be 'logisitic' for use with effects")} if(present[4] != 0){if(cl$threshold != "flexible") stop("'threshold' must be 'flexible' for use with effects")} if(present[1] != 0){if(!is.null(cl$scale)) stop("'scale' must be NULL for use with effects")} if(present[2] != 0){if(!is.null(cl$nominal)) stop("'nominal' must be NULL for use with effects")} } if(is.null(mod$Hessian)){ message("\nRe-fitting to get Hessian\n") mod <- update(mod, Hess=TRUE) } cl$formula <- cl$location cl$method <- cl$link .m <- match(c("formula", "data", "subset","weights", "na.action", "contrasts", "method"), names(cl), 0L) cl <- cl[c(1L, .m)] cl$start <- c(mod$beta, mod$Theta) cl[[1L]] <- as.name("polr") mod2 <- eval(cl) mod2$coefficients <- mod$beta # get vcov numTheta <- length(mod$Theta) numBeta <- length(mod$beta) or <- c( (numTheta+1):(numTheta + numBeta), 1:(numTheta)) mod2$vcov <- as.matrix(vcov(mod)[or, or]) class(mod2) <- c("fakeclm2", class(mod2)) mod2 } #method for 'fakeglm' objects. Do not export vcov.fakeclm2 <- function(object, ...) object$vcov #The next three functions should be exported effect.clm2 <- function(term, mod, ...) { effect(term, clm.to.polr(mod), ...) } allEffects.clm2 <- function(mod, ...){ allEffects(clm.to.polr(mod), ...) } Effect.clm2 <- function(focal.predictors, mod, ...){ Effect(focal.predictors, clm.to.polr(mod), ...) } ### ### clmm ### clmm.to.polr <- function(mod) { if (requireNamespace("MASS", quietly=TRUE)){ polr <- MASS::polr } else stop("The MASS package is needed for this function") cl <- mod$call present <- match(c("scale", "nominal", "link", "threshold"), names(cl), 0L) if(any(present != 0)) { if(present[3] != 0){if(cl$link != "logit") stop("'link' must be 'logit' for use with effects")} if(present[4] != 0){if(cl$threshold != "flexible") stop("'threshold' must be 'flexible' for use with effects")} if(present[1] != 0){if(!is.null(cl$scale)) stop("'scale' must be NULL for use with effects")} if(present[2] != 0){if(!is.null(cl$nominal)) stop("'nominal' must be NULL for use with effects")} } if(is.null(mod$Hessian)){ message("\nRe-fitting to get Hessian\n") mod <- update(mod, Hess=TRUE) } cl$formula <- fixmod(mod$formula) # changed for clm2 cl$method <- cl$link .m <- match(c("formula", "data", "subset","weights", "na.action", "contrasts", "method"), names(cl), 0L) cl <- cl[c(1L, .m)] cl$start <- c(mod$beta, mod$Theta) cl[[1L]] <- as.name("polr") mod2 <- eval(cl) mod2$coefficients <- mod$beta # get vcov numTheta <- length(mod$Theta) numBeta <- length(mod$beta) or <- c( (numTheta+1):(numTheta + numBeta), 1:(numTheta)) mod2$vcov <- as.matrix(vcov(mod)[or, or]) class(mod2) <- c("fakeclmm", class(mod2)) mod2 } #method for 'fakeglm' objects. Do not export vcov.fakeclmm <- function(object, ...) object$vcov #The next three functions should be exported effect.clmm <- function(term, mod, ...) { effect(term, clmm.to.polr(mod), ...) } allEffects.clmm <- function(mod, ...){ allEffects(clmm.to.polr(mod), ...) } Effect.clmm <- function(focal.predictors, mod, ...){ Effect(focal.predictors, clmm.to.polr(mod), ...) } ### ### clm ### clm.to.polr <- function(mod) { if (requireNamespace("MASS", quietly=TRUE)){ polr <- MASS::polr } else stop("The MASS package is needed for this function") cl <- mod$call present <- match(c("scale", "nominal", "link", "threshold"), names(cl), 0L) if(any(present != 0)) { if(present[3] != 0){if(cl$link != "logit") stop("'link' must be 'logit' for use with effects")} if(present[4] != 0){if(cl$threshold != "flexible") stop("'threshold' must be 'flexible' for use with effects")} if(present[1] != 0){if(!is.null(cl$scale)) stop("'scale' must be NULL for use with effects")} if(present[2] != 0){if(!is.null(cl$nominal)) stop("'nominal' must be NULL for use with effects")} } if(is.null(mod$Hessian)){ message("\nRe-fitting to get Hessian\n") mod <- update(mod, Hess=TRUE) } # cl$formula <- cl$location cl$method <- cl$link .m <- match(c("formula", "data", "subset","weights", "na.action", "contrasts", "method"), names(cl), 0L) cl <- cl[c(1L, .m)] cl$start <- c(mod$beta, mod$Theta) cl[[1L]] <- as.name("polr") mod2 <- eval(cl) mod2$coefficients <- mod$beta # get vcov numTheta <- length(mod$Theta) numBeta <- length(mod$beta) or <- c( (numTheta+1):(numTheta + numBeta), 1:(numTheta)) mod2$vcov <- as.matrix(vcov(mod)[or, or]) class(mod2) <- c("fakeclm", class(mod2)) mod2 } #method for 'fakeglm' objects. Do not export vcov.fakeclm <- function(object, ...) object$vcov #The next three functions should be exported effect.clm <- function(term, mod, ...) { effect(term, clm.to.polr(mod), ...) } allEffects.clm <- function(mod, ...){ allEffects(clm.to.polr(mod), ...) } Effect.clm <- function(focal.predictors, mod, ...){ Effect(focal.predictors, clm.to.polr(mod), ...) } effects/R/effectsmer.R0000644000176200001440000001715213156517145014356 0ustar liggesusers# effect.mer and effect.lme built from effect.lm by S. Weisberg 29 June 2011 # 2012-03-08 to require() lme4 or nlme. J. Fox # 2012-10-05 effect.lme didn't work with 'weights', now corrected. S. Weisberg # 2013-03-05: introduced merMod methods for development version of lme4. J. Fox # 2013-04-06: added support for lme4.0, J. Fox # 2013-07-30: added 'data' argument to lme.to.glm and mer.to.glm to allow # calling effect from within a subroutine. # 2013-09-25: removed the 'data' argument as it makes the functions fail with # logs, splines and polynomials # 2014-09-24: added option for KR cov matrix to mer.to.glm(). J. Fox # 2014-12-07: don't assume that pbkrtest is installed. J. Fox # 2014-12-20: mer.to.glm failed for negative.binomial() because the link has an argument # that was handled incorrectly by the family.glmResp function. This function is no longer # used by mer.to.glm. The same error will recur in any link with an argument. # 2015-06-10: requireNamespace("pbkrtest") rather than require("pbkrtest) # 2015-07-02: fixed bug when the name of the data frame was the name of a function (e.g., sort, or lm) # 2015-12-13: make it work with pbkrtest 0.4-3. J. Fox # 2016-01-07: modified 'fixmod' to allow "||" in variance formulae # 2016-01-19: Fixed bug in glm.to.mer when 'poly' is used in a model. # 2016-06-08: Fixed bug handling the 'start' argument in glm.to.mer. Fix by Ben Bolker, bug from Mariano Devoto # 2016-11-18: Change to mer.to.glm and lme.to.glm for stability in unusual glms. By Nate TeGrotenhuis. # 2017-03-28: in mer.to.glm, changed a name from m to .m. The fake glm is created from the mer model's # call slot, which included the name of the data.frame, if any. A data.frame named 'm' # therefore did not work. Same bug fixed in lme.to.glm # the function lm.wfit gets the hessian wrong for mer's. Get the variance # from the vcov method applied to the mer object. fixmod <- function (term) { if (!("|" %in% all.names(term)) && !("||" %in% all.names(term))) return(term) if ((is.call(term) && term[[1]] == as.name("|")) || (is.call(term) && term[[1]] == as.name("||"))) return(NULL) if (length(term) == 2) { nb <- fixmod(term[[2]]) if (is.null(nb)) return(NULL) term[[2]] <- nb return(term) } nb2 <- fixmod(term[[2]]) nb3 <- fixmod(term[[3]]) if (is.null(nb2)) return(nb3) if (is.null(nb3)) return(nb2) term[[2]] <- nb2 term[[3]] <- nb3 term } # lme.to.glm evaluates a 'glm' model that is as similar to a given 'lme' # model, in the same pattern as mer.to.glm. This could be speeded up # slightly by using 'lm' rather than 'glm' but I use 'glm' to parallel # mer.to.glm more closely. The differences are: (1) match fewer args # in the call; (2) different def of mod2$coefficients; no other # changes # The argument 'data' to lme.to.glm and to mer.to.glm copies the data # from the object into the local environment and makes it visible when 'effect' # is called from within another function. lme.to.glm <- function(mod) { cl <- mod$call cl$formula <- cl$fixed cl$control <- glm.control(epsilon=1) # suggested by Nate TeGrotenhuis .m <- match(c("formula", "data", "subset", "na.action", "contrasts"), names(cl), 0L) cl <- cl[c(1L, .m)] cl[[1L]] <- as.name("glm") mod2 <- eval(cl) pw <- attr(mod$modelStruct$varStruct, "weights") if(!is.null(pw)) mod2$prior.weights <- pw mod2$coefficients <- mod$coefficients$fixed mod2$vcov <- as.matrix(vcov(mod)) mod2$linear.predictors <- model.matrix(mod2) %*% mod2$coefficients mod2$fitted.values <- mod2$family$linkinv(mod2$linear.predictors) mod2$weights <- as.vector(with(mod2, prior.weights * (family$mu.eta(linear.predictors)^2 / family$variance(fitted.values)))) mod2$residuals <- with(mod2, prior.weights * (y - fitted.values)/weights ) class(mod2) <- c("fakeglm", class(mod2)) mod2 } # mer.to.glm evaluates a 'glm' model that is as similar to a given 'mer' # model as possible. It is of class c("fakeglm", "glm", "lm") # several items are added to the created objects. Do not export mer.to.glm <- function(mod, KR=FALSE) { if (KR && !requireNamespace("pbkrtest", quietly=TRUE)){ KR <- FALSE warning("pbkrtest is not available, KR set to FALSE") } # object$family$family doesn't work correctly with the negative binomial family because of the # argument in the family function, so the old line # family <- family(mod) # returns an error message for these models. The following kluge fixes this. # If this bug is fixed in lme4, this code may break because it expects resp$family$family # to return "Link Name(arg)" with ONE argument, and so spaces between Name and "(arg)" family1 <- function(object, ...) {UseMethod("family1", object@resp)} family1.lmResp <- function(object, ...) family(object, ...) family1.glmResp <- function(object, ...){ famname <- object@resp$family$family open.paren <- regexpr("\\(", famname) if(open.paren==-1) { name <- famname arg <- list() } else { name <- sub(" ", ".", tolower(substr(famname, 1, -1 + open.paren))) arg <- list(as.numeric(gsub("\\)", "", substr(famname, 1 + open.paren, 100)))) } if(is.null(object@resp$family$initialize)) do.call(name, arg) else object@resp$family } family <- family1(mod) # end link <- family$link family <- family$family cl <- mod@call cl$control <- glm.control(epsilon=1) # suggested by Nate TeGrotenhuis if(cl[[1]] =="nlmer") stop("effects package does not support 'nlmer' objects") .m <- match(c("formula", "family", "data", "weights", "subset", "na.action", "offset", "model", "contrasts"), names(cl), 0L) cl <- cl[c(1L, .m)] cl[[1L]] <- as.name("glm") cl$formula <- fixmod(as.formula(cl$formula)) # cl$data <- mod@frame # caused bug with a 'poly' in the formula mod2 <- eval(cl) mod2$coefficients <- lme4::fixef(mod) #mod@fixef mod2$vcov <- if (family == "gaussian" && link == "identity" && KR) as.matrix(pbkrtest::vcovAdj(mod)) else as.matrix(vcov(mod)) mod2$linear.predictors <- model.matrix(mod2) %*% mod2$coefficients mod2$fitted.values <- mod2$family$linkinv(mod2$linear.predictors) mod2$weights <- as.vector(with(mod2, prior.weights * (family$mu.eta(linear.predictors)^2 / family$variance(fitted.values)))) mod2$residuals <- with(mod2, prior.weights * (y - fitted.values)/weights ) class(mod2) <- c("fakeglm", class(mod2)) mod2 } #method for 'fakeglm' objects. Do not export vcov.fakeglm <- function(object, ...) object$vcov #The next six functions should be exported as S3 methods effect.mer <- function(term, mod, vcov.=vcov, KR=FALSE, ...) { result <- effect(term, mer.to.glm(mod, KR=KR), vcov., ...) result$formula <- as.formula(formula(mod)) result } effect.merMod <- function(term, mod, vcov.=vcov, KR=FALSE, ...){ effect.mer(term, mod, vcov.=vcov, KR=KR, ...) } effect.lme <- function(term, mod, ...) { mod1 <- lme.to.glm(mod) result <- effect(term, mod1) result$formula <- as.formula(formula(mod)) result } allEffects.mer <- function(mod, KR=FALSE,...){ allEffects(mer.to.glm(mod,KR=KR), ...) } allEffects.merMod <- function(mod, KR=FALSE,...){ allEffects(mer.to.glm(mod,KR=KR), ...) } allEffects.lme <- function(mod, ...){ allEffects(lme.to.glm(mod), ...) } effects/R/plot.effpoly.R0000644000176200001440000012514513156517415014656 0ustar liggesusers# Plot method for effpoly objects # modified by Michael Friendly: added ci.style="bands" & alpha.band= arg # modified by Michael Friendly: added lwd= argument for llines (was lwd=2) # 2013-11-06: fixed drop dimension when only one focal predictor. John # 2014-10-10: namespace fixes. John # 2014-12-05: made key.args more flexible. John # 2014-03-22: use wide columns by default only when x for legend not set. J. Fox # 2016-09-08: added show.strip.values argument to plot.effpoly(). J. Fox # 2017-08-16: modified plot.effpoly() to consolidate arguments and use lattice theme. J. Fox # 2017-08-20: reintroduce legacy arguments for plot.effpoly() # 2017-08-20: introduced multiline argument under lines argument and as a "legacy" argument # 2017-09-10: use replacement for grid.panel() plot.effpoly <- function(x, x.var=which.max(levels), main=paste(effect, "effect plot"), symbols=TRUE, lines=TRUE, axes, confint, lattice, ..., # legacy arguments: type, multiline, rug, xlab, ylab, colors, cex, lty, lwd, factor.names, show.strip.values, ci.style, band.colors, band.transparency, style, transform.x, ticks.x, xlim, ticks, ylim, rotx, roty, alternating, grid, layout, key.args, use.splines){ if (!is.logical(lines) && !is.list(lines)) lines <- list(lty=lines) lines <- applyDefaults(lines, defaults=list(lty=trellis.par.get("superpose.line")$lty, lwd=trellis.par.get("superpose.line")$lwd[1], col=NULL, splines=TRUE, multiline=NULL), arg="lines") if (missing(multiline)) multiline <- lines$multiline if (missing(lwd)) lwd <- lines$lwd if (missing(use.splines)) use.splines <- lines$splines lines.col <- lines$col lines <- if (missing(lty)) lines$lty else lty if (!is.logical(symbols) && !is.list(symbols)) symbols <- list(pch=symbols) symbols <- applyDefaults(symbols, defaults= list(pch=trellis.par.get("superpose.symbol")$pch, cex=trellis.par.get("superpose.symbol")$cex[1]), arg="symbols") cex <- symbols$cex symbols <- symbols$pch if (missing(axes)) axes <- NULL axes <- applyDefaults(axes, defaults=list( x=list(rotate=0, rug=TRUE), y=list(lab=NULL, lim=c(NA, NA), ticks=list(at=NULL, n=5), type="probability", rotate=0), alternating=TRUE, grid=FALSE), arg="axes") x.args <- applyDefaults(axes$x, defaults=list(rotate=0, rug=TRUE), arg="axes$x") if (missing(xlab)) { xlab.arg <- FALSE xlab <- list() } if (missing(xlim)) { xlim.arg <- FALSE xlim <- list() } if (missing(ticks.x)) { ticks.x.arg <- FALSE ticks.x <- list() } if (missing(transform.x)) { transform.x.arg <- FALSE transform.x <- list() } if (missing(rotx)) rotx <- x.args$rotate if (missing(rug)) rug <- x.args$rug rotx <- x.args$rotate rug <- x.args$rug x.args$rotate <- NULL x.args$rug <- NULL x.pred.names <- names(x.args) if (length(x.pred.names) > 0){ for (pred.name in x.pred.names){ x.pred.args <- applyDefaults(x.args[[pred.name]], defaults=list(lab=NULL, lim=NULL, ticks=NULL, transform=NULL), arg=paste0("axes$x$", pred.name)) if (!xlab.arg) xlab[[pred.name]] <- x.pred.args$lab if (!xlim.arg) xlim[[pred.name]] <- x.pred.args$lim if (!ticks.x.arg) ticks.x[[pred.name]] <- x.pred.args$ticks if (!transform.x.arg) transform.x[[pred.name]] <- x.pred.args$transform } } if (length(xlab) == 0) xlab <- NULL if (length(xlim) == 0) xlim <- NULL if (length(ticks.x) == 0) ticks.x <- NULL if (length(transform.x) == 0) transform.x <- NULL y.args <- applyDefaults(axes$y, defaults=list(lab=NULL, lim=c(NA, NA), ticks=list(at=NULL, n=5), type="probability", style="lines", rotate=0), arg="axes$y") if (missing(ylim)) ylim <- y.args$lim if (missing(ticks)) ticks <- y.args$ticks if (missing(type)) type <- y.args$type type <- match.arg(type, c("probability", "logit")) if (missing(ylab)) ylab <- y.args$lab if (is.null(ylab)) ylab <- paste0(x$response, " (", type, ")") if (missing(roty)) roty <- y.args$rotate if (missing(alternating)) alternating <- axes$alternating if (missing(grid)) grid <- axes$grid if (missing(style)) style <- match.arg(y.args$style, c("lines", "stacked")) if (missing(colors)) colors <- if (is.null(lines.col)){ if (style == "lines" || x$model == "multinom") trellis.par.get("superpose.line")$col else sequential_hcl(length(x$y.levels)) } else { lines.col } if (missing(confint)) confint <- NULL confint <- applyDefaults(confint, defaults=list(style=if (style == "lines" && !is.null(x$se.prob)) "bands" else "none", alpha=0.15, col=colors), onFALSE=list(style="none", alpha=0, col="white"), arg="confint") if (missing(ci.style)) ci.style <- confint$style if (missing(band.transparency)) band.transparency <- confint$alpha if (missing(band.colors)) band.colors <- confint$col if(!is.null(ci.style)) ci.style <- match.arg(ci.style, c("auto", "bars", "lines", "bands", "none")) confint <- confint$style != "none" if (is.null(multiline)) multiline <- if (confint) FALSE else TRUE effect.llines <- llines has.se <- !is.null(x$confidence.level) if (confint && !has.se) stop("there are no confidence limits to plot") if (style == "stacked"){ if (type != "probability"){ type <- "probability" warning('type set to "probability" for stacked plot') } if (confint){ confint <- FALSE warning('confint set to FALSE for stacked plot') } ylim <- c(0, 1) } if (missing(lattice)) lattice <- NULL lattice <- applyDefaults(lattice, defaults=list( layout=NULL, key.args=NULL, strip=list(factor.names=TRUE, values=TRUE), array=list(row=1, col=1, nrow=1, ncol=1, more=FALSE), arg="lattice" )) if (missing(layout)) layout <- lattice$layout if (missing(key.args)) key.args <- lattice$key.args strip.args <- applyDefaults(lattice$strip, defaults=list(factor.names=TRUE, values=TRUE), arg="lattice$strip") factor.names <- strip.args$factor.names if (missing(show.strip.values)) show.strip.values <- strip.args$values array.args <- applyDefaults(lattice$array, defaults=list(row=1, col=1, nrow=1, ncol=1, more=FALSE), arg="lattice$array") row <- array.args$row col <- array.args$col nrow <- array.args$nrow ncol <- array.args$ncol more <- array.args$more .mod <- function(a, b) ifelse( (d <- a %% b) == 0, b, d) .modc <- function(a) .mod(a, length(colors)) .mods <- function(a) .mod(a, length(symbols)) .modl <- function(a) .mod(a, length(lines)) effect <- paste(sapply(x$variables, "[[", "name"), collapse="*") split <- c(col, row, ncol, nrow) n.predictors <- length(names(x$x)) y.lev <- x$y.lev n.y.lev <- length(y.lev) ylevel.names <- make.names(paste("prob",y.lev)) colnames(x$prob) <- colnames(x$logit) <- ylevel.names if (has.se){ colnames(x$lower.logit) <- colnames(x$upper.logit) <- colnames(x$lower.prob) <- colnames(x$upper.prob)<- ylevel.names } x.frame <-as.data.frame(x) predictors <- names(x.frame)[1:n.predictors] levels <- if (n.predictors==1) length (x.frame[,predictors]) else sapply(apply(x.frame[, predictors, drop=FALSE], 2, unique), length) if (is.character(x.var)) { which.x <- which(x.var == predictors) if (length(which.x) == 0) stop(paste("x.var = '", x.var, "' is not in the effect.", sep="")) x.var <- which.x } x.vals <- x.frame[, names(x.frame)[x.var]] response <- matrix(0, nrow=nrow(x.frame), ncol=n.y.lev) for (i in 1:length(x$y.lev)){ level <- which(colnames(x$prob)[i] == ylevel.names) response[,i] <- rep(x$y.lev[level], length(response[,i])) } prob <- as.vector(x$prob) logit <- as.vector(x$logit) response <- as.vector(response) if (has.se){ lower.prob <- as.vector(x$lower.prob) upper.prob <- as.vector(x$upper.prob) lower.logit <- as.vector(x$lower.logit) upper.logit <- as.vector(x$upper.logit) } response <- factor(response, levels=y.lev) Data <- data.frame(prob, logit) if (has.se) Data <- cbind(Data, data.frame(lower.prob, upper.prob, lower.logit, upper.logit)) Data[[x$response]] <- response for (i in 1:length(predictors)){ Data <- cbind(Data, x.frame[predictors[i]]) } levs <- levels(x$data[[predictors[x.var]]]) n.predictor.cats <- sapply(Data[, predictors[-c(x.var)], drop=FALSE], function(x) length(unique(x))) if (length(n.predictor.cats) == 0) n.predictor.cats <- 1 ci.style <- if(is.null(ci.style) || ci.style == "auto") { if(is.factor(x$data[[predictors[x.var]]])) "bars" else "bands"} else ci.style if( ci.style=="none" ) confint <- FALSE ### no confidence intervals if confint == FALSE or ci.style=="none" if (!confint){ # plot without confidence bands if (style == "lines"){ # line plot if (!multiline){ layout <- if(is.null(layout)) c(prod(n.predictor.cats), length(levels(response)), 1) else layout ### factor if (is.factor(x$data[[predictors[x.var]]])){ # x-variable a factor range <- if (type=="probability") range(prob, na.rm=TRUE) else range(logit, na.rm=TRUE) ylim <- if (!any(is.na(ylim))) ylim else c(range[1] - .025*(range[2] - range[1]), range[2] + .025*(range[2] - range[1])) tickmarks <- make.ticks(ylim, link=I, inverse=I, at=ticks$at, n=ticks$n) levs <- levels(x$data[[predictors[x.var]]]) if (show.strip.values){ for (pred in predictors[-x.var]){ Data[[pred]] <- as.factor(Data[[pred]]) } } result <- xyplot(eval(if (type=="probability") parse(text=if (n.predictors==1) paste("prob ~ as.numeric(", predictors[x.var],") |", x$response) else paste("prob ~ as.numeric(", predictors[x.var],") |", paste(predictors[-x.var], collapse="*"), paste("*", x$response))) else parse(text=if (n.predictors==1) paste("logit ~ as.numeric(", predictors[x.var],") |", x$response) else paste("logit ~ as.numeric(", predictors[x.var],")|", paste(predictors[-x.var], collapse="*"), paste("*", x$response)))), par.strip.text=list(cex=0.8), strip=function(...) strip.default(..., strip.names=c(factor.names, TRUE), sep=" = "), panel=function(x, y, subscripts, x.vals, rug, ... ){ if (grid) ticksGrid(x=1:length(levs), y=tickmarks$at) good <- !is.na(y) effect.llines(x[good], y[good], lwd=lwd, type="b", pch=19, col=colors[1], cex=cex, ...) subs <- subscripts+as.numeric(rownames(Data)[1])-1 }, ylab=ylab, ylim=if (is.null(ylim)) if (type == "probability") range(prob) else range(logit) else ylim, xlab=if (is.null(xlab)) predictors[x.var] else xlab, main=main, x.vals=x$data[[predictors[x.var]]], rug=rug, scales=list(x=list(at=1:length(levs), labels=levs, rot=rotx), y=list(at=tickmarks$at, labels=tickmarks$labels, rot=roty), alternating=alternating), layout=layout, data=Data, ...) result$split <- split result$more <- more class(result) <- c("plot.eff", class(result)) } else { # x-variable numeric if(use.splines) effect.llines <- spline.llines # added 10/17/13 range <- if (type=="probability") range(prob, na.rm=TRUE) else range(logit, na.rm=TRUE) ylim <- if (!any(is.na(ylim))) ylim else c(range[1] - .025*(range[2] - range[1]), range[2] + .025*(range[2] - range[1])) tickmarks <- make.ticks(ylim, link=I, inverse=I, at=ticks$at, n=ticks$n) nm <- predictors[x.var] x.vals <- x$data[[nm]] if (nm %in% names(ticks.x)){ at <- ticks.x[[nm]]$at n <- ticks.x[[nm]]$n } else{ at <- NULL n <- 5 } xlm <- if (nm %in% names(xlim)){ xlim[[nm]] } else range.adj(Data[nm]) # range(x.vals) tickmarks.x <- if ((nm %in% names(transform.x)) && !(is.null(transform.x))){ trans <- transform.x[[nm]]$trans make.ticks(trans(xlm), link=transform.x[[nm]]$trans, inverse=transform.x[[nm]]$inverse, at=at, n=n) } else { trans <- I make.ticks(xlm, link=I, inverse=I, at=at, n=n) } if (show.strip.values){ for (pred in predictors[-x.var]){ Data[[pred]] <- as.factor(Data[[pred]]) } } result <- xyplot(eval(if (type=="probability") parse(text=if (n.predictors==1) paste("prob ~ trans(", predictors[x.var],") |", x$response) else paste("prob ~ trans(", predictors[x.var],") |", paste(predictors[-x.var], collapse="*"), paste("*", x$response))) else parse(text=if (n.predictors==1) paste("logit ~ trans(", predictors[x.var],") |", x$response) else paste("logit ~ trans(", predictors[x.var],") |", paste(predictors[-x.var], collapse="*"), paste("*", x$response))) ), par.strip.text=list(cex=0.8), strip=function(...) strip.default(..., strip.names=c(factor.names, TRUE), sep=" = "), panel=function(x, y, subscripts, x.vals, rug, ... ){ if (grid) ticksGrid(x=tickmarks.x$at, y=tickmarks$at) if (rug) lrug(trans(x.vals)) good <- !is.na(y) effect.llines(x[good], y[good], lwd=lwd, col=colors[1], ...) subs <- subscripts+as.numeric(rownames(Data)[1])-1 }, ylab=ylab, xlim=suppressWarnings(trans(xlm)), ylim= if (is.null(ylim)) if (type == "probability") range(prob) else range(logit) else ylim, xlab=if (is.null(xlab)) predictors[x.var] else xlab, main=main, x.vals=x$data[[predictors[x.var]]], rug=rug, scales=list(y=list(at=tickmarks$at, labels=tickmarks$labels, rot=roty), x=list(at=tickmarks.x$at, labels=tickmarks.x$labels, rot=rotx), alternating=alternating), layout=layout, data=Data, ...) result$split <- split result$more <- more class(result) <- c("plot.eff", class(result)) } } else { layout <- if (is.null(layout)){ lay <- c(prod(n.predictor.cats[-(n.predictors - 1)]), prod(n.predictor.cats[(n.predictors - 1)]), 1) if (lay[1] > 1) lay else lay[c(2, 1, 3)] } else layout if (n.y.lev > min(c(length(colors), length(lines), length(symbols)))) warning('Colors, lines and symbols may have been recycled') range <- if (type=="probability") range(prob, na.rm=TRUE) else range(logit, na.rm=TRUE) ylim <- if (!any(is.na(ylim))) ylim else c(range[1] - .025*(range[2] - range[1]), range[2] + .025*(range[2] - range[1])) tickmarks <- make.ticks(ylim, link=I, inverse=I, at=ticks$at, n=ticks$n) if (is.factor(x$data[[predictors[x.var]]])){ # x-variable a factor key <- list(title=x$response, cex.title=1, border=TRUE, text=list(as.character(unique(response))), lines=list(col=colors[.modc(1:n.y.lev)], lty=lines[.modl(1:n.y.lev)], lwd=lwd), points=list(pch=symbols[.mods(1:n.y.lev)], col=colors[.modc(1:n.y.lev)]), columns = if ("x" %in% names(key.args)) 1 else find.legend.columns(n.y.lev)) for (k in names(key.args)) key[k] <- key.args[k] if (show.strip.values){ for (pred in predictors[-x.var]){ Data[[pred]] <- as.factor(Data[[pred]]) } } result <- xyplot(eval(if (type=="probability") parse(text=if (n.predictors==1) paste("prob ~ as.numeric(", predictors[x.var], ")") else paste("prob ~ as.numeric(", predictors[x.var],") | ", paste(predictors[-x.var], collapse="*"))) else parse(text=if (n.predictors==1) paste("logit ~ as.numeric(", predictors[x.var], ")") else paste("logit ~ as.numeric(", predictors[x.var],") | ", paste(predictors[-x.var], collapse="*")))), strip=function(...) strip.default(..., strip.names=c(factor.names, TRUE), sep=" = "), panel=function(x, y, subscripts, rug, z, x.vals, ...){ if (grid) ticksGrid(x=1:length(levs), y=tickmarks$at) for (i in 1:n.y.lev){ sub <- z[subscripts] == y.lev[i] good <- !is.na(y[sub]) effect.llines(x[sub][good], y[sub][good], lwd=lwd, type="b", col=colors[.modc(i)], lty=lines[.modl(i)], pch=symbols[i], cex=cex, ...) } }, ylab=ylab, ylim= if (is.null(ylim)) if (type == "probability") range(prob) else range(logit) else ylim, xlab=if (is.null(xlab)) predictors[x.var] else xlab, x.vals=x$data[[predictors[x.var]]], rug=rug, z=response, scales=list(x=list(at=1:length(levs), labels=levs, rot=rotx), y=list(at=tickmarks$at, labels=tickmarks$labels, rot=roty), alternating=alternating), main=main, key=key, layout=layout, data=Data, ...) result$split <- split result$more <- more class(result) <- c("plot.eff", class(result)) } else { # x-variable numeric if(use.splines) effect.llines <- spline.llines # added 10/17/13 range <- if (type=="probability") range(prob, na.rm=TRUE) else range(logit, na.rm=TRUE) ylim <- if (!any(is.na(ylim))) ylim else c(range[1] - .025*(range[2] - range[1]), range[2] + .025*(range[2] - range[1])) tickmarks <- make.ticks(ylim, link=I, inverse=I, at=ticks$at, n=ticks$n) nm <- predictors[x.var] x.vals <- x$data[[nm]] if (nm %in% names(ticks.x)){ at <- ticks.x[[nm]]$at n <- ticks.x[[nm]]$n } else{ at <- NULL n <- 5 } xlm <- if (nm %in% names(xlim)){ xlim[[nm]] } else range.adj(Data[nm]) # range(x.vals) tickmarks.x <- if ((nm %in% names(transform.x)) && !(is.null(transform.x))){ trans <- transform.x[[nm]]$trans make.ticks(trans(xlm), link=transform.x[[nm]]$trans, inverse=transform.x[[nm]]$inverse, at=at, n=n) } else { trans <- I make.ticks(xlm, link=I, inverse=I, at=at, n=n) } key <- list(title=x$response, cex.title=1, border=TRUE, text=list(as.character(unique(response))), lines=list(col=colors[.modc(1:n.y.lev)], lty=lines[.modl(1:n.y.lev)], lwd=lwd), columns = if ("x" %in% names(key.args)) 1 else find.legend.columns(n.y.lev)) for (k in names(key.args)) key[k] <- key.args[k] if (show.strip.values){ for (pred in predictors[-x.var]){ Data[[pred]] <- as.factor(Data[[pred]]) } } result <- xyplot(eval(if (type=="probability") parse(text=if (n.predictors==1) paste("prob ~ trans(", predictors[x.var], ")") else paste("prob ~ trans(", predictors[x.var],") |", paste(predictors[-x.var], collapse="*"))) else parse(text=if (n.predictors==1) paste("logit ~ trans(", predictors[x.var], ")") else paste("logit ~ trans(", predictors[x.var],") | ", paste(predictors[-x.var], collapse="*")))), strip=function(...) strip.default(..., strip.names=c(factor.names, TRUE), sep=" = "), panel=function(x, y, subscripts, rug, z, x.vals, ...){ if (grid) ticksGrid(x=tickmarks.x$at, y=tickmarks$at) if (rug) lrug(trans(x.vals)) for (i in 1:n.y.lev){ sub <- z[subscripts] == y.lev[i] good <- !is.na(y[sub]) effect.llines(x[sub][good], y[sub][good], lwd=lwd, type="l", col=colors[.modc(i)], lty=lines[.modl(i)], ...) } }, ylab=ylab, xlim=suppressWarnings(trans(xlm)), ylim= if (is.null(ylim)) if (type == "probability") range(prob) else range(logit) else ylim, xlab=if (is.null(xlab)) predictors[x.var] else xlab, x.vals=x$data[[predictors[x.var]]], rug=rug, z=response, scales=list(x=list(at=tickmarks.x$at, labels=tickmarks.x$labels, rot=rotx), y=list(at=tickmarks$at, labels=tickmarks$labels, rot=roty), alternating=alternating), main=main, key=key, layout=layout, data=Data, ...) result$split <- split result$more <- more class(result) <- c("plot.eff", class(result)) } } } else { # stacked plot tickmarks <- make.ticks(c(0, 1), link=I, inverse=I, at=ticks$at, n=ticks$n) layout <- if (is.null(layout)){ lay <- c(prod(n.predictor.cats[-(n.predictors - 1)]), prod(n.predictor.cats[(n.predictors - 1)]), 1) if (lay[1] > 1) lay else lay[c(2, 1, 3)] } else layout if (n.y.lev > length(colors)) stop(paste('Not enough colors to plot', n.y.lev, 'regions')) key <- list(text=list(lab=rev(y.lev)), rectangle=list(col=rev(colors[1:n.y.lev]))) for (k in names(key.args)) key[k] <- key.args[k] if (is.factor(x$data[[predictors[x.var]]])){ # x-variable a factor result <- barchart(eval(parse(text=if (n.predictors == 1) paste("prob ~ ", predictors[x.var], sep="") else paste("prob ~ ", predictors[x.var]," | ", paste(predictors[-x.var], collapse="*")))), strip=function(...) strip.default(..., strip.names=c(factor.names, TRUE), sep=" = "), panel=function(x, y, ...){ panel.barchart(x, y, ...) if (grid) ticksGrid(x=NA, y=tickmarks$at, col="white") }, groups = response, col=colors, horizontal=FALSE, stack=TRUE, data=Data, ylim=ylim, # if (is.null(ylim)) 0:1 else ylim, ylab=ylab, xlab=if (is.null(xlab)) predictors[x.var] else xlab, scales=list(x=list(rot=rotx, at=1:length(levs), labels=levs), y=list(rot=roty, at=tickmarks$at, labels=tickmarks$labels), alternating=alternating), main=main, key=key, layout=layout) result$split <- split result$more <- more class(result) <- c("plot.eff", class(result)) } else { # x-variable numeric if(use.splines) effect.llines <- spline.llines # added 10/17/13 nm <- predictors[x.var] x.vals <- x$data[[nm]] if (nm %in% names(ticks.x)){ at <- ticks.x[[nm]]$at n <- ticks.x[[nm]]$n } else{ at <- NULL n <- 5 } xlm <- if (nm %in% names(xlim)){ xlim[[nm]] } else range.adj(Data[nm]) # range(x.vals) tickmarks.x <- if ((nm %in% names(transform.x)) && !(is.null(transform.x))){ trans <- transform.x[[nm]]$trans make.ticks(trans(xlm), link=transform.x[[nm]]$trans, inverse=transform.x[[nm]]$inverse, at=at, n=n) } else { trans <- I make.ticks(xlm, link=I, inverse=I, at=at, n=n) } if (show.strip.values){ for (pred in predictors[-x.var]){ x$x[[pred]] <- as.factor(x$x[[pred]]) } } result <- densityplot(eval(parse(text=if (n.predictors == 1) paste("~ trans(", predictors[x.var], ")", sep="") else paste("~ trans(", predictors[x.var], ") | ", paste(predictors[-x.var], collapse="*")))), probs=x$prob, strip=function(...) strip.default(..., strip.names=c(factor.names, TRUE), sep=" = "), panel = function(x, subscripts, rug, x.vals, probs=probs, col=colors, ...){ fill <- function(x, y1, y2, col){ if (length(y2) == 1) y2 <- rep(y2, length(y1)) if (length(y1) == 1) y1 <- rep(y1, length(y2)) panel.polygon(c(x, rev(x)), c(y1, rev(y2)), col=col) } n <- ncol(probs) Y <- t(apply(probs[subscripts,], 1, cumsum)) fill(x, 0, Y[,1], col=col[1]) for (i in 2:n){ fill(x, Y[,i-1], Y[,i], col=col[i]) } if (rug) lrug(trans(x.vals)) if (grid) ticksGrid(x=tickmarks.x$at, y=tickmarks$at, col="white") }, rug=rug, x.vals=x$data[[predictors[x.var]]], data=x$x, xlim=suppressWarnings(trans(xlm)), ylim= c(0, 1), # if (is.null(ylim)) 0:1 else ylim, ylab=ylab, xlab=if (is.null(xlab)) predictors[x.var] else xlab, scales=list(x=list(at=tickmarks.x$at, labels=tickmarks.x$labels, rot=rotx), y=list(rot=roty, at=tickmarks$at, labels=tickmarks$labels), alternating=alternating), main=main, key=key, layout=layout, ...) result$split <- split result$more <- more class(result) <- c("plot.eff", class(result)) } } } ### with confidence bands else{ # plot with confidence bands if (type == "probability"){ lower <- lower.prob upper <- upper.prob } else { lower <- lower.logit upper <- upper.logit } if (!multiline){ layout <- if(is.null(layout)) c(prod(n.predictor.cats), length(levels(response)), 1) else layout ### factor if (is.factor(x$data[[predictors[x.var]]])){ # x-variable a factor range <- range(c(lower, upper), na.rm=TRUE) ylim <- if (!any(is.na(ylim))) ylim else c(range[1] - .025*(range[2] - range[1]), range[2] + .025*(range[2] - range[1])) tickmarks <- make.ticks(ylim, link=I, inverse=I, at=ticks$at, n=ticks$n) levs <- levels(x$data[[predictors[x.var]]]) if (show.strip.values){ for (pred in predictors[-x.var]){ Data[[pred]] <- as.factor(Data[[pred]]) } } result <- xyplot(eval(if (type=="probability") parse(text=if (n.predictors==1) paste("prob ~ as.numeric(", predictors[x.var],") |", x$response) else paste("prob ~ as.numeric(", predictors[x.var],") |", paste(predictors[-x.var], collapse="*"), paste("*", x$response))) else parse(text=if (n.predictors==1) paste("logit ~ as.numeric(", predictors[x.var],") |", x$response) else paste("logit ~ as.numeric(", predictors[x.var],")|", paste(predictors[-x.var], collapse="*"), paste("*", x$response)))), par.strip.text=list(cex=0.8), strip=function(...) strip.default(..., strip.names=c(factor.names, TRUE), sep=" = "), panel=function(x, y, subscripts, x.vals, rug, lower, upper, ... ){ if (grid) ticksGrid(x=1:length(levs), y=tickmarks$at) good <- !is.na(y) effect.llines(x[good], y[good], lwd=lwd, type="b", pch=19, col=colors[1], cex=cex, ...) subs <- subscripts+as.numeric(rownames(Data)[1])-1 if (ci.style == "bars"){ larrows(x0=x[good], y0=lower[subs][good], x1=x[good], y1=upper[subs][good], angle=90, code=3, col=colors[.modc(2)], length=0.125*cex/1.5) } else if(ci.style == "lines"){ effect.llines(x[good], lower[subs][good], lty=2, col=colors[.modc(2)]) effect.llines(x[good], upper[subs][good], lty=2, col=colors[.modc(2)]) } else { if(ci.style == "bands") { panel.bands(x[good], y[good], lower[subs][good], upper[subs][good], fill=band.colors[1], alpha=band.transparency) }} }, ylab=ylab, ylim= if (is.null(ylim)) c(min(lower), max(upper)) else ylim, xlab=if (is.null(xlab)) predictors[x.var] else xlab, main=main, x.vals=x$data[[predictors[x.var]]], rug=rug, lower=lower, upper=upper, scales=list(x=list(at=1:length(levs), labels=levs, rot=rotx), y=list(at=tickmarks$at, labels=tickmarks$labels, rot=roty), alternating=alternating), layout=layout, data=Data, ...) result$split <- split result$more <- more class(result) <- c("plot.eff", class(result)) } else { # x-variable numeric if(use.splines) effect.llines <- spline.llines # added 10/17/13 range <- range(c(lower, upper), na.rm=TRUE) ylim <- if (!any(is.na(ylim))) ylim else c(range[1] - .025*(range[2] - range[1]), range[2] + .025*(range[2] - range[1])) tickmarks <- make.ticks(ylim, link=I, inverse=I, at=ticks$at, n=ticks$n) nm <- predictors[x.var] x.vals <- x$data[[nm]] if (nm %in% names(ticks.x)){ at <- ticks.x[[nm]]$at n <- ticks.x[[nm]]$n } else{ at <- NULL n <- 5 } xlm <- if (nm %in% names(xlim)){ xlim[[nm]] } else range.adj(Data[nm]) # range(x.vals) tickmarks.x <- if ((nm %in% names(transform.x)) && !(is.null(transform.x))){ trans <- transform.x[[nm]]$trans make.ticks(trans(xlm), link=transform.x[[nm]]$trans, inverse=transform.x[[nm]]$inverse, at=at, n=n) } else { trans <- I make.ticks(xlm, link=I, inverse=I, at=at, n=n) } if (show.strip.values){ for (pred in predictors[-x.var]){ Data[[pred]] <- as.factor(Data[[pred]]) } } result <- xyplot(eval(if (type=="probability") parse(text=if (n.predictors==1) paste("prob ~ trans(", predictors[x.var],") |", x$response) else paste("prob ~ trans(", predictors[x.var],") |", paste(predictors[-x.var], collapse="*"), paste("*", x$response))) else parse(text=if (n.predictors==1) paste("logit ~ trans(", predictors[x.var],") |", x$response) else paste("logit ~ trans(", predictors[x.var],") |", paste(predictors[-x.var], collapse="*"), paste("*", x$response))) ), par.strip.text=list(cex=0.8), strip=function(...) strip.default(..., strip.names=c(factor.names, TRUE), sep=" = "), panel=function(x, y, subscripts, x.vals, rug, lower, upper, ... ){ if (grid) ticksGrid(x=tickmarks.x$at, y=tickmarks$at) if (rug) lrug(trans(x.vals)) good <- !is.na(y) effect.llines(x[good], y[good], lwd=lwd, col=colors[1], ...) subs <- subscripts+as.numeric(rownames(Data)[1])-1 if (ci.style == "bars"){ larrows(x0=x[good], y0=lower[subs][good], x1=x[good], y1=upper[subs][good], angle=90, code=3, col=colors[.modc(2)], length=0.125*cex/1.5) } else if(ci.style == "lines"){ effect.llines(x[good], lower[subs][good], lty=2, col=colors[.modc(2)]) effect.llines(x[good], upper[subs][good], lty=2, col=colors[.modc(2)]) } else { if(ci.style == "bands") { panel.bands(x[good], y[good], lower[subs][good], upper[subs][good], fill=band.colors[1], alpha=band.transparency) }} }, ylab=ylab, xlim=suppressWarnings(trans(xlm)), ylim= if (is.null(ylim)) c(min(lower), max(upper)) else ylim, xlab=if (is.null(xlab)) predictors[x.var] else xlab, main=main, x.vals=x$data[[predictors[x.var]]], rug=rug, lower=lower, upper=upper, scales=list(y=list(at=tickmarks$at, labels=tickmarks$labels, rot=roty), x=list(at=tickmarks.x$at, labels=tickmarks.x$labels, rot=rotx), alternating=alternating), layout=layout, data=Data, ...) result$split <- split result$more <- more class(result) <- c("plot.eff", class(result)) } } else { layout <- if (is.null(layout)){ lay <- c(prod(n.predictor.cats[-(n.predictors - 1)]), prod(n.predictor.cats[(n.predictors - 1)]), 1) if (lay[1] > 1) lay else lay[c(2, 1, 3)] } else layout if (n.y.lev > min(c(length(colors), length(lines), length(symbols)))) warning('Colors, lines and symbols may have been recycled') if (is.factor(x$data[[predictors[x.var]]])){ # x-variable a factor range <- range(c(lower, upper), na.rm=TRUE) ylim <- if (!any(is.na(ylim))) ylim else c(range[1] - .025*(range[2] - range[1]), range[2] + .025*(range[2] - range[1])) tickmarks <- make.ticks(ylim, link=I, inverse=I, at=ticks$at, n=ticks$n) key <- list(title=x$response, cex.title=1, border=TRUE, text=list(as.character(unique(response))), lines=list(col=colors[.modc(1:n.y.lev)], lty=lines[.modl(1:n.y.lev)], lwd=lwd), points=list(pch=symbols[.mods(1:n.y.lev)], col=colors[.modc(1:n.y.lev)]), columns = if ("x" %in% names(key.args)) 1 else find.legend.columns(n.y.lev)) for (k in names(key.args)) key[k] <- key.args[k] if (show.strip.values){ for (pred in predictors[-x.var]){ Data[[pred]] <- as.factor(Data[[pred]]) } } result <- xyplot(eval(if (type=="probability") parse(text=if (n.predictors==1) paste("prob ~ as.numeric(", predictors[x.var], ")") else paste("prob ~ as.numeric(", predictors[x.var],") | ", paste(predictors[-x.var], collapse="*"))) else parse(text=if (n.predictors==1) paste("logit ~ as.numeric(", predictors[x.var], ")") else paste("logit ~ as.numeric(", predictors[x.var],") | ", paste(predictors[-x.var], collapse="*")))), strip=function(...) strip.default(..., strip.names=c(factor.names, TRUE), sep=" = "), panel=function(x, y, subscripts, rug, z, x.vals, lower, upper, ...){ if (grid) ticksGrid(x=1:length(levs), y=tickmarks$at) for (i in 1:n.y.lev){ sub <- z[subscripts] == y.lev[i] good <- !is.na(y[sub]) effect.llines(x[sub][good], y[sub][good], lwd=lwd, type="b", col=colors[.modc(i)], lty=lines[.modl(i)], pch=symbols[i], cex=cex, ...) if (ci.style == "bars"){ larrows(x0=x[sub][good], y0=lower[subscripts][sub][good], x1=x[sub][good], y1=upper[subscripts][sub][good], angle=90, code=3, col=colors[.modc(i)], length=0.125*cex/1.5) } else if(ci.style == "lines"){ effect.llines(x[sub][good], lower[subscripts][sub][good], lty=lines[.modl(i)], col=colors[.modc(i)]) effect.llines(x[sub][good], upper[subscripts][sub][good], lty=lines[.modl(i)], col=colors[.modc(i)]) } else { if(ci.style == "bands") { panel.bands(x[sub][good], y[sub][good], lower[subscripts][sub][good], upper[subscripts][sub][good], fill=colors[.modc(i)], alpha=band.transparency) }} } }, ylab=ylab, ylim= if (is.null(ylim)) c(min(lower), max(upper)) else ylim, xlab=if (is.null(xlab)) predictors[x.var] else xlab, x.vals=x$data[[predictors[x.var]]], rug=rug, z=response, lower=lower, upper=upper, scales=list(x=list(at=1:length(levs), labels=levs, rot=rotx), y=list(at=tickmarks$at, labels=tickmarks$labels, rot=roty), alternating=alternating), main=main, key=key, layout=layout, data=Data, ...) result$split <- split result$more <- more class(result) <- c("plot.eff", class(result)) } else { # x-variable numeric if(use.splines) effect.llines <- spline.llines # added 10/17/13 range <- range(c(lower, upper), na.rm=TRUE) ylim <- if (!any(is.na(ylim))) ylim else c(range[1] - .025*(range[2] - range[1]), range[2] + .025*(range[2] - range[1])) tickmarks <- make.ticks(ylim, link=I, inverse=I, at=ticks$at, n=ticks$n) nm <- predictors[x.var] x.vals <- x$data[[nm]] if (nm %in% names(ticks.x)){ at <- ticks.x[[nm]]$at n <- ticks.x[[nm]]$n } else{ at <- NULL n <- 5 } xlm <- if (nm %in% names(xlim)){ xlim[[nm]] } else range.adj(Data[nm]) # range(x.vals) tickmarks.x <- if ((nm %in% names(transform.x)) && !(is.null(transform.x))){ trans <- transform.x[[nm]]$trans make.ticks(trans(xlm), link=transform.x[[nm]]$trans, inverse=transform.x[[nm]]$inverse, at=at, n=n) } else { trans <- I make.ticks(xlm, link=I, inverse=I, at=at, n=n) } key <- list(title=x$response, cex.title=1, border=TRUE, text=list(as.character(unique(response))), lines=list(col=colors[.modc(1:n.y.lev)], lty=lines[.modl(1:n.y.lev)], lwd=lwd), columns = if ("x" %in% names(key.args)) 1 else find.legend.columns(n.y.lev)) for (k in names(key.args)) key[k] <- key.args[k] if (show.strip.values){ for (pred in predictors[-x.var]){ Data[[pred]] <- as.factor(Data[[pred]]) } } result <- xyplot(eval(if (type=="probability") parse(text=if (n.predictors==1) paste("prob ~ trans(", predictors[x.var], ")") else paste("prob ~ trans(", predictors[x.var],") |", paste(predictors[-x.var], collapse="*"))) else parse(text=if (n.predictors==1) paste("logit ~ trans(", predictors[x.var], ")") else paste("logit ~ trans(", predictors[x.var],") | ", paste(predictors[-x.var], collapse="*")))), strip=function(...) strip.default(..., strip.names=c(factor.names, TRUE), sep=" = "), panel=function(x, y, subscripts, rug, z, x.vals, lower, upper, ...){ if (grid) ticksGrid(x=tickmarks.x$at, y=tickmarks$at) if (rug) lrug(trans(x.vals)) for (i in 1:n.y.lev){ sub <- z[subscripts] == y.lev[i] good <- !is.na(y[sub]) effect.llines(x[sub][good], y[sub][good], lwd=lwd, type="l", col=colors[.modc(i)], lty=lines[.modl(i)], ...) if (ci.style == "bars"){ larrows(x0=x[sub][good], y0=lower[subscripts][sub][good], x1=x[sub][good], y1=upper[subscripts][sub][good], angle=90, code=3, col=colors[.modc(i)], length=0.125*cex/1.5) } else if(ci.style == "lines"){ effect.llines(x[sub][good], lower[subscripts][sub][good], lty=lines[.modl(i)], col=colors[.modc(i)]) effect.llines(x[sub][good], upper[subscripts][sub][good], lty=lines[.modl(i)], col=colors[.modc(i)]) } else { if(ci.style == "bands") { panel.bands(x[sub][good], y[sub][good], lower[subscripts][sub][good], upper[subscripts][sub][good], fill=colors[.modc(i)], alpha=band.transparency) }} } }, ylab=ylab, xlim=suppressWarnings(trans(xlm)), ylim= if (is.null(ylim)) c(min(lower), max(upper)) else ylim, xlab=if (is.null(xlab)) predictors[x.var] else xlab, x.vals=x$data[[predictors[x.var]]], rug=rug, z=response, lower=lower, upper=upper, scales=list(x=list(at=tickmarks.x$at, labels=tickmarks.x$labels, rot=rotx), y=list(at=tickmarks$at, labels=tickmarks$labels, rot=roty), alternating=alternating), main=main, key=key, layout=layout, data=Data, ...) result$split <- split result$more <- more class(result) <- c("plot.eff", class(result)) } } } result } effects/R/utilities.R0000644000176200001440000005117413156517677014262 0ustar liggesusers# utilities and common functions for effects package # John Fox, Jangman Hong, and Sanford Weisberg # 7-25-2013 S. Weisberg modified analyze.model and Analyze.model to ignore # default.levels, and use xlevels to set default. Use grid.pretty by default # 11-09-2013: fixed error message in Analyze.model(), bug reported by Joris Meys. J. Fox # 2013-10-15: eliminated functions not needed after effect() methods removed. J. Fox # 2013-10-29: fixed as.data.frame.*() to handle NA levels. J. Fox # 2014-03-13: modified Fixup.model.matrix() and Analyze.model() to handle partial residuals; # added is.factor.predictor() and is.numeric.predictor(). J. Fox # 2014-03-14: error message for non-factor, non-numeric predictor # 2014-07-08: if no numeric predictor, partial residuals suppressed with warning rather than an error # 2014-10-09: namespace fixes. J. Fox # 2015-04-08: added setStrip(), restoreStrip(). J. Fox # 2015-07-07: fixed matchVarName() so that it handles periods in names properly. J. Fox # 2015-09-10: added a fix for class = 'array' in Analyze.model. S. Weisberg # 2016-02-16: fix Analyze.model(), Fixup.model.matrix() to handle non-focal terms like polynomials correctly; clean up code. J. Fox # 2016-03-01: correct and improve computation of partial residuals # 2017-07-10: fix warnings about 1 x 1 arrays produced in eff.mul() and eff.polr() in R 3.4.0 (reported by Stefan Th. Gries). J. Fox # 2017-07-14: added applyDefaults() and isFALSE(). J. Fox # 2017-07-27: added effectsTheme(); removed setStrip(), restoreStrip(). J. Fox # 2017-08-08: added .onAttach() to set lattice theme. J. Fox # 2017-08-26: added scheffe() to compute multipler for Scheffe-type confidence bounds. J. Fox # 2017-08-29: enhanced applyDefaults() with onFALSE argument. J. Fox # 2017-09-02: added nice() # 2017-09-08: small changes to accommodate Effect.svyglm() # 2017-09-10: added replacement for ticksGrid() has.intercept <- function(model, ...) any(names(coefficients(model))=="(Intercept)") term.names <- function (model, ...) { term.names <- gsub(" ", "", labels(terms(model))) if (has.intercept(model)) c("(Intercept)", term.names) else term.names } response.name <- function (model, ...) deparse(attr(terms(model), "variables")[[2]]) mfrow <- function(n, max.plots=0){ # number of rows and columns for array of n plots if (max.plots != 0 & n > max.plots) stop(paste("number of plots =",n," exceeds maximum =", max.plots)) rows <- round(sqrt(n)) cols <- ceiling(n/rows) c(rows, cols) } expand.model.frame <- function (model, extras, envir = environment(formula(model)), na.expand = FALSE){ # modified version of R base function f <- formula(model) data <- eval(model$call$data, envir) ff <- foo ~ bar + baz if (is.call(extras)) gg <- extras else gg <- parse(text = paste("~", paste(extras, collapse = "+")))[[1]] ff[[2]] <- f[[2]] ff[[3]][[2]] <- f[[3]] ff[[3]][[3]] <- gg[[2]] if (!na.expand) { naa <- model$call$na.action subset <- model$call$subset rval <- if (is.null(data)) eval(call("model.frame", ff, # modified subset = subset, na.action = naa), envir) # lines else eval(call("model.frame", ff, data = data, # subset = subset, na.action = naa), envir) # } else { subset <- model$call$subset rval <- eval(call("model.frame", ff, data = data, subset = subset, na.action = I), envir) oldmf <- model.frame(model) keep <- match(rownames(oldmf), rownames(rval)) rval <- rval[keep, ] class(rval) <- "data.frame" } return(rval) } is.relative <- function(term1, term2, factors) { all(!(factors[,term1]&(!factors[,term2]))) } descendants <- function(term, mod, ...){ names <- term.names(mod) if (has.intercept(mod)) names <- names[-1] if(length(names)==1) return(NULL) which.term <- which(term == names) if (length(which.term) == 0){ factors <- attr(terms(...), "factors") rownames(factors) <- gsub(" ", "", rownames(factors)) colnames(factors) <- gsub(" ", "", colnames(factors)) (1:length(names))[sapply(names, function(term2) is.relative(term, term2, factors))] } else { factors <- attr(terms(mod), "factors") rownames(factors) <- gsub(" ", "", rownames(factors)) colnames(factors) <- gsub(" ", "", colnames(factors)) (1:length(names))[-which.term][sapply(names[-which.term], function(term2) is.relative(term, term2, factors))] } } is.high.order.term <- function(term, mod,...){ 0 == length(descendants(term, mod, ...)) } subscripts <- function(index, dims){ subs <- function(dims, index){ dim <- length(dims) if (dim == 0) return(NULL) cum <- c(1,cumprod(dims))[dim] i <- index %/% cum if (index %% cum != 0) i <- i + 1 c(i, subs(dims[-dim], index - (i - 1)*cum)) } rev(subs(dims, index)) } matrix.to.df <- function(matrix, colclasses){ opt <- options(warn = -1) on.exit(options(opt)) ncol <- ncol(matrix) colnames <- colnames(matrix) colclasses[sapply(colclasses, function(x) "integer" %in% x)] <- "numeric" result <- vector(mode="list", length=ncol) names(result) <- colnames for (j in 1:ncol){ result[[j]] <- matrix[, j] class <- colclasses[[colnames[j]]] result[[colnames[j]]] <- if ("numeric" %in% class) { decChar <- getOption('OutDec') if (decChar == '.') as.numeric(result[[colnames[j]]]) else as.numeric(gsub(decChar, '.', matrix[,j])) } else if ("ordered" %in% class) ordered(result[[colnames[j]]]) else if ("factor" %in% class) factor(result[[colnames[j]]]) else result[[colnames[j]]] } as.data.frame(result) } # the following function is a modification of code contributed by Steve Taylor as.data.frame.eff <- function(x, row.names=NULL, optional=TRUE, transform=x$transformation$inverse, ...){ xx <- x$x for (var in names(xx)){ if (is.factor(xx[[var]])){ xx[[var]] <- addNA(xx[[var]]) # handle factors with "valid" NA level } } x$x <- xx result <- if (is.null(x$se)) data.frame(x$x, fit=transform(x$fit)) else data.frame(x$x, fit=transform(x$fit), se=x$se, lower=transform(x$lower), upper=transform(x$upper)) attr(result, "transformation") <- transform result } as.data.frame.effpoly <- function(x, row.names=NULL, optional=TRUE, ...){ factors <- sapply(x$variables, function(x) x$is.factor) factor.levels <- lapply(x$variables[factors], function(x) x$levels) if (!length(factor.levels) == 0){ factor.names <- names(factor.levels) for (fac in factor.names){ x$x[[fac]] <- factor(x$x[[fac]], levels=factor.levels[[fac]], exclude=NULL) } } result <- data.frame(x$x, x$prob, x$logit) if (!is.null(x$confidence.level)) result <- cbind(result, x$se.prob, x$se.logit, x$lower.prob, x$upper.prob, x$lower.logit, x$upper.logit) result } as.data.frame.efflatent <- function(x, row.names=NULL, optional=TRUE, ...){ xx <- x$x for (var in names(xx)){ if (is.factor(xx$var)){ xx$var <- addNA(xx$var) # handle factors with "valid" NA level } } x$x <- xx if (is.null(x$se)) data.frame(x$x, fit=x$fit) else data.frame(x$x, fit=x$fit, se=x$se, lower=x$lower, upper=x$upper) } logit2p <- function(logit) 1/(1 + exp(-logit)) p2logit <- function(p) log(p/(1 - p)) lrug <- function(x) { if (length(unique(x)) < 0.8 * length(x)) x <- jitter(x) grid.segments(x, unit(0, "npc"), x, unit(0.5, "lines"), default.units="native") } ## model.response not generic model.response.gls <- function(model){ model.response(model.frame(as.formula(model$call$model), data=eval(model$call$data))) } terms.gls <- function(x, ...) terms(formula(x)) ## vcov method for eff objects vcov.eff <- function(object, ...) object$vcov ## [ method for efflist objects `[.efflist` <- function(x, ...){ y <- NextMethod("[") class(y) <- class(x) y } ### the following functions are for use by Effect() methods Analyze.model <- function(focal.predictors, mod, xlevels, default.levels=NULL, formula.rhs, partial.residuals=FALSE, quantiles, x.var=NULL, data=NULL, typical=mean){ if ((!is.null(mod$nan.action)) && class(mod$na.action) == "exclude") class(mod$na.action) <- "omit" all.predictors <- all.vars(formula.rhs) check.vars <- !(focal.predictors %in% all.predictors) excluded.predictors <- setdiff(all.predictors, focal.predictors) number.bad <- sum(check.vars) if (any(check.vars)) { message <- if (number.bad == 1) paste("the following predictor is not in the model:", focal.predictors[check.vars]) else paste("the following predictors are not in the model:", paste(focal.predictors[check.vars], collapse=", ")) stop(message) } X.mod <- model.matrix(mod) cnames <- colnames(X.mod) factor.cols <- rep(FALSE, length(cnames)) names(factor.cols) <- cnames for (name in all.predictors){ if (is.factor.predictor(name, mod)) factor.cols[grep(paste("^", name, sep=""), cnames)] <- TRUE } factor.cols[grep(":", cnames)] <- FALSE X <- na.omit(expand.model.frame(mod, all.predictors)) bad <- sapply(X[, all.predictors, drop=FALSE], function(x) !(is.factor(x) || is.numeric(x))) if (any(bad)){ message <- if (sum(bad) == 1) paste("the following predictor isn't a factor or numeric:", all.predictors[bad]) else paste("the following predictors aren't factors or numeric:", paste(all.predictors[bad], collapse=", ")) stop(message) } x <- list() factor.levels <- list() if(length(xlevels)==0 & length(default.levels) == 1L) xlevels <- default.levels if(is.numeric(xlevels) & length(xlevels) == 1L){ levs <- xlevels for(name in focal.predictors) xlevels[[name]] <- levs } for (name in focal.predictors){ levels <- mod$xlevels[[name]] if(is.null(levels)) levels <- mod$xlevels[[paste("factor(",name,")",sep="")]] fac <- !is.null(levels) if (!fac) { levels <- if (is.null(xlevels[[name]])){ if (partial.residuals){ quantile(X[, name], quantiles) } else{ # grid.pretty(range(X[, name])) nice(seq(min(X[, name]), max(X[, name]), length.out=5)) } } else { if(length(xlevels[[name]]) == 1L) { nice(seq(min(X[, name]), max(X[,name]), length=xlevels[[name]]))} else xlevels[[name]]} } else factor.levels[[name]] <- levels x[[name]] <- list(name=name, is.factor=fac, levels=levels) } if (partial.residuals){ numeric.predictors <- sapply(focal.predictors, function(predictor) is.numeric.predictor(predictor, mod)) if (!any(numeric.predictors)) warning("there are no numeric focal predictors", "\n partial residuals suppressed") else{ x.var <- which(numeric.predictors)[1] x.var.name <- focal.predictors[x.var] if (is.null(mod$xlevels[[x.var.name]])){ x.var.range <- range(X[, focal.predictors[x.var]]) x[[x.var]][["levels"]] <- seq(from=x.var.range[1], to=x.var.range[2], length=100) } } } x.excluded <- list() for (name in excluded.predictors){ levels <- mod$xlevels[[name]] fac <- !is.null(levels) level <- if (fac) levels[1] else typical(X[, name]) if (fac) factor.levels[[name]] <- levels x.excluded[[name]] <- list(name=name, is.factor=fac, level=level) } dims <- sapply(x, function(x) length(x$levels)) len <- prod(dims) n.focal <- length(focal.predictors) n.excluded <- length(excluded.predictors) n.vars <- n.focal + n.excluded predict.data <-matrix('', len, n.vars) excluded <- sapply(x.excluded, function(x) x$level) for (i in 1:len){ subs <- subscripts(i, dims) for (j in 1:n.focal){ predict.data[i,j] <- x[[j]]$levels[subs[j]] } if (n.excluded > 0) predict.data[i, (n.focal + 1):n.vars] <- excluded } colnames(predict.data) <- c(sapply(x, function(x) x$name), sapply(x.excluded, function(x) x$name)) colclasses <- lapply(X, class) colclasses[colclasses == "matrix"] <- "numeric" colclasses[colclasses == "array"] <- "numeric" predict.data <- matrix.to.df(predict.data, colclasses=colclasses) list(predict.data=predict.data, factor.levels=factor.levels, factor.cols=factor.cols, focal.predictors=focal.predictors, n.focal=n.focal, excluded.predictors=excluded.predictors, n.excluded=n.excluded, x=x, X.mod=X.mod, cnames=cnames, X=X, x.var=x.var) } Fixup.model.matrix <- function(mod, mod.matrix, mod.matrix.all, X.mod, factor.cols, cnames, focal.predictors, excluded.predictors, typical, given.values, apply.typical.to.factors=FALSE){ attr(mod.matrix, "assign") <- attr(mod.matrix.all, "assign") if (length(excluded.predictors) > 0){ strangers <- Strangers(mod, focal.predictors, excluded.predictors) stranger.cols <- apply(outer(strangers, attr(mod.matrix,'assign'), '=='), 2, any) } else stranger.cols <- rep(FALSE, ncol(mod.matrix)) if (has.intercept(mod)) stranger.cols[1] <- TRUE if (any(stranger.cols)) { facs <- factor.cols & stranger.cols covs <- (!factor.cols) & stranger.cols if (has.intercept(mod)) covs[1] <- FALSE if (any(facs)){ mod.matrix[,facs] <- matrix(apply(as.matrix(X.mod[,facs]), 2, if (apply.typical.to.factors) typical else mean), nrow=nrow(mod.matrix), ncol=sum(facs), byrow=TRUE) } if (!is.null(given.values)){ stranger.names <- cnames[stranger.cols] given <- stranger.names %in% names(given.values) if (any(given)) { mod.matrix[,stranger.names[given]] <- matrix(given.values[stranger.names[given]], nrow=nrow(mod.matrix), ncol=length(stranger.names[given]), byrow=TRUE) } } for (name in cnames){ components <- unlist(strsplit(name, ':')) components <- components[components %in% cnames] if (length(components) > 1) { mod.matrix[,name] <- apply(mod.matrix[,components], 1, prod) } } } mod.matrix } matchVarName <- function(name, expressions){ scratch <- "zAMIjw4RN3" # randomly generated string name <- gsub("\\.", scratch, name) expressions <- gsub("\\.", scratch, as.character(expressions)) a <- !grepl(paste("[.]+", name, sep=""), expressions) b <- !grepl(paste(name, "[.]+", sep=""), expressions) c <- grepl(paste("\\b", name, "\\b", sep=""), expressions) a & b & c } Strangers <- function(mod, focal.predictors, excluded.predictors){ names <- term.names(mod) if (has.intercept(mod)) names <- names[-1] sel <- apply(sapply(excluded.predictors, matchVarName, expressions=names), 1, any) (1:length(sel))[sel] } # the following is used by effect.multinom() and Effect.multinom() eff.mul <- function(x0, B, se, m, p, r, V){ mu <- exp(x0 %*% B) mu <- mu/(1 + sum(mu)) mu[m] <- 1 - sum(mu) logits <- log(mu/(1 - mu)) if (!se) return(list(p=mu, logits=logits)) d <- array(0, c(m, m - 1, p)) exp.x0.B <- as.vector(exp(x0 %*% B)) sum.exp.x0.B <- sum(exp.x0.B) for (j in 1:(m-1)){ d[m, j,] <- - exp.x0.B[j]*x0 for (jj in 1:(m-1)){ d[j, jj,] <- if (jj != j) - exp(as.vector(x0 %*% (B[,jj] + B[,j])))*x0 else exp.x0.B[j]*(1 + sum.exp.x0.B - exp.x0.B[j])*x0 } } d <- d/(1 + sum.exp.x0.B)^2 V.mu <- rep(0, m) for (j in 1:m){ dd <- as.vector(t(d[j,,])) for (s in 1:r){ for (t in 1:r){ V.mu[j] <- V.mu[j] + V[s,t]*dd[s]*dd[t] } } } V.logits <- V.mu/(mu^2 * (1 - mu)^2) list(p=mu, std.err.p=sqrt(V.mu), logits=logits, std.error.logits=sqrt(V.logits)) } # the following are used by effect.polr() and Effect.polr() eff.polr <- function(x0, b, alpha, V, m, r, se){ eta0 <- as.vector(x0 %*% b) mu <- rep(0, m) mu[1] <- 1/(1 + exp(alpha[1] + eta0)) for (j in 2:(m-1)){ mu[j] <- exp(eta0)*(exp(alpha[j - 1]) - exp(alpha[j]))/ ((1 + exp(alpha[j - 1] + eta0))*(1 + exp(alpha[j] + eta0))) } mu[m] <- 1 - sum(mu) logits <- log(mu/(1 - mu)) if (!se) return(list(p=mu, logits=logits)) d <- matrix(0, m, r) d[1, 1] <- - exp(alpha[1] + eta0)/(1 + exp(alpha[1] + eta0))^2 d[1, m:r] <- - exp(alpha[1] + eta0)*x0/(1 + exp(alpha[1] + eta0))^2 for (j in 2:(m-1)){ d[j, j-1] <- exp(alpha[j-1] + eta0)/(1 + exp(alpha[j-1] + eta0))^2 d[j, j] <- - exp(alpha[j] + eta0)/(1 + exp(alpha[j] + eta0))^2 d[j, m:r] <- exp(eta0)*(exp(alpha[j]) - exp(alpha[j-1]))* (exp(alpha[j-1] + alpha[j] + 2*eta0) - 1) * x0 / (((1 + exp(alpha[j-1] + eta0))^2)* ((1 + exp(alpha[j] + eta0))^2)) } d[m, m-1] <- exp(alpha[m-1] + eta0)/(1 + exp(alpha[m-1] + eta0))^2 d[m, m:r] <- exp(alpha[m-1] + eta0)*x0/(1 + exp(alpha[m-1] + eta0))^2 V.mu <- rep(0, m) for (j in 1:m){ dd <- d[j,] for (s in 1:r){ for (t in 1:r){ V.mu[j] <- V.mu[j] + V[s,t]*dd[s]*dd[t] } } } V.logits <- V.mu/(mu^2 * (1 - mu)^2) list(p=mu, std.err.p=sqrt(V.mu), logits=logits, std.error.logits=sqrt(V.logits)) } eff.latent <- function(X0, b, V, se){ eta <- X0 %*% b if (!se) return(list(fit=eta)) var <- diag(X0 %*% V %*% t(X0)) list(fit=eta, se=sqrt(var)) } # determine class of a predictor is.factor.predictor <- function(predictor, model) { !is.null(model$xlevels[[predictor]]) } is.numeric.predictor <- function(predictor, model) { is.null(model$xlevels[[predictor]]) } # custom lattice theme effectsTheme <- function(strip.background=list(col=gray(seq(0.95, 0.5, length=3))), strip.shingle=list(col="black"), clip=list(strip="off"), superpose.line=list(lwd=c(2, rep(1, 6)))){ current <- sapply(c("strip.background", "strip.shingle", "clip", "superpose.line"), trellis.par.get) result <- list(strip.background=strip.background, strip.shingle=strip.shingle, clip=clip, superpose.line=superpose.line) attr(result, "current") <- current result } .onAttach <- function(libname, pkgname){ if (!"package:lattice" %in% search()){ lattice::trellis.par.set(effectsTheme(), warn=FALSE) packageStartupMessage("lattice theme set by effectsTheme()", "\nSee ?effectsTheme for details.") } else packageStartupMessage("Use the command", "\n lattice::trellis.par.set(effectsTheme())", "\n to customize lattice options for effects plots.", "\nSee ?efffectTheme for details.") } # to handle defaults for list-style arguments applyDefaults <- function(args, defaults, onFALSE, arg=""){ if (is.null(args)) return(defaults) if (isFALSE(args)) { if (missing(onFALSE)) return(FALSE) else return(onFALSE) } names <- names(args) names <- names[names != ""] if (!isTRUE(args) && length(names) != length(args)) warning("unnamed ", arg, " arguments, will be ignored") if (isTRUE(args) || is.null(names)) defaults else defaults[names] <- args[names] as.list(defaults) } isFALSE <- function(x){ length(x) == 1 && is.logical(x) && !isTRUE(x) } # compute multiplier for Scheffe-type confidence bounds scheffe <- function(level, p, df=Inf){ sqrt(p*qf(level, p, df)) } # function to compute "nice" numbers nice <- function (x, direction = c("round", "down", "up"), lead.digits = 1) { direction <- match.arg(direction) if (length(x) > 1){ result <- sapply(x, nice, direction = direction, lead.digits = lead.digits) if (anyDuplicated(result)) result <- nice(x, direction=direction, lead.digits = lead.digits + 1) return(result) } if (x == 0) return(0) power.10 <- floor(log(abs(x), 10)) if (lead.digits > 1) power.10 <- power.10 - lead.digits + 1 lead.digit <- switch(direction, round = round(abs(x)/10^power.10), down = floor(abs(x)/10^power.10), up = ceiling(abs(x)/10^power.10)) sign(x) * lead.digit * 10^power.10 } ticksGrid <- function(x, y, col=reference.line$col){ reference.line <- trellis.par.get("reference.line") panel.abline(h=y, v=x, col=col, lty=reference.line$lty) } effects/R/effects.R0000644000176200001440000000513213156517060013641 0ustar liggesusers# effect generic and methods; allEffects # John Fox, Sanford Weisberg, and Jangman Hong # last modified 2012-12-08 by J. Fox # 10/31/2012 modifed effect.lm to use z distn for ses with mer and nlme objects # 12-21-2012 Allow for empty cells in factor interactions, S. Weisberg # 7-15-2013: S. Weisberg: deleted 'default.levels' argument. Changed and # generalized xlevels argument to include the function of default.levels. # 2013-10-15: eliminated generic effect() and all its methods. J. Fox # 2014-07-02: added vcov. argument to effect # 2014-12-10: Changed 'effect' back to a generic function. S. Weisberg effect <- function(term, mod, vcov.=vcov, ...){ UseMethod("effect", mod) } effect.default <- function(term, mod, vcov.=vcov, ...){ term <- gsub(" ", "", gsub("\\*", ":", term)) terms <- term.names(mod) if (has.intercept(mod)) terms <- terms[-1] which.term <- which(term == terms) mod.aug<- list() if (length(which.term) == 0){ message("NOTE: ", term, " does not appear in the model") mod.aug <- update(formula(mod), eval(parse(text=paste(". ~ . +", term)))) } if (!is.high.order.term(term, mod, mod.aug)) message("NOTE: ", term, " is not a high-order term in the model") predictors <- all.vars(parse(text=term)) Effect(predictors, mod, vcov.=vcov., ...) } allEffects <- function(mod, ...) UseMethod("allEffects") allEffects.default <- function(mod, ...){ high.order.terms <- function(mod){ names <- term.names(mod) if (has.intercept(mod)) names<-names[-1] rel <- lapply(names, descendants, mod=mod) (1:length(names))[sapply(rel, function(x) length(x)==0)] } names <- term.names(mod) if (has.intercept(mod)) names <- names[-1] if (length(names) == 0) stop("the model contains no terms (beyond a constant)") terms <- names[high.order.terms(mod)] result <- lapply(terms, effect, mod=mod, ...) names(result) <- terms class(result) <- 'efflist' result } allEffects.gls <- function(mod, ...){ high.order.terms <- function(mod){ mod <- lm(as.formula(mod$call$model), data=eval(mod$call$data)) names <- term.names(mod) if (has.intercept(mod)) names<-names[-1] rel <- lapply(names, descendants, mod=mod) (1:length(names))[sapply(rel, function(x) length(x)==0)] } names <- term.names(mod) if (has.intercept(mod)) names <- names[-1] if (length(names) == 0) stop("the model contains no terms (beyond a constant)") terms <- names[high.order.terms(mod)] result <- lapply(terms, effect, mod=mod, ...) names(result) <- terms class(result) <- 'efflist' result } effects/R/plot-methods.R0000644000176200001440000012160013156517330014640 0ustar liggesusers # plot.eff method for effects package, moved here from plot-summary-print-methods.R # The plot.effpoly method remains there for now. # 2013-10-17: Added use.splines keyword to plot.eff. Sandy # 2013-10-17: Made ci.style="bands" default for variates; allow "bands" if multiline=TRUE # 2013-10-29: fixed plot.eff() to handle factors with "valid" NA level. J. Fox # 2014-03-03: modified plot.eff() to handle partial residuals. J. Fox # 2014-09-20: fixed plot.eff() to work with partial residuals when rescale.axis=FALSE; # added smooth.residuals argument. J. Fox # 2014-10-10: namespace fixes. J. Fox # 2014-12-05: made key.args more flexible. J. Fox # 2015-03-22: use wide columns by default only when x for legend not set. J. Fox # 2015-03-25: use non-robust loess smooth for partial residuals for non-Gaussian families. J. Fox # 2015-03-25: rationalized type and rescale.axis args to plot.eff(); deprecated rescale.axis arg. J. Fox # 2015-05-28: added residuals.smooth.color argument. J. Fox # 2015-08-28: added residuals.cex argument. J. Fox # 2016-03-01: move computation of partial residuals to the plot.eff() method. J. Fox # 2016-05-22: modified make.ticks() to avoid possible failure due to floating-point inaccuracy. J. Fox # 2016-08-31: fixed plotting with partial residuals with various scalings of y-axis and x-axis. J. Fox # 2016-09-16: added show.strip.values argument to plot.eff(). J. Fox # 2017-06-12: fixed bug in plot.eff() for multiline displays with many conditioning variables. J. Fox # 2017-07-15: modified plot.eff() to consolidate arguments and use lattice theme. J. Fox # 2017-08-09: small bug fixes, reorganized axes=list(x=list()) argument. J. Fox # 2017-08-17: tweaked layout. J. Fox # 2017-08-23: Fixed bug with the lattice=list(array()) argument in plot.efflist --- lattice was as # an argument to the next method twice # 2017-08-23: plot.eff, in key.args, set default for between.columns=0 # 2017-08-20: reintroduce legacy arguments for plot.eff() # 2017-09-10: use replacement for grid.panel() # the following functions aren't exported find.legend.columns <- function(n, target=min(4, n)){ rem <- n %% target if (rem != 0 && rem < target/2) target <- target - 1 target } make.ticks <- function(range, link, inverse, at, n) { warn <- options(warn=-1) on.exit(options(warn)) link <- if (is.null(link)) function(x) nlm(function(y) (inverse(y) - x)^2, mean(range))$estimate else link if (is.null(n)) n <- 5 labels <- if (is.null(at)){ range.labels <- sapply(range, inverse) labels <- grid.pretty(range.labels) } else at ticks <- try(sapply(labels, link), silent=TRUE) if (inherits(ticks, "try-error")){ ticks <- seq(range[1], range[2], length=n) } list(at=ticks, labels=format(labels)) } range.adj <- function(x){ range <- range(x, na.rm=TRUE) c(range[1] - .025*(range[2] - range[1]), range[2] + .025*(range[2] - range[1])) } # added, modified from http://www.r-bloggers.com/confidence-bands-with-lattice-and-r/ panel.bands <- function(x, y, upper, lower, fill, col, subscripts, ..., font, fontface, use.splines=FALSE) { if(!missing(subscripts)) { upper <- upper[subscripts] lower <- lower[subscripts] } if (use.splines){ up <- spline(x, upper) down <- spline(x, lower) x <- up$x upper <- up$y lower <- down$y } panel.polygon(c(x, rev(x)), c(upper, rev(lower)), col = fill, fill=fill, border = FALSE, ...) } # modified by Michael Friendly: added key.args: # modified by Michael Friendly: added ci.style="bands" # modified by Michael Friendly: added lwd= argument for llines (not used elsewhere) # modified by Michael Friendly: added alpha.band= argument for ci.style="bands" spline.llines <- function(x, y, ...) llines(spline(x, y), ...) plot.eff <- function(x, x.var, z.var=which.min(levels), main=paste(effect, "effect plot"), symbols=TRUE, lines=TRUE, axes, confint, partial.residuals, id, lattice, ..., # legacy arguments: multiline, rug, xlab, ylab, colors, cex, lty, lwd, ylim, xlim, factor.names, ci.style, band.transparency, band.colors, type, ticks, alternating, rotx, roty, grid, layout, rescale.axis, transform.x, ticks.x, show.strip.values, key.args, use.splines, residuals.color, residuals.pch, residuals.cex, smooth.residuals, residuals.smooth.color, show.fitted, span) { closest <- function(x, x0) apply(outer(x, x0, FUN=function(x, x0) abs(x - x0)), 1, which.min) .mod <- function(a, b) ifelse( (d <- a %% b) == 0, b, d) .modc <- function(a) .mod(a, length(colors)) .mods <- function(a) .mod(a, length(symbols)) .modl <- function(a) .mod(a, length(lines)) .modb <- function(a) .mod(a, length(band.colors)) if (!is.logical(lines) && !is.list(lines)) lines <- list(lty=lines) lines <- applyDefaults(lines, defaults=list(multiline=is.null(x$se), lty=trellis.par.get("superpose.line")$lty, lwd=trellis.par.get("superpose.line")$lwd[1], col=trellis.par.get("superpose.line")$col, splines=TRUE), onFALSE=list(multiline=FALSE, lty=0, lwd=0, col=rgb(1, 1, 1, alpha=0), splines=FALSE), arg="lines") if (missing(multiline)) multiline <- lines$multiline if (missing(lwd)) lwd <- lines$lwd if (missing(colors)) colors <- lines$col if (missing(use.splines)) use.splines <- lines$splines lines <- if (missing(lty)) lines$lty else lty if (!is.logical(symbols) && !is.list(symbols)) symbols <- list(pch=symbols) symbols <- applyDefaults(symbols, defaults=list(pch=trellis.par.get("superpose.symbol")$pch, cex=trellis.par.get("superpose.symbol")$cex[1]), onFALSE=list(pch=NA_integer_, cex=0), arg="symbols") cex <- symbols$cex symbols <- symbols$pch if (missing(axes)) axes <- NULL axes <- applyDefaults(axes, defaults=list( x=list(rotate=0, rug=TRUE), y=list(lab=NA, lim=NA, ticks=list(at=NULL, n=5), type="rescale", rotate=0), alternating=TRUE, grid=FALSE), arg="axes") x.args <- applyDefaults(axes$x, defaults=list(rotate=0, rug=TRUE), arg="axes$x") if (missing(xlab)) { xlab.arg <- FALSE xlab <- list() } if (missing(xlim)) { xlim.arg <- FALSE xlim <- list() } if (missing(ticks.x)) { ticks.x.arg <- FALSE ticks.x <- list() } if (missing(transform.x)) { transform.x.arg <- FALSE transform.x <- list() } if (missing(rotx)) rotx <- x.args$rotate if (missing(rug)) rug <- x.args$rug x.args$rotate <- NULL x.args$rug <- NULL x.pred.names <- names(x.args) if (length(x.pred.names) > 0){ for (pred.name in x.pred.names){ x.pred.args <- applyDefaults(x.args[[pred.name]], defaults=list(lab=NULL, lim=NULL, ticks=NULL, transform=NULL), arg=paste0("axes$x$", pred.name)) if (!xlab.arg) xlab[[pred.name]] <- x.pred.args$lab if (!xlim.arg) xlim[[pred.name]] <- x.pred.args$lim if (!ticks.x.arg) ticks.x[[pred.name]] <- x.pred.args$ticks if (!transform.x.arg) transform.x[[pred.name]] <- x.pred.args$transform } } if (length(xlab) == 0) xlab <- NA if (length(xlim) == 0) xlim <- NA if (length(ticks.x) == 0) ticks.x <- NA if (length(transform.x) == 0) transform.x <- NA y.args <- applyDefaults(axes$y, defaults=list(lab=NA, lim=NA, ticks=list(at=NULL, n=5), type="rescale", rotate=0), arg="axes$y") if (missing(ylab)) ylab <- y.args$lab if (missing(ylim)) ylim <- y.args$lim if (missing(ticks)) ticks <- y.args$ticks if (missing(type)) type <- y.args$type if (!missing(rescale.axis)) type <- if (rescale.axis) "rescale" else "response" type <- match.arg(type, c("rescale", "response", "link")) if (missing(roty)) roty <- y.args$rotate if (missing(alternating)) alternating <- axes$alternating if (missing(grid)) grid <- axes$grid if (missing(confint) || isTRUE(confint)) confint <- NULL confint <- applyDefaults(confint, defaults=list(style=NULL, alpha=0.15, col=colors), onFALSE=list(style="none", alpha=0, col=NA_integer_), arg="confint") if (missing(ci.style)) ci.style <- confint$style if (missing(band.transparency)) band.transparency <- confint$alpha if (missing(band.colors)) band.colors <- confint$col if(!is.null(ci.style)) ci.style <- match.arg(ci.style, c("auto", "bars", "lines", "bands", "none")) if (missing(partial.residuals)) partial.residuals <- NULL if (is.logical(partial.residuals)) partial.residuals <- list(plot=partial.residuals) partial.residuals <- applyDefaults(partial.residuals, defaults=list( plot=!is.null(x$residuals), fitted=FALSE, col=colors[2], pch=1, cex=1, smooth=TRUE, span=2/3, smooth.col=colors[2], lty=lines[1], lwd=lwd), arg="partial.residuals") if (missing(show.fitted)) show.fitted <- partial.residuals$fitted if (missing(residuals.color)) residuals.color <- partial.residuals$col if (missing(residuals.pch)) residuals.pch <- partial.residuals$pch if (missing(residuals.cex)) residuals.cex <- partial.residuals$cex if (missing(smooth.residuals)) smooth.residuals <- partial.residuals$smooth if (missing(residuals.smooth.color)) residuals.smooth.color <- partial.residuals$smooth.col residuals.lty <- partial.residuals$lty residuals.lwd <- partial.residuals$lwd if (missing(span)) span <- partial.residuals$span partial.residuals <- partial.residuals$plot if (missing(id) || isFALSE(id)) { id.n <- 0 id.cex <- 0 id.col <- NULL id.labels <- NULL } else { id <- applyDefaults(id, list( n=2, cex=0.75, col=residuals.color, labels=NULL ), arg="id") id.n <- id$n id.col <- id$col id.cex <- id$cex id.labels <- id$labels } if (missing(lattice)) lattice <- NULL lattice <- applyDefaults(lattice, defaults=list( layout=NULL, key.args=list(), strip=list(factor.names=TRUE, values=!partial.residuals), array=list(row=1, col=1, nrow=1, ncol=1, more=FALSE), arg="lattice" )) if (missing(layout)) layout <- lattice$layout if (missing(key.args)){ lattice$key.args[["between.columns"]] <- if(is.null(lattice$key.args[["between.columns"]])) 0 else lattice$key.args[["between.columns"]] key.args <- lattice$key.args } strip.args <- applyDefaults(lattice$strip, defaults=list(factor.names=TRUE, values=!partial.residuals), arg="lattice$strip") if (missing(factor.names)) factor.names <- strip.args$factor.names if (missing(show.strip.values)) show.strip.values <- strip.args$values array.args <- applyDefaults(lattice$array, defaults=list(row=1, col=1, nrow=1, ncol=1, more=FALSE), arg="lattice$array") row <- array.args$row col <- array.args$col nrow <- array.args$nrow ncol <- array.args$ncol more <- array.args$more if (smooth.residuals && !is.null(x$family)){ loess.family <- if (x$family == "gaussian") "symmetric" else "gaussian" } switch(type, rescale = { type <- "response" rescale.axis <- TRUE }, response = { type <- "response" rescale.axis <- FALSE }, link = { type <- "link" rescale.axis <- TRUE } ) levels <- sapply(x$variables, function(z) length(as.vector(z[["levels"]]))) thresholds <- x$thresholds has.thresholds <- !is.null(thresholds) effect.llines <- llines if (is.na(ylab)){ ylab <- if (has.thresholds) paste(x$response, ": ", paste(x$y.levels, collapse=", "), sep="") else x$response } if (has.thresholds){ threshold.labels <- abbreviate(x$y.levels, minlength=1) threshold.labels <- paste(" ", paste(threshold.labels[-length(threshold.labels)], threshold.labels[-1], sep=" - "), " ", sep="") } original.link <- trans.link <- x$transformation$link original.inverse <- trans.inverse <- x$transformation$inverse residuals <- if (partial.residuals) x$residuals else NULL if (!is.null(residuals) && !is.null(id.labels)) names(residuals) <- id.labels partial.residuals.range <- x$partial.residuals.range if (!rescale.axis){ x$lower[!is.na(x$lower)] <- trans.inverse(x$lower[!is.na(x$lower)]) x$upper[!is.na(x$upper)] <- trans.inverse(x$upper[!is.na(x$upper)]) x$fit[!is.na(x$fit)] <- trans.inverse(x$fit)[!is.na(x$fit)] trans.link <- trans.inverse <- I } x.all <- x$x.all split <- c(col, row, ncol, nrow) if (missing(x.var)) x.var <- x$x.var if (!is.null(x.var) && is.numeric(x.var)) x.var <- names(x.var) x.data <- x$data effect <- paste(sapply(x$variables, "[[", "name"), collapse="*") vars <- x$variables x <- as.data.frame(x, transform=I) for (i in 1:length(vars)){ if (!(vars[[i]]$is.factor)) next x[,i] <- factor(x[,i], levels=vars[[i]]$levels, exclude=NULL) } has.se <- !is.null(x$se) n.predictors <- ncol(x) - 1 - 3*has.se if (n.predictors == 1){ predictor <- names(x)[1] if (is.list(xlab)) xlab <- xlab[[predictor]] ### factor no other predictors if (is.factor(x[,1])){ ci.style <- if(is.null(ci.style) || ci.style == "auto") "bars" else ci.style range <- if(has.se & ci.style!="none") range(c(x$lower, x$upper), na.rm=TRUE) else range(x$fit, na.rm=TRUE) ylim <- if (!any(is.na(ylim))) ylim else c(range[1] - .025*(range[2] - range[1]), range[2] + .025*(range[2] - range[1])) tickmarks <- if (type == "response" && rescale.axis) make.ticks(ylim, link=trans.link, inverse=trans.inverse, at=ticks$at, n=ticks$n) else make.ticks(ylim, link=I, inverse=I, at=ticks$at, n=ticks$n) levs <- levels(x[,1]) plot <- xyplot(eval(parse( text=paste("fit ~ as.numeric(", names(x)[1], ")"))), strip=function(...) strip.default(..., strip.names=c(factor.names, TRUE)), panel=function(x, y, lower, upper, has.se, ...){ if (grid) ticksGrid(x=1:length(levs), y=tickmarks$at) good <- !is.na(y) if (has.se){ if (ci.style == "bars"){ larrows(x0=x[good], y0=lower[good], x1=x[good], y1=upper[good], angle=90, code=3, col=colors[.modc(2)], length=0.125*cex/1.5) } else if(ci.style == "lines") { effect.llines(x[good], lower[good], lty=2, col=colors[.modc(2)]) effect.llines(x[good], upper[good], lty=2, col=colors[.modc(2)]) } else{ if(ci.style == "bands") { panel.bands(x[good], y[good], upper[good], lower[good], fill=band.colors[1], alpha=band.transparency, use.splines=FALSE) }} } effect.llines(x[good], y[good], lwd=lwd, col=colors[1], lty=lines, type='b', pch=19, cex=cex, ...) if (has.thresholds){ panel.abline(h=thresholds, lty=3) panel.text(rep(current.panel.limits()$xlim[1], length(thresholds)), thresholds, threshold.labels, adj=c(0,0), cex=0.75) panel.text(rep(current.panel.limits()$xlim[2], length(thresholds)), thresholds, threshold.labels, adj=c(1,0), cex=0.75) } }, ylim=ylim, ylab=ylab, xlab=if (is.na(xlab)) names(x)[1] else xlab, scales=list(x=list(at=1:length(levs), labels=levs, rot=rotx), y=list(at=tickmarks$at, labels=tickmarks$labels, rot=roty), alternating=alternating, y=roty), main=main, lower=x$lower, upper=x$upper, has.se=has.se, data=x, ...) result <- update(plot, layout = if (is.null(layout)) c(0, prod(dim(plot))) else layout) result$split <- split result$more <- more class(result) <- c("plot.eff", class(result)) } ### variate, no other predictors *** else { effect.llines <- if(use.splines) spline.llines else effect.llines ci.style <- if(is.null(ci.style) || ci.style == "auto") "bands" else ci.style range <- if(has.se && ci.style!="none") range(c(x$lower, x$upper), na.rm=TRUE) else range(x$fit, na.rm=TRUE) ylim <- if (!any(is.na(ylim))) ylim else if (is.null(residuals)) c(range[1] - .025*(range[2] - range[1]), range[2] + .025*(range[2] - range[1])) else if (rescale.axis) c(min(partial.residuals.range[1], range[1] - .025*(range[2] - range[1])), max(partial.residuals.range[2], range[2] + .025*(range[2] - range[1]))) else c(min(original.inverse(partial.residuals.range[1]), range[1] - .025*(range[2] - range[1])), max(original.inverse(partial.residuals.range[2]), range[2] + .025*(range[2] - range[1]))) tickmarks <- if (type == "response" && rescale.axis) make.ticks(ylim, link=trans.link, inverse=trans.inverse, at=ticks$at, n=ticks$n) else make.ticks(ylim, link=I, inverse=I, at=ticks$at, n=ticks$n) nm <- names(x)[1] x.vals <- x.data[, nm] if (nm %in% names(ticks.x)){ at <- ticks.x[[nm]]$at n <- ticks.x[[nm]]$n } else{ at <- NULL n <- 5 } xlm <- if (nm %in% names(xlim)){ xlim[[nm]] } else range.adj(x[nm]) # range(x.vals) tickmarks.x <- if ((nm %in% names(transform.x)) && !(is.null(transform.x))){ trans <- transform.x[[nm]]$trans make.ticks(trans(xlm), link=transform.x[[nm]]$trans, inverse=transform.x[[nm]]$inverse, at=at, n=n) } else { trans <- I make.ticks(xlm, link=I, inverse=I, at=at, n=n) } if (is.null(x.var)){ if (!is.null(residuals)){ x.var <- names(x)[1] } else x.var <- which.max(levels) } if (!is.null(residuals)) x.fit <- x.data[, predictor] if (is.numeric(x.var)) x.var <- predictor plot <- xyplot(eval(parse( text=paste("fit ~ trans(", x.var, ")"))), strip=function(...) strip.default(..., strip.names=c(factor.names, TRUE)), panel=function(x, y, x.vals, rug, lower, upper, has.se, ...){ if (grid) ticksGrid(x=tickmarks.x$at, y=tickmarks$at) good <- !is.na(y) axis.length <- diff(range(x)) effect.llines(x[good], y[good], lwd=lwd, col=colors[1], ...) if (rug && is.null(residuals)) lrug(trans(x.vals)) if (has.se){ if (ci.style == "bars"){ larrows(x0=x[good], y0=lower[good], x1=x[good], y1=upper[good], angle=90, code=3, col=eval(colors[.modc(2)]), length=.125*cex/1.5) } else if(ci.style == "lines") { effect.llines(x[good], lower[good], lty=2, col=colors[.modc(2)]) effect.llines(x[good], upper[good], lty=2, col=colors[.modc(2)]) } else{ if(ci.style == "bands") { panel.bands(x[good], y[good], upper[good], lower[good], fill=band.colors[1], alpha=band.transparency, use.splines=use.splines) }} } if (has.thresholds){ panel.abline(h=thresholds, lty=3) panel.text(rep(current.panel.limits()$xlim[1], length(thresholds)), thresholds, threshold.labels, adj=c(0,0), cex=0.75) panel.text(rep(current.panel.limits()$xlim[2], length(thresholds)), thresholds, threshold.labels, adj=c(1,0), cex=0.75) } if (!is.null(residuals)){ fitted <- y[good][closest(trans(x.fit), x[good])] partial.res <- if (!rescale.axis) original.inverse(original.link(fitted) + residuals) else fitted + residuals lpoints(trans(x.fit), partial.res, col=residuals.color, pch=residuals.pch, cex=residuals.cex) if (show.fitted) lpoints(trans(x.fit), fitted, pch=16, col=residuals.color) # REMOVE ME if (smooth.residuals){ llines(loess.smooth(trans(x.fit), partial.res, span=span, family=loess.family), lwd=residuals.lwd, lty=residuals.lty, col=residuals.smooth.color) } if (id.n > 0){ M <- cbind(trans(x.fit), partial.res) md <- mahalanobis(M, colMeans(M), cov(M)) biggest <- order(md, decreasing=TRUE)[1:id.n] pos <- ifelse(trans(x.fit[biggest]) > mean(current.panel.limits()$xlim), 2, 4) ltext(trans(x.fit[biggest]), partial.res[biggest], names(partial.res)[biggest], pos=pos, col=id.col, cex=id.cex) } } }, ylim=ylim, xlim=suppressWarnings(trans(xlm)), ylab=ylab, xlab=if (is.na(xlab)) names(x)[1] else xlab, x.vals=x.vals, rug=rug, main=main, lower=x$lower, upper=x$upper, has.se=has.se, data=x, scales=list(y=list(at=tickmarks$at, labels=tickmarks$labels, rot=roty), x=list(at=tickmarks.x$at, labels=tickmarks.x$labels, rot=rotx), alternating=alternating), ...) result <- update(plot, layout = if (is.null(layout)) c(0, prod(dim(plot))) else layout) result$split <- split result$more <- more class(result) <- c("plot.eff", class(result)) } return(result) } ### more than one predictor predictors <- names(x)[1:n.predictors] levels <- sapply(apply(x[,predictors], 2, unique), length) if (is.null(x.var)){ if (!is.null(residuals)){ x.var <- names(x)[1] } else x.var <- which.max(levels) } if (is.list(xlab)) xlab <- xlab[[x.var]] if (!is.null(residuals)) x.fit <- x.data[, x.var] if (is.character(x.var)) { which.x <- which(x.var == predictors) if (length(which.x) == 0) stop(paste("x.var = '", x.var, "' is not in the effect.", sep="")) x.var <- which.x } if (is.character(z.var)) { which.z <- which(z.var == predictors) if (length(which.z) == 0) stop(paste("z.var = '", z.var, "' is not in the effect.", sep="")) z.var <- which.z } if (x.var == z.var) z.var <- z.var + 1 ### multiline if (multiline){ if (!is.null(residuals)) warning("partial residuals are not displayed in a multiline plot") ci.style <- if(is.null(ci.style)) "none" else ci.style if(ci.style == "lines") { cat("Confidence interval style 'lines' changed to 'bars'\n") ci.style <- "bars"} range <- if (has.se && ci.style !="none") range(c(x$lower, x$upper), na.rm=TRUE) else range(x$fit, na.rm=TRUE) ylim <- if (!any(is.na(ylim))) ylim else c(range[1] - .025*(range[2] - range[1]), range[2] + .025*(range[2] - range[1])) tickmarks <- if (type == "response" && rescale.axis) make.ticks(ylim, link=trans.link, inverse=trans.inverse, at=ticks$at, n=ticks$n) else make.ticks(ylim, link=I, inverse=I, at=ticks$at, n=ticks$n) zvals <- unique(x[, z.var]) ### multiline factor if (is.factor(x[,x.var])){ if (ci.style == "auto") ci.style <- "bars" levs <- levels(x[,x.var]) key <- list(title=predictors[z.var], cex.title=1, border=TRUE, text=list(as.character(zvals)), lines=list(col=colors[.modc(1:length(zvals))], lty=lines[.modl(1:length(zvals))], lwd=lwd), points=list(col=colors[.modc(1:length(zvals))], pch=symbols[.mods(1:length(zvals))]), columns = if ("x" %in% names(key.args)) 1 else find.legend.columns(length(zvals))) for (k in names(key.args)) key[k] <- key.args[k] if (show.strip.values && n.predictors > 2){ for (pred in predictors[-c(x.var, z.var)]){ x[[pred]] <- as.factor(x[[pred]]) } } plot <- xyplot(eval(parse( text=paste("fit ~ as.numeric(", predictors[x.var], ")", if (n.predictors > 2) paste(" |", paste(predictors[-c(x.var, z.var)], collapse="*"))))), strip=function(...) strip.default(..., strip.names=c(factor.names, TRUE), sep=" = "), panel=function(x, y, subscripts, z, lower, upper, show.se, ...){ if (grid) ticksGrid(x=1:length(levs), y=tickmarks$at) for (i in 1:length(zvals)){ sub <- z[subscripts] == zvals[i] good <- !is.na(y[sub]) os <- if(show.se) (i - (length(zvals) + 1)/2) * (2/(length(zvals)-1)) * .01 * (length(zvals) - 1) else 0 effect.llines(x[sub][good]+os, y[sub][good], lwd=lwd, type='b', col=colors[.modc(i)], pch=symbols[.mods(i)], lty=lines[.modl(i)], cex=cex, ...) if (show.se){ larrows(x0=x[sub][good]+os, y0=lower[subscripts][sub][good], x1=x[sub][good]+os, y1=upper[subscripts][sub][good], angle=90, code=3, col=eval(colors[.modc(i)]), length=.125*cex/1.5) } } if (has.thresholds){ panel.abline(h=thresholds, lty=3) panel.text(rep(current.panel.limits()$xlim[1], length(thresholds)), thresholds, threshold.labels, adj=c(0,0), cex=0.75) panel.text(rep(current.panel.limits()$xlim[2], length(thresholds)), thresholds, threshold.labels, adj=c(1,0), cex=0.75) } }, ylim=ylim, ylab=ylab, xlab=if (is.na(xlab)) predictors[x.var] else xlab, z=x[,z.var], scales=list(x=list(at=1:length(levs), labels=levs, rot=rotx), y=list(at=tickmarks$at, labels=tickmarks$labels, rot=roty), alternating=alternating), zvals=zvals, main=main, key=key, lower=x$lower, upper=x$upper, show.se=has.se && ci.style=="bars", data=x, ...) result <- update(plot, layout = if (is.null(layout)) c(0, prod(dim(plot))) else layout) result$split <- split result$more <- more class(result) <- c("plot.eff", class(result)) } ### multiline variate else{ if (ci.style == "auto") ci.style <- "bands" effect.llines <- if(use.splines) spline.llines else effect.llines nm <- names(x)[x.var] x.vals <- x.data[, nm] if (nm %in% names(ticks.x)){ at <- ticks.x[[nm]]$at n <- ticks.x[[nm]]$n } else{ at <- NULL n <- 5 } xlm <- if (nm %in% names(xlim)){ xlim[[nm]] } else range.adj(x[nm]) tickmarks.x <- if ((nm %in% names(transform.x)) && !(is.null(transform.x))){ trans <- transform.x[[nm]]$trans make.ticks(trans(xlm), link=transform.x[[nm]]$trans, inverse=transform.x[[nm]]$inverse, at=at, n=n) } else { trans <- I make.ticks(xlm, link=I, inverse=I, at=at, n=n) } key <- list(title=predictors[z.var], cex.title=1, border=TRUE, text=list(as.character(zvals)), lines=list(col=colors[.modc(1:length(zvals))], lty=lines[.modl(1:length(zvals))], lwd=lwd), columns = if ("x" %in% names(key.args)) 1 else find.legend.columns(length(zvals))) for (k in names(key.args)) key[k] <- key.args[k] if (show.strip.values && n.predictors > 2){ for (pred in predictors[-c(x.var, z.var)]){ x[[pred]] <- as.factor(x[[pred]]) } } plot <- xyplot(eval(parse( text=paste("fit ~trans(", predictors[x.var], ")", if (n.predictors > 2) paste(" |", paste(predictors[-c(x.var, z.var)], collapse="*"))))), strip=function(...) strip.default(..., strip.names=c(factor.names, TRUE), sep=" = "), panel=function(x, y, subscripts, x.vals, rug, z, lower, upper, show.se, ...){ if (grid) ticksGrid(x=tickmarks.x$at, y=tickmarks$at) if (rug && is.null(residuals)) lrug(trans(x.vals)) axis.length <- diff(range(x)) for (i in 1:length(zvals)){ sub <- z[subscripts] == zvals[i] good <- !is.na(y[sub]) effect.llines(x[sub][good], y[sub][good], lwd=lwd, type='l', col=colors[.modc(i)], lty=lines[.modl(i)], cex=cex, ...) if(show.se){ if(ci.style == "bars"){ os <- (i - (length(zvals) + 1)/2) * (2/(length(zvals)-1)) * .01 * axis.length larrows(x0=x[sub][good]+os, y0=lower[subscripts][sub][good], x1=x[sub][good]+os, y1=upper[subscripts][sub][good], angle=90, code=3, col=eval(colors[.modc(i)]), length=.125*cex/1.5) } if(ci.style == "bands"){ panel.bands(x[sub][good], y[sub][good], upper[subscripts][sub][good], lower[subscripts][sub][good], fill=eval(band.colors[.modb(i)]), alpha=band.transparency, use.splines=use.splines) } } } if (has.thresholds){ panel.abline(h=thresholds, lty=3) panel.text(rep(current.panel.limits()$xlim[1], length(thresholds)), thresholds, threshold.labels, adj=c(0,0), cex=0.75) panel.text(rep(current.panel.limits()$xlim[2], length(thresholds)), thresholds, threshold.labels, adj=c(1,0), cex=0.75) } }, ylim=ylim, xlim=suppressWarnings(trans(xlm)), ylab=ylab, xlab=if (is.na(xlab)) predictors[x.var] else xlab, x.vals=x.vals, rug=rug, z=x[,z.var], zvals=zvals, main=main, key=key, # lower=x$lower, upper=x$upper, show.se=has.se && ci.style %in% c("bars", "bands"), # data=x, scales=list(y=list(at=tickmarks$at, labels=tickmarks$labels), rot=roty, x=list(at=tickmarks.x$at, labels=tickmarks.x$labels, rot=rotx), alternating=alternating), ...) result <- update(plot, layout = if (is.null(layout)) c(0, prod(dim(plot))) else layout) result$split <- split result$more <- more class(result) <- c("plot.eff", class(result)) } return(result) } # multiplot ci.style <- if(is.null(ci.style) || ci.style == "auto"){ if(is.factor(x[, x.var])) "bars" else "bands"} else ci.style range <- if (has.se && ci.style !="none") range(c(x$lower, x$upper), na.rm=TRUE) else range(x$fit, na.rm=TRUE) # multiplot factor if (is.factor(x[,x.var])){ ylim <- if (!any(is.na(ylim))) ylim else c(range[1] - .025*(range[2] - range[1]), range[2] + .025*(range[2] - range[1])) tickmarks <- if (type == "response" && rescale.axis) make.ticks(ylim, link=trans.link, inverse=trans.inverse, at=ticks$at, n=ticks$n) else make.ticks(ylim, link=I, inverse=I, at=ticks$at, n=ticks$n) levs <- levels(x[,x.var]) if (show.strip.values){ for (pred in predictors[-x.var]){ x[[pred]] <- as.factor(x[[pred]]) } } plot <- xyplot(eval(parse( text=paste("fit ~ as.numeric(", predictors[x.var], ") |", paste(predictors[-x.var], collapse="*")))), strip=function(...) strip.default(..., strip.names=c(factor.names, TRUE), sep=" = "), panel=function(x, y, subscripts, lower, upper, has.se, ...){ if (grid) ticksGrid(x=1:length(levs), y=tickmarks$at) good <- !is.na(y) if (has.se){ if (ci.style == "bars"){ larrows(x0=x[good], y0=lower[subscripts][good], x1=x[good], y1=upper[subscripts][good], angle=90, code=3, col=colors[.modc(2)], length=0.125*cex/1.5) } else if(ci.style == "lines") { effect.llines(x[good], lower[subscripts][good], lty=2, col=colors[.modc(2)]) effect.llines(x[good], upper[subscripts][good], lty=2, col=colors[.modc(2)]) } else{ if(ci.style == "bands") { panel.bands(x[good], y[good], upper[subscripts][good], lower[subscripts][good], fill=band.colors[1], alpha=band.transparency, use.splines=FALSE) }} } effect.llines(x[good], y[good], lwd=lwd, type='b', col=colors[1], pch=19, cex=cex, ...) if (has.thresholds){ panel.abline(h=thresholds, lty=3) panel.text(rep(current.panel.limits()$xlim[1], length(thresholds)), thresholds, threshold.labels, adj=c(0,0), cex=0.75) panel.text(rep(current.panel.limits()$xlim[2], length(thresholds)), thresholds, threshold.labels, adj=c(1,0), cex=0.75) } }, ylim=ylim, ylab=ylab, xlab=if (is.na(xlab)) predictors[x.var] else xlab, scales=list(x=list(at=1:length(levs), labels=levs, rot=rotx), y=list(at=tickmarks$at, labels=tickmarks$labels, rot=roty), alternating=alternating), main=main, lower=x$lower, upper=x$upper, has.se=has.se, data=x, ...) result <- update(plot, layout = if (is.null(layout)) c(0, prod(dim(plot))) else layout) result$split <- split result$more <- more class(result) <- c("plot.eff", class(result)) } ### multiplot variate *** else{ effect.llines <- if(use.splines) spline.llines else effect.llines nm <- names(x)[x.var] x.vals <- x.data[, nm] if (nm %in% names(ticks.x)){ at <- ticks.x[[nm]]$at n <- ticks.x[[nm]]$n } else{ at <- NULL n <- 5 } xlm <- if (nm %in% names(xlim)){ xlim[[nm]] } else range.adj(x[nm]) tickmarks.x <- if ((nm %in% names(transform.x)) && !(is.null(transform.x))){ trans <- transform.x[[nm]]$trans make.ticks(trans(xlm), link=transform.x[[nm]]$trans, inverse=transform.x[[nm]]$inverse, at=at, n=n) } else { trans <- I make.ticks(xlm, link=I, inverse=I, at=at, n=n) } ylim <- if (!any(is.na(ylim))) ylim else if (is.null(residuals)) c(range[1] - .025*(range[2] - range[1]), range[2] + .025*(range[2] - range[1])) else if (rescale.axis) c(min(partial.residuals.range[1], range[1] - .025*(range[2] - range[1])), max(partial.residuals.range[2], range[2] + .025*(range[2] - range[1]))) else c(min(original.inverse(partial.residuals.range[1]), range[1] - .025*(range[2] - range[1])), max(original.inverse(partial.residuals.range[2]), range[2] + .025*(range[2] - range[1]))) tickmarks <- if (type == "response" && rescale.axis) make.ticks(ylim, link=trans.link, inverse=trans.inverse, at=ticks$at, n=ticks$n) else make.ticks(ylim, link=I, inverse=I, at=ticks$at, n=ticks$n) x.fit <- x.data[, predictors[x.var]] use <- rep(TRUE, length(residuals)) xx <- x[, predictors[-x.var], drop=FALSE] if (show.strip.values){ for (pred in predictors[-x.var]){ x[[pred]] <- as.factor(x[[pred]]) } } plot <- xyplot(eval(parse( text=paste("fit ~ trans(", predictors[x.var], ") |", paste(predictors[-x.var], collapse="*")))), strip=function(...) strip.default(..., strip.names=c(factor.names, TRUE), sep=" = "), panel=function(x, y, subscripts, x.vals, rug, lower, upper, has.se, ...){ if (grid) ticksGrid(x=tickmarks.x$at, y=tickmarks$at) good <- !is.na(y) effect.llines(x[good], y[good], lwd=lwd, col=colors[1], ...) if (rug && is.null(residuals)) lrug(trans(x.vals)) if (has.se){ if (ci.style == "bars"){ larrows(x0=x[good], y0=lower[subscripts][good], x1=x[good], y1=upper[subscripts][good], angle=90, code=3, col=eval(colors[.modc(2)]), length=.125*cex/1.5) } else if(ci.style == "lines") { effect.llines(x[good], lower[subscripts][good], lty=2, col=colors[.modc(2)]) effect.llines(x[good], upper[subscripts][good], lty=2, col=colors[.modc(2)]) } else if(ci.style == "bands") { panel.bands(x[good], y[good], upper[subscripts][good], lower[subscripts][good], fill=band.colors[1], alpha=band.transparency, use.splines=use.splines) } } if (!is.null(residuals)){ predictors <- predictors[-x.var] factors <- sapply(xx, is.factor) for (predictor in predictors){ use <- use & if(factors[predictor]) x.all[, predictor] == xx[subscripts[1], predictor] else x.all[, predictor] == xx[subscripts[1], predictor] } n.in.panel <- sum(use) if (n.in.panel > 0){ fitted <- y[good][closest(trans(x.fit[use]), x[good])] partial.res <- if (!rescale.axis) original.inverse(original.link(fitted) + residuals[use]) else fitted + residuals[use] lpoints(trans(x.fit[use]), partial.res, col=residuals.color, pch=residuals.pch, cex=residuals.cex) if (show.fitted) lpoints(trans(x.fit[use]), fitted, pch=16, col=residuals.color) # REMOVE ME if (smooth.residuals && n.in.panel >= 10) { llines(loess.smooth(x.fit[use], partial.res, span=span, family=loess.family), lwd=residuals.lwd, lty=residuals.lty, col=residuals.smooth.color) } if (id.n > 0){ M <- cbind(trans(x.fit[use]), partial.res) md <- mahalanobis(M, colMeans(M), cov(M)) biggest <- order(md, decreasing=TRUE)[1:id.n] pos <- ifelse(trans(x.fit[use][biggest]) > mean(current.panel.limits()$xlim), 2, 4) ltext(trans(x.fit[use][biggest]), partial.res[biggest], names(partial.res)[biggest], pos=pos, col=id.col, cex=id.cex) } } } if (has.thresholds){ panel.abline(h=thresholds, lty=3) panel.text(rep(current.panel.limits()$xlim[1], length(thresholds)), thresholds, threshold.labels, adj=c(0,0), cex=0.75) panel.text(rep(current.panel.limits()$xlim[2], length(thresholds)), thresholds, threshold.labels, adj=c(1,0), cex=0.75) } }, ylim=ylim, xlim=suppressWarnings(trans(xlm)), ylab=ylab, xlab=if (is.na(xlab)) predictors[x.var] else xlab, x.vals=x.vals, rug=rug, main=main, lower=x$lower, upper=x$upper, has.se=has.se, data=x, scales=list(y=list(at=tickmarks$at, labels=tickmarks$labels, rot=roty), x=list(at=tickmarks.x$at, labels=tickmarks.x$labels, rot=rotx), alternating=alternating), ...) result <- update(plot, layout = if (is.null(layout)) c(0, prod(dim(plot))) else layout) result$split <- split result$more <- more class(result) <- c("plot.eff", class(result)) } return(result) } print.plot.eff <- function(x, ...){ NextMethod(split=x$split, more=x$more, ...) invisible(x) } plot.efflist <- function(x, selection, rows, cols, ask=FALSE, graphics=TRUE, lattice, ...){ # Next line added 8/23/17 along with lattice, also lattice arg above lattice <- if(missing(lattice)) list() else lattice if (!missing(selection)){ if (is.character(selection)) selection <- gsub(" ", "", selection) return(plot(x[[selection]], ...)) } effects <- gsub(":", "*", names(x)) if (ask){ repeat { selection <- menu(effects, graphics=graphics, title="Select Term to Plot") if (selection == 0) break else print(plot(x[[selection]], ...)) } } else { neffects <- length(x) mfrow <- mfrow(neffects) if (missing(rows) || missing(cols)){ rows <- mfrow[1] cols <- mfrow[2] } for (i in 1:rows) { for (j in 1:cols){ if ((i-1)*cols + j > neffects) break more <- !((i-1)*cols + j == neffects) lattice[["array"]] <- list(row=i, col=j, nrow=rows, ncol=cols, more=more) print(plot(x[[(i-1)*cols + j]], lattice=lattice, ...)) } } } } effects/R/summary-print-methods.R0000644000176200001440000002507613156517507016531 0ustar liggesusers# plot, summary, and print methods for effects package # John Fox and Jangman Hong # last modified 2012-11-30 by J. Fox # 29 June 2011 added grid, rotx and roty arguments to the two plot methods # by S. Weisberg # 21 Dec 2012 modest modification of empty cells with crossed factors # 2013-01-17: Added factor.ci.style arg to plot.eff() and plot.effpoly(). J. Fox # 2013-01-18: Added CI bars to multiline plots with factor.ci.style="bars" # 2013-01-19: Renamed 'factor.ci.style' to 'ci.style'. Added a 'none' option # extended to variate terms if multiline=TRUE, ci.style="bars" # 2013-01-30: scale arrow "heads" for error bars relative to cex # 2013-05-31: fixed symbol colors in legends in plot.eff(). J. Fox # 2013-08-14: fixed bug in restoring warn option. J. Fox # 2013-08-27: fixed symbols argument for multiline plot in plot.eff(), reported by Ulrike Gromping. J. Fox # 2013-08-31: fixed handling of ticks.x argument. John # 2013-09-25: moved plot.eff methods to plot.methods.R for easier work. Michael # 2013-10-17: added use.splines argument to plot.effpoly. Sandy summary.eff <- function(object, type=c("response", "link"), ...){ result <- list() result$header <- paste("\n", gsub(":", "*", object$term), 'effect\n') result$offset <- object$offset type <- match.arg(type) if (type == "response") { object$fit <- object$transformation$inverse(object$fit) if (!is.null(object$confidence.level)){ object$lower <- object$transformation$inverse(object$lower) object$upper <- object$transformation$inverse(object$upper) } } result$effect <- array(object$fit, dim=sapply(object$variables, function(x) length(x$levels)), dimnames=lapply(object$variables, function(x) x$levels)) if (!is.null(object$se)){ result$lower.header <- paste('\n Lower', round(100*object$confidence.level, 2), 'Percent Confidence Limits\n') result$lower <- array(object$lower, dim=sapply(object$variables, function(x) length(x$levels)), dimnames=lapply(object$variables, function(x) x$levels)) result$upper.header <- paste('\n Upper', round(100*object$confidence.level, 2), 'Percent Confidence Limits\n') result$upper <- array(object$upper, dim=sapply(object$variables, function(x) length(x$levels)), dimnames=lapply(object$variables, function(x) x$levels)) } if (object$discrepancy > 1e-3) result$warning <- paste("\nWarning: There is an average discrepancy of", round(object$discrepancy, 3), "percent \n in the 'safe' predictions for effect", object$term, '\n') class(result) <- "summary.eff" result } print.summary.eff <- function(x, ...){ cat(x$header) if (x$offset != 0) cat("\noffset = ", x$offset, "\n\n") print(x$effect, ...) if (!is.null(x$lower)){ cat(x$lower.header) print(x$lower, ...) cat(x$upper.header) print(x$upper, ...) } if (!is.null(x$thresholds)){ cat("\nThresholds:\n") print(x$thresholds, ...) } if (!is.null(x$warning)) cat(x$warning) invisible(x) } print.eff <- function(x, type=c("response", "link"), ...){ cat(paste("\n", gsub(":", "*", x$term), 'effect\n')) if (x$offset != 0) cat("\noffset = ", x$offset, "\n\n") type <- match.arg(type) if (type == "response") x$fit <- x$transformation$inverse(x$fit) table <- array(x$fit, dim=sapply(x$variables, function(x) length(x$levels)), dimnames=lapply(x$variables, function(x) x$levels)) print(table, ...) if (x$discrepancy > 1e-3) cat(paste("\nWarning: There is an average discrepancy of", round(x$discrepancy, 3), "percent \n in the 'safe' predictions for effect", x$term, '\n')) invisible(x) } print.efflist <- function(x, ...){ cat(" model: ") form <- x[[1]]$formula attributes(form) <- NULL print(form) for (effect in names(x)){ print(x[[effect]], ...) } invisible(x) } summary.efflist <- function(object, ...){ cat(" model: ") form <- object[[1]]$formula attributes(form) <- NULL print(form) for (effect in names(object)){ print(summary(object[[effect]], ...)) } invisible(NULL) } print.effpoly <- function(x, type=c("probability", "logits"), ...){ type <- match.arg(type) x.frame <-as.data.frame(x) n.predictors <- length(names(x$x)) predictors <- names(x.frame)[1:n.predictors] y.lev <- x$y.lev ylevel.names <- make.names(paste("prob",y.lev)) colnames(x$prob) <- colnames(x$logit) <- ylevel.names y.categories <- matrix(0, nrow=length(x.frame[,predictors[1]]), ncol=length(y.lev)) for (i in 1:length(y.lev)){ level <- which(colnames(x$prob)[i] == ylevel.names) y.categories[,i] <- rep(y.lev[level], length(y.categories[,i])) } y.categories <- as.vector(y.categories) y.categories <- factor(y.categories) for (i in 1:length(y.lev)){ cat(paste("\n", gsub(":", "*", x$term), " effect (", type,") for ", y.lev[i], "\n", sep="")) table <- array(if (type == "probability") {x$prob[y.categories==y.lev[i]]} else {x$logit[y.categories==y.lev[i]]}, dim=sapply(x$variables, function(x) length(x$levels)), dimnames=lapply(x$variables, function(x) x$levels)) print(table, ...) } if (x$discrepancy > 0.1) cat(paste("\nWarning: There is an average discrepancy of", round(x$discrepancy, 2), "percent \n in the 'safe' predictions for effect", x$term, '\n')) invisible(x) } summary.effpoly <- function(object, type=c("probability", "logits"), ...){ type <- match.arg(type) x.frame <-as.data.frame(object) n.predictors <- length(names(object$x)) predictors <- names(x.frame)[1:n.predictors] y.lev <- object$y.lev ylevel.names <- make.names(paste("prob",y.lev)) colnames(object$prob) <- colnames(object$logit) <- colnames(object$lower.logit) <- colnames(object$upper.logit) <- colnames(object$lower.prob) <- colnames(object$upper.prob)<- ylevel.names y.categories <-matrix(0, nrow=length(x.frame[,predictors[1]]), ncol=length(y.lev)) for (i in 1:length(y.lev)){ level <- which(colnames(object$prob)[i] == ylevel.names) y.categories[,i] <- rep(y.lev[level], length(y.categories[,i])) } y.categories <- as.vector(y.categories) y.categories <- factor(y.categories) for (i in 1:length(y.lev)){ cat(paste("\n", gsub(":", "*", object$term), " effect (" , type, ") for ", y.lev[i], "\n", sep="")) table <- array(if (type == "probability") {object$prob[y.categories==y.lev[i]]} else {object$logit[y.categories==y.lev[i]]}, dim=sapply(object$variables, function(x) length(x$levels)), dimnames=lapply(object$variables, function(x) x$levels)) print(table, ...) } if (is.null(object$confidence.level)) return(invisible(NULL)) for (i in 1:length(y.lev)){ cat(paste("\n", 'Lower', object$confidence.level*100, 'Percent Confidence Limits for' , y.lev[i],'\n')) table <- if (type == "probability") object$lower.prob else object$lower.logit table <- array(table[y.categories==y.lev[i]], dim=sapply(object$variables, function(x) length(x$levels)), dimnames=lapply(object$variables, function(x) x$levels)) print(table, ...) } for (i in 1:length(y.lev)){ cat(paste("\n", 'Upper', object$confidence.level*100, 'Percent Confidence Limits for' , y.lev[i],'\n')) table <- if (type == "probability") object$upper.prob else object$upper.logit table <- array(table[y.categories==y.lev[i]], dim=sapply(object$variables, function(x) length(x$levels)), dimnames=lapply(object$variables, function(x) x$levels)) print(table, ...) } if (object$discrepancy > 0.1) cat(paste("\nWarning: There is an average discrepancy of", round(object$discrepancy, 2), "percent \n in the 'safe' predictions for effect", object$term, '\n')) invisible(NULL) } print.efflatent <- function(x, ...){ cat(paste("\n", gsub(":", "*", x$term), 'effect\n')) table <- array(x$fit, dim=sapply(x$variables, function(x) length(x$levels)), dimnames=lapply(x$variables, function(x) x$levels)) print(table, ...) cat("\nThresholds:\n") print(x$thresholds, ...) if (x$discrepancy > 0.1) cat(paste("\nWarning: There is an average discrepancy of", round(x$discrepancy, 3), "percent \n in the 'safe' predictions for effect", x$term, '\n')) invisible(x) } summary.efflatent <- function(object, ...){ result <- list() result$header <- paste("\n", gsub(":", "*", object$term), 'effect\n') result$effect <- array(object$fit, dim=sapply(object$variables, function(x) length(x$levels)), dimnames=lapply(object$variables, function(x) x$levels)) if (!is.null(object$se)){ result$lower.header <- paste('\n Lower', round(100*object$confidence.level, 2), 'Percent Confidence Limits\n') result$lower <- array(object$lower, dim=sapply(object$variables, function(x) length(x$levels)), dimnames=lapply(object$variables, function(x) x$levels)) result$upper.header <- paste('\n Upper', round(100*object$confidence.level, 2), 'Percent Confidence Limits\n') result$upper <- array(object$upper, dim=sapply(object$variables, function(x) length(x$levels)), dimnames=lapply(object$variables, function(x) x$levels)) } result$thresholds <- object$thresholds if (object$discrepancy > 0.1) result$warning <- paste("\nWarning: There is an average discrepancy of", round(object$discrepancy, 3), "percent \n in the 'safe' predictions for effect", object$term, '\n') class(result) <- "summary.eff" result } effects/R/predictorEffects.R0000644000176200001440000001275413156517466015537 0ustar liggesusers# last update: 2017-07-16 # 2017-08-14 fixed bug in plot.predictoreff on passing 'multiline' to lines list # 2017-08-30 for compatibility with other effect plots, default # is now multiline=FALSE predictorEffect <- function(predictor, mod, xlevels, ...){ UseMethod("predictorEffect", mod) } predictorEffect.svyglm <- function(predictor, mod, xlevels, ...){ mod$call <- list(mod$call, data=mod$data) NextMethod(object=mod) } predictorEffect.default <- function(predictor, mod, xlevels=list(), ...){ all.vars <- all.vars(formula(mod)) data <- na.omit(expand.model.frame(mod, all.vars)[, all.vars]) if (is.null(xlevels[[predictor]]) && is.numeric(data[[predictor]])){ xlevels[[predictor]] <- quantile(data[[predictor]], seq(0.01, 0.99, by=0.02)) } # find the right effect to use terms <- attr(terms(mod), "term.labels") # get the predictor names: predictors <- all.vars(parse(text=terms)) sel <- which(predictors == predictor) if(length(sel) != 1) stop("First argument must be the quoted name of one predictor in the formula") # create correspondence table decode <- function(name) all.vars(parse(text=unlist(strsplit(name, ":")))) tab <- rep(FALSE, length(terms)) for(j in 1:length(terms)){if(predictor %in% decode(terms[j])) tab[j] <- TRUE} ans <- unlist(strsplit(paste(terms[tab], collapse=":"), ":")) ans <- unique(all.vars(parse(text=ans))) result <- Effect(ans, mod, xlevels=xlevels, ...) class(result) <- c("predictoreff", "eff") result } predictorEffects <- function(mod, predictors = ~ ., ...){ # convert `predictors` arg to a list of predictors vterms <- if(is.character(predictors)) paste("~",predictors) else predictors vform <- update(formula(mod), vterms) vlabels <- attr(terms(vform), "term.labels") vpred <- all.vars(parse(text=vlabels)) # get list of predictors from the model mlabels <- attr(attr(model.frame(mod), "terms"), "term.labels") mpred <- all.vars(parse(text=mlabels)) # check that 'vpred' is a subset of 'mpred'. If so apply predictorEffect if(!all(vpred %in% mpred)) stop("argument 'predictors' not a subset of the predictors") else { result <- list() for(p in vpred) result[[p]] <- predictorEffect(p, mod, ...) } class(result) <- 'predictorefflist' result } # plot methods plot.predictoreff <- function(x, x.var, main = paste(names(x$variables)[1], "predictor effect plot"), ...){ if(missing(x.var)) x.var <- names(x$variables)[1] NextMethod(x, x.var=x.var, main=main, ...) } # This next function differs for plot.efflist only by changing the title on each plot plot.predictorefflist <- function(x, selection, rows, cols, ask=FALSE, graphics=TRUE, lattice, ...){ lattice <- if(missing(lattice)) list() else lattice if(length(x) == 1) plot(x[[1]], ...) else { if (!missing(selection)){ if (is.character(selection)) selection <- gsub(" ", "", selection) return(plot(x[[selection]], ...)) } effects <- gsub(":", "*", names(x)) if (ask){ repeat { selection <- menu(effects, graphics=graphics, title="Select Term to Plot") if (selection == 0) break else print(plot(x[[selection]], z.var=names(x)[selection], ...)) } } else { neffects <- length(x) mfrow <- mfrow(neffects) if (missing(rows) || missing(cols)){ rows <- mfrow[1] cols <- mfrow[2] } for (i in 1:rows) { for (j in 1:cols){ if ((i-1)*cols + j > neffects) break more <- !((i-1)*cols + j == neffects) lattice[["array"]] <- list(row=i, col=j, nrow=rows, ncol=cols, more=more) print(plot(x[[(i-1)*cols + j]], lattice=lattice, x.var=names(x)[(i-1)*cols + j], main=paste(names(x)[(i-1)*cols + j], "predictor effect plot"), ...)) } } } }} plot.predictorefflist <- function(x, selection, rows, cols, ask=FALSE, graphics=TRUE, lattice, ...){ # Next line added 8/23/17 along with lattice, also lattice arg above lattice <- if(missing(lattice)) list() else lattice if (!missing(selection)){ if (is.character(selection)) selection <- gsub(" ", "", selection) return(plot(x[[selection]], ...)) } effects <- gsub(":", "*", names(x)) if (ask){ repeat { selection <- menu(effects, graphics=graphics, title="Select Term to Plot") if (selection == 0) break else print(plot(x[[selection]], ...)) } } else { neffects <- length(x) mfrow <- mfrow(neffects) if (missing(rows) || missing(cols)){ rows <- mfrow[1] cols <- mfrow[2] } for (i in 1:rows) { for (j in 1:cols){ if ((i-1)*cols + j > neffects) break more <- !((i-1)*cols + j == neffects) lattice[["array"]] <- list(row=i, col=j, nrow=rows, ncol=cols, more=more) print(plot(x[[(i-1)*cols + j]], lattice=lattice, ...)) } } } } # print and summary methods print.predictorefflist <- function(x, ...){ for (eff in x){ print(eff, ...) } invisible(x) } print.predictoreff <- function(x, ...){ cat("\n", names(x$variables)[1], "predictor effect\n") NextMethod() } summary.predictorefflist <- function(object, ...){ for (eff in object){ cat("\n", names(eff$variables)[1], "predictor effect\n") print(summary(eff, ...)) } } effects/R/Effect.R0000644000176200001440000007522213156517033013425 0ustar liggesusers# Effect generic and methods # John Fox and Sanford Weisberg # 12-21-2012 Allow for empty cells in factor interactions, S. Weisberg # 2012-03-05: Added .merMod method for development version of lme4, J. Fox # 2012-04-06: Added support for lme4.0, J. Fox # 2013-07-15: Changed default xlevels and default.levels # 2013-10-15: Added Effect.default(). J. Fox # 2013-10-22: fixed bug in Effect.lm() when na.action=na.exclude. J. Fox # 2013-10-29: code to handle "valid" NAs in factors. J. Fox # 2013-11-06: fixed bug in Effect.multinom() in construction of effect object # when there is only one focal predictor; caused as.data.frame.effpoly() to fail # 2014-03-13: modified Effect.lm() to compute partial residuals. J. Fox # 2014-05-06: fixed bug in Effect.gls() when cor or var structure depends on variables in the data set. J. Fox # 2014-08-02: added vcov.=vcov argument to allow other methods of estimating var(coef.estimates) # 2014-09-25: added KR argument to Effect.mer() and Effect.merMod(). J. Fox # 2014-12-07: don't assume that pbkrtest is installed. J. Fox # 2015-03-25: added "family" element to eff objects returned by Effect.lm(). J. Fox # 2016-02-16: fixed problem in handling terms like polynomials for non-focal predictors. J. Fox # 2016-03-01: recoded calculation of partial residuals. J. Fox # 2016-07-19: added checkFormula(). J. Fox # 2017-08-18: removed default.levels argument. J. Fox # 2017-08-26: introduced confint list argument, including Scheffe intervals. J. Fox # 2017-08-29: reintroduce legacy se and confidence.level arguments. # 2017-09-07: added Effect.svyglm() # 2017-09-14: no partial residuals for Effect.svyglm() checkFormula <- function(object){ if (!inherits(object, "formula")){ object <- formula(object) } formula <- as.character(object) rhs <- formula[length(formula)] res <- regexpr("as.factor\\(|factor\\(|as.ordered\\(|ordered\\(|as.numeric\\(|as.integer\\(", rhs) res == -1 || attr(res, "match.length") == 0 } Effect <- function(focal.predictors, mod, ...){ if (!checkFormula(mod)) stop("model formula should not contain calls to", "\n factor(), as.factor(), ordered(), as.ordered(),", " as.numeric(), or as.integer();", "\n see 'Warnings and Limitations' in ?Effect") UseMethod("Effect", mod) } Effect.lm <- function(focal.predictors, mod, xlevels=list(), fixed.predictors, vcov. = vcov, confint=TRUE, transformation = list(link = family(mod)$linkfun, inverse = family(mod)$linkinv), partial.residuals=FALSE, quantiles=seq(0.2, 0.8, by=0.2), x.var=NULL, ..., #legacy arguments: given.values, typical, offset, se, confidence.level){ if (missing(fixed.predictors)) fixed.predictors <- NULL fixed.predictors <- applyDefaults(fixed.predictors, list(given.values=NULL, typical=mean, apply.typical.to.factors=FALSE, offset=mean), arg="fixed.predictors") if (missing(given.values)) given.values <- fixed.predictors$given.values if (missing(typical)) typical <- fixed.predictors$typical if (missing(offset)) offset <- fixed.predictors$offset apply.typical.to.factors <- fixed.predictors$apply.typical.to.factors confint <- applyDefaults(confint, list(compute=TRUE, level=.95, type="pointwise"), onFALSE=list(compute=FALSE, level=.95, type="pointwise"), arg="confint") if (missing(se)) se <- confint$compute if (missing(confidence.level)) confidence.level <- confint$level confidence.type <- match.arg(confint$type, c("pointwise", "Scheffe", "scheffe")) default.levels <- NULL # just for backwards compatibility data <- if (partial.residuals){ all.vars <- all.vars(formula(mod)) expand.model.frame(mod, all.vars)[, all.vars] } else NULL if (!is.null(given.values) && !all(which <- names(given.values) %in% names(coef(mod)))) stop("given.values (", names(given.values[!which]), ") not in the model") off <- if (is.numeric(offset) && length(offset) == 1) offset else if (is.function(offset)) { mod.off <- model.offset(model.frame(mod)) if (is.null(mod.off)) 0 else offset(mod.off) } else stop("offset must be a function or a number") formula.rhs <- formula(mod)[[3]] model.components <- Analyze.model(focal.predictors, mod, xlevels, default.levels, formula.rhs, partial.residuals=partial.residuals, quantiles=quantiles, x.var=x.var, data=data, typical=typical) excluded.predictors <- model.components$excluded.predictors predict.data <- model.components$predict.data predict.data.all.rounded <- predict.data.all <- if (partial.residuals) na.omit(data[, all.vars(formula(mod))]) else NULL factor.levels <- model.components$factor.levels factor.cols <- model.components$factor.cols n.focal <- model.components$n.focal x <- model.components$x X.mod <- model.components$X.mod cnames <- model.components$cnames X <- model.components$X x.var <- model.components$x.var formula.rhs <- formula(mod)[c(1, 3)] Terms <- delete.response(terms(mod)) mf <- model.frame(Terms, predict.data, xlev = factor.levels, na.action=NULL) mod.matrix <- model.matrix(formula.rhs, data = mf, contrasts.arg = mod$contrasts) if (is.null(x.var)) partial.residuals <- FALSE factors <- sapply(predict.data, is.factor) if (partial.residuals){ for (predictor in focal.predictors[-x.var]){ if (!factors[predictor]){ values <- unique(predict.data[, predictor]) predict.data.all.rounded[, predictor] <- values[apply(outer(predict.data.all[, predictor], values, function(x, y) (x - y)^2), 1, which.min)] } } } mod.matrix.all <- model.matrix(mod) wts <- weights(mod) if (is.null(wts)) wts <- rep(1, length(residuals(mod))) mod.matrix <- Fixup.model.matrix(mod, mod.matrix, mod.matrix.all, X.mod, factor.cols, cnames, focal.predictors, excluded.predictors, typical, given.values, apply.typical.to.factors) #, # look for aliased coefficients and remove those columns from mod.matrix mod.matrix <- mod.matrix[, !is.na(mod$coefficients)] effect <- off + mod.matrix %*% mod$coefficients[!is.na(mod$coefficients)] if (partial.residuals){ res <- na.omit(residuals(mod, type="working")) fitted <- na.omit(if (inherits(mod, "glm")) predict(mod, type="link") else predict(mod)) partial.residuals.range <- range(fitted + res) } else { res <- partial.residuals.range <- NULL } result <- list(term = paste(focal.predictors, collapse="*"), formula = formula(mod), response = response.name(mod), variables = x, fit = effect, x = predict.data[, 1:n.focal, drop=FALSE], x.all=predict.data.all.rounded[, focal.predictors, drop=FALSE], model.matrix = mod.matrix, data = X, discrepancy = 0, offset=off, residuals=res, partial.residuals.range=partial.residuals.range, x.var=x.var) # find empty cells, if any, and correct whichFact <- unlist(lapply(result$variables, function(x) x$is.factor)) zeroes <- NULL if(sum(whichFact) > 1){ nameFact <- names(whichFact)[whichFact] counts <- xtabs(as.formula( paste("~", paste(nameFact, collapse="+"))), model.frame(mod)) zeroes <- which(counts == 0) } if(length(zeroes) > 0){ levs <- expand.grid(lapply(result$variables, function(x) x$levels)) good <- rep(TRUE, dim(levs)[1]) for(z in zeroes){ good <- good & apply(levs, 1, function(x) !all(x == levs[z, whichFact])) } result$fit[!good] <- NA } if (se) { if (any(family(mod)$family == c("binomial", "poisson"))) { z <- if (confidence.type == "pointwise") { qnorm(1 - (1 - confidence.level)/2) } else { p <- length(na.omit(coef(mod))) scheffe(confidence.level, p) } } else { z <- if (confidence.type == "pointwise") { qt(1 - (1 - confidence.level)/2, df = mod$df.residual) } else { p <- length(na.omit(coef(mod))) scheffe(confidence.level, p, mod$df.residual) } } V <- vcov.(mod) eff.vcov <- mod.matrix %*% V %*% t(mod.matrix) rownames(eff.vcov) <- colnames(eff.vcov) <- NULL var <- diag(eff.vcov) result$vcov <- eff.vcov result$se <- sqrt(var) result$lower <- effect - z * result$se result$upper <- effect + z * result$se result$confidence.level <- confidence.level if(length(zeroes) > 0){ result$se[!good] <- NA result$lower[!good] <- NA result$upper[!good] <- NA } } if (is.null(transformation$link) && is.null(transformation$inverse)) { transformation$link <- I transformation$inverse <- I } result$transformation <- transformation result$family <- family(mod)$family class(result) <- "eff" result } Effect.mer <- function(focal.predictors, mod, KR=FALSE, ...) { result <- Effect(focal.predictors, mer.to.glm(mod, KR=KR), ...) result$formula <- as.formula(formula(mod)) result } Effect.merMod <- function(focal.predictors, mod, KR=FALSE, ...){ Effect.mer(focal.predictors, mod, KR=KR, ...) } Effect.lme <- function(focal.predictors, mod, ...) { result <- Effect(focal.predictors, lme.to.glm(mod), ...) result$formula <- as.formula(formula(mod)) result } Effect.gls <- function(focal.predictors, mod, xlevels = list(), fixed.predictors, vcov. = vcov, confint=TRUE, transformation = NULL, ..., #legacy arguments: given.values, typical, se, confidence.level){ if (missing(fixed.predictors)) fixed.predictors <- NULL fixed.predictors <- applyDefaults(fixed.predictors, list(given.values=NULL, typical=mean), arg="fixed.predictors") if (missing(given.values)) given.values <- fixed.predictors$given.values if (missing(typical)) typical <- fixed.predictors$typical confint <- applyDefaults(confint, list(compute=TRUE, level=.95, type="pointwise"), onFALSE=list(compute=FALSE, level=.95, type="pointwise"), arg="confint") if (missing(se)) se <- confint$compute if (missing(confidence.level)) confidence.level <- confint$level confidence.type <- match.arg(confint$type, c("pointwise", "Scheffe", "scheffe")) default.levels <- NULL # just for backwards compatibility if (missing(given.values)) given.values <- NULL else if (!all(which <- names(given.values) %in% names(coef(mod)))) stop("given.values (", names(given.values[!which]), ") not in the model") formula.rhs <- formula(mod)[[3]] .data <- eval(mod$call$data) mod.lm <- lm(as.formula(mod$call$model), data=.data, na.action=na.exclude) model.components <- Analyze.model(focal.predictors, mod.lm, xlevels, default.levels, formula.rhs, typical=typical) excluded.predictors <- model.components$excluded.predictors predict.data <- model.components$predict.data factor.levels <- model.components$factor.levels factor.cols <- model.components$factor.cols n.focal <- model.components$n.focal x <- model.components$x X.mod <- model.components$X.mod cnames <- model.components$cnames X <- model.components$X formula.rhs <- formula(mod)[c(1, 3)] nrow.X <- nrow(X) mf <- model.frame(formula.rhs, data=rbind(X[,names(predict.data),drop=FALSE], predict.data), xlev=factor.levels) mod.matrix.all <- model.matrix(formula.rhs, data=mf, contrasts.arg=mod$contrasts) mod.matrix <- mod.matrix.all[-(1:nrow.X),] mod.matrix <- Fixup.model.matrix(mod.lm, mod.matrix, model.matrix(mod.lm), X.mod, factor.cols, cnames, focal.predictors, excluded.predictors, typical, given.values) fit.1 <- na.omit(predict(mod)) mod.2 <- lm.fit(mod.matrix.all[1:nrow.X,], fit.1) class(mod.2) <- "lm" use <- !is.na(residuals(mod.lm)) .data <- .data[use, ] .data$.y <- model.response.gls(mod) .data$.X <- mod.matrix.all[1:nrow.X, ] mod.3 <- update(mod, .y ~ .X - 1, data=.data) discrepancy <- 100*mean(abs(fitted(mod.2)- fit.1)/(1e-10 + mean(abs(fit.1)))) if (discrepancy > 1e-3) warning(paste("There is a discrepancy of", round(discrepancy, 3), "percent \n in the 'safe' predictions used to generate effect", paste(focal.predictors, collapse="*"))) effect <- mod.matrix %*% mod$coefficients result <- list(term = paste(focal.predictors, collapse="*"), formula = formula(mod), response = response.name(mod), variables = x, fit = effect, x = predict.data[, 1:n.focal, drop=FALSE], model.matrix = mod.matrix, data = X, discrepancy = discrepancy, offset=0) if (se){ p <- mod$dims[["p"]] df.res <- mod$dims[["N"]] - p z <- if (confidence.type == "pointwise") { qt(1 - (1 - confidence.level)/2, df = df.res) } else { scheffe(confidence.level, p, df.res) } mod.2$terms <- terms(mod) V <- vcov.(mod.3) eff.vcov <- mod.matrix %*% V %*% t(mod.matrix) rownames(eff.vcov) <- colnames(eff.vcov) <- NULL var <- diag(eff.vcov) result$vcov <- eff.vcov result$se <- sqrt(var) result$lower <- effect - z*result$se result$upper <- effect + z*result$se result$confidence.level <- confidence.level } if (is.null(transformation$link) && is.null(transformation$inverse)){ transformation$link <- I transformation$inverse <- I } result$transformation <- transformation class(result) <- "eff" result } Effect.multinom <- function(focal.predictors, mod, xlevels=list(), fixed.predictors, vcov. = vcov, confint=TRUE, ..., #legacy arguments: se, confidence.level, given.values, typical){ if (missing(fixed.predictors)) fixed.predictors <- NULL fixed.predictors <- applyDefaults(fixed.predictors, list(given.values=NULL, typical=mean), arg="fixed.predictors") if (missing(given.values)) given.values <- fixed.predictors$given.values if (missing(typical)) typical <- fixed.predictors$typical confint <- applyDefaults(confint, list(compute=TRUE, level=.95, type="pointwise"), onFALSE=list(compute=FALSE, level=.95, type="pointwise"), arg="confint") if (missing(se)) se <- confint$compute if (missing(confidence.level)) confidence.level <- confint$level confidence.type <- match.arg(confint$type, c("pointwise", "Scheffe", "scheffe")) default.levels <- NULL # just for backwards compatibility if (length(mod$lev) < 3) stop("effects for multinomial logit model only available for response levels > 2") if (missing(given.values)) given.values <- NULL else if (!all(which <- colnames(given.values) %in% names(coef(mod)))) stop("given.values (", colnames(given.values[!which]),") not in the model") formula.rhs <- formula(mod)[c(1, 3)] model.components <- Analyze.model(focal.predictors, mod, xlevels, default.levels, formula.rhs, typical=typical) excluded.predictors <- model.components$excluded.predictors predict.data <- model.components$predict.data factor.levels <- model.components$factor.levels factor.cols <- model.components$factor.cols # n.focal <- model.components$n.focal x <- model.components$x X.mod <- model.components$X.mod cnames <- model.components$cnames X <- model.components$X formula.rhs <- formula(mod)[c(1, 3)] Terms <- delete.response(terms(mod)) mf <- model.frame(Terms, predict.data, xlev = factor.levels) mod.matrix <- model.matrix(formula.rhs, data = mf, contrasts.arg = mod$contrasts) X0 <- Fixup.model.matrix(mod, mod.matrix, model.matrix(mod), X.mod, factor.cols, cnames, focal.predictors, excluded.predictors, typical, given.values) resp.names <- make.names(mod$lev, unique=TRUE) resp.names <- c(resp.names[-1], resp.names[1]) # make the last level the reference level B <- t(coef(mod)) V <- vcov.(mod) m <- ncol(B) + 1 p <- nrow(B) r <- p*(m - 1) n <- nrow(X0) P <- Logit <- matrix(0, n, m) colnames(P) <- paste("prob.", resp.names, sep="") colnames(Logit) <- paste("logit.", resp.names, sep="") if (se){ z <- if (confidence.type == "pointwise") { qnorm(1 - (1 - confidence.level)/2) } else { scheffe(confidence.level, p) } Lower.P <- Upper.P <- Lower.logit <- Upper.logit <- SE.P <- SE.logit <- matrix(0, n, m) colnames(Lower.logit) <- paste("L.logit.", resp.names, sep="") colnames(Upper.logit) <- paste("U.logit.", resp.names, sep="") colnames(Lower.P) <- paste("L.prob.", resp.names, sep="") colnames(Upper.P) <- paste("U.prob.", resp.names, sep="") colnames(SE.P) <- paste("se.prob.", resp.names, sep="") colnames(SE.logit) <- paste("se.logit.", resp.names, sep="") } for (i in 1:n){ res <- eff.mul(X0[i,], B, se, m, p, r, V) # compute effects # P[i,] <- prob <- res$p # fitted probabilities P[i,] <- res$p # fitted probabilities Logit[i,] <- logit <- res$logits # fitted logits if (se){ # SE.P[i,] <- se.p <- res$std.err.p # std. errors of fitted probs SE.P[i,] <- res$std.err.p # std. errors of fitted probs SE.logit[i,] <- se.logit <- res$std.error.logits # std. errors of logits Lower.P[i,] <- logit2p(logit - z*se.logit) Upper.P[i,] <- logit2p(logit + z*se.logit) Lower.logit[i,] <- logit - z*se.logit Upper.logit[i,] <- logit + z*se.logit } } resp.levs <- c(m, 1:(m-1)) # restore the order of the levels P <- P[, resp.levs] Logit <- Logit[, resp.levs] if (se){ Lower.P <- Lower.P[, resp.levs] Upper.P <- Upper.P[, resp.levs] Lower.logit <- Lower.logit[, resp.levs] Upper.logit <- Upper.logit[, resp.levs] SE.P <- SE.P[, resp.levs] SE.logit <- SE.logit[, resp.levs] } result <- list(term=paste(focal.predictors, collapse="*"), formula=formula(mod), response=response.name(mod), y.levels=mod$lev, variables=x, x=predict.data[, focal.predictors, drop=FALSE], model.matrix=X0, data=X, discrepancy=0, model="multinom", prob=P, logit=Logit) if (se) result <- c(result, list(se.prob=SE.P, se.logit=SE.logit, lower.logit=Lower.logit, upper.logit=Upper.logit, lower.prob=Lower.P, upper.prob=Upper.P, confidence.level=confidence.level)) # find empty cells, if any, and correct whichFact <- unlist(lapply(result$variables, function(x) x$is.factor)) zeroes <- NULL if(sum(whichFact) > 1){ nameFact <- names(whichFact)[whichFact] counts <- xtabs(as.formula( paste("~", paste(nameFact, collapse="+"))), model.frame(mod)) zeroes <- which(counts == 0) } if(length(zeroes) > 0){ levs <- expand.grid(lapply(result$variables, function(x) x$levels)) good <- rep(TRUE, dim(levs)[1]) for(z in zeroes){ good <- good & apply(levs, 1, function(x) !all(x == levs[z, whichFact])) } result$prob[!good, ] <- NA result$logit[!good, ] <- NA if (se){ result$se.prob[!good, ] <- NA result$se.logit[!good, ] <- NA result$lower.prob[!good, ] <- NA result$upper.prob[!good, ] <- NA } } class(result) <-'effpoly' result } Effect.polr <- function(focal.predictors, mod, xlevels=list(), fixed.predictors, vcov.=vcov, confint=TRUE, latent=FALSE, ..., #legacy arguments: se, confidence.level, given.values, typical){ if (missing(fixed.predictors)) fixed.predictors <- NULL fixed.predictors <- applyDefaults(fixed.predictors, list(given.values=NULL, typical=mean), arg="fixed.predictors") if (missing(given.values)) given.values <- fixed.predictors$given.values if (missing(typical)) typical <- fixed.predictors$typical confint <- applyDefaults(confint, list(compute=TRUE, level=.95, type="pointwise"), onFALSE=list(compute=FALSE, level=.95, type="pointwise"), arg="confint") if (missing(se)) se <- confint$compute if (missing(confidence.level)) confidence.level <- confint$level confidence.type <- match.arg(confint$type, c("pointwise", "Scheffe", "scheffe")) default.levels <- NULL # just for backwards compatibility if (mod$method != "logistic") stop('method argument to polr must be "logistic"') if (missing(given.values)) given.values <- NULL else if (!all(which <- names(given.values) %in% names(coef(mod)))) stop("given.values (", names(given.values[!which]),") not in the model") formula.rhs <- formula(mod)[c(1, 3)] model.components <- Analyze.model(focal.predictors, mod, xlevels, default.levels, formula.rhs, typical=typical) excluded.predictors <- model.components$excluded.predictors predict.data <- model.components$predict.data factor.levels <- model.components$factor.levels factor.cols <- model.components$factor.cols # n.focal <- model.components$n.focal x <- model.components$x X.mod <- model.components$X.mod cnames <- model.components$cnames X <- model.components$X Terms <- delete.response(terms(mod)) mf <- model.frame(Terms, predict.data, xlev = factor.levels, na.action=NULL) mod.matrix <- model.matrix(formula.rhs, data = mf, contrasts.arg = mod$contrasts) X0 <- Fixup.model.matrix(mod, mod.matrix, model.matrix(mod), X.mod, factor.cols, cnames, focal.predictors, excluded.predictors, typical, given.values) resp.names <- make.names(mod$lev, unique=TRUE) X0 <- X0[,-1, drop=FALSE] b <- coef(mod) p <- length(b) # corresponds to p - 1 in the text alpha <- - mod$zeta # intercepts are negatives of thresholds z <- if (confidence.type == "pointwise") { qnorm(1 - (1 - confidence.level)/2) } else { scheffe(confidence.level, p + length(alpha)) } result <- list(term=paste(focal.predictors, collapse="*"), formula=formula(mod), response=response.name(mod), y.levels=mod$lev, variables=x, x=predict.data[, focal.predictors, drop=FALSE], model.matrix=X0, data=X, discrepancy=0, model="polr") if (latent){ res <- eff.latent(X0, b, vcov.(mod)[1:p, 1:p], se) result$fit <- res$fit if (se){ result$se <- res$se result$lower <- result$fit - z*result$se result$upper <- result$fit + z*result$se result$confidence.level <- confidence.level } transformation <- list() transformation$link <- I transformation$inverse <- I result$transformation <- transformation result$thresholds <- -alpha class(result) <- c("efflatent", "eff") return(result) } m <- length(alpha) + 1 r <- m + p - 1 indices <- c((p+1):r, 1:p) V <- vcov.(mod)[indices, indices] for (j in 1:(m-1)){ # fix up the signs of the covariances V[j,] <- -V[j,] # for the intercepts V[,j] <- -V[,j]} n <- nrow(X0) P <- Logit <- matrix(0, n, m) colnames(P) <- paste("prob.", resp.names, sep="") colnames(Logit) <- paste("logit.", resp.names, sep="") if (se){ Lower.logit <- Upper.logit <- Lower.P <- Upper.P <- SE.P <- SE.Logit <- matrix(0, n, m) colnames(Lower.logit) <- paste("L.logit.", resp.names, sep="") colnames(Upper.logit) <- paste("U.logit.", resp.names, sep="") colnames(Lower.P) <- paste("L.prob.", resp.names, sep="") colnames(Upper.P) <- paste("U.prob.", resp.names, sep="") colnames(SE.P) <- paste("se.prob.", resp.names, sep="") colnames(SE.Logit) <- paste("se.logit.", resp.names, sep="") } for (i in 1:n){ res <- eff.polr(X0[i,], b, alpha, V, m, r, se) # compute effects P[i,] <- res$p # fitted probabilities Logit[i,] <- logit <- res$logits # fitted logits if (se){ SE.P[i,] <- res$std.err.p # std. errors of fitted probs SE.Logit[i,] <- se.logit <- res$std.error.logits # std. errors of logits Lower.P[i,] <- logit2p(logit - z*se.logit) Upper.P[i,] <- logit2p(logit + z*se.logit) Lower.logit[i,] <- logit - z*se.logit Upper.logit[i,] <- logit + z*se.logit } } result$prob <- P result$logit <- Logit if (se) result <- c(result, list(se.prob=SE.P, se.logit=SE.Logit, lower.logit=Lower.logit, upper.logit=Upper.logit, lower.prob=Lower.P, upper.prob=Upper.P, confidence.level=confidence.level)) class(result) <-'effpoly' result } Effect.default <- function(focal.predictors, mod, xlevels = list(), fixed.predictors, vcov. = vcov, confint=TRUE, transformation = list(link = I, inverse = I), ..., #legacy arguments: se, confidence.level, given.values, typical, offset){ if (missing(fixed.predictors)) fixed.predictors <- NULL fixed.predictors <- applyDefaults(fixed.predictors, list(given.values=NULL, typical=mean), arg="fixed.predictors") if (missing(given.values)) given.values <- fixed.predictors$given.values if (missing(typical)) typical <- fixed.predictors$typical confint <- applyDefaults(confint, list(compute=TRUE, level=.95, type="pointwise"), onFALSE=list(compute=FALSE, level=.95, type="pointwise"), arg="confint") if (missing(se)) se <- confint$compute if (missing(confidence.level)) confidence.level <- confint$level confidence.type <- match.arg(confint$type, c("pointwise", "Scheffe", "scheffe")) default.levels <- NULL # just for backwards compatibility if (missing(given.values)) given.values <- NULL else if (!all(which <- names(given.values) %in% names(coef(mod)))) stop("given.values (", names(given.values[!which]), ") not in the model") off <- if (is.numeric(offset) && length(offset) == 1) offset else if (is.function(offset)) { mod.off <- model.offset(model.frame(mod)) if (is.null(mod.off)) 0 else offset(mod.off) } else stop("offset must be a function or a number") formula.rhs <- formula(mod)[[3]] model.components <- Analyze.model(focal.predictors, mod, xlevels, default.levels, formula.rhs, typical=typical) excluded.predictors <- model.components$excluded.predictors predict.data <- model.components$predict.data factor.levels <- model.components$factor.levels factor.cols <- model.components$factor.cols n.focal <- model.components$n.focal x <- model.components$x X.mod <- model.components$X.mod cnames <- model.components$cnames X <- model.components$X formula.rhs <- formula(mod)[c(1, 3)] Terms <- delete.response(terms(mod)) mf <- model.frame(Terms, predict.data, xlev = factor.levels, na.action=NULL) mod.matrix <- model.matrix(formula.rhs, data = mf, contrasts.arg = mod$contrasts) mod.matrix <- Fixup.model.matrix(mod, mod.matrix, model.matrix(mod), X.mod, factor.cols, cnames, focal.predictors, excluded.predictors, typical, given.values) mod.matrix <- mod.matrix[, !is.na(coef(mod))] effect <- off + mod.matrix %*% mod$coefficients[!is.na(coef(mod))] result <- list(term = paste(focal.predictors, collapse="*"), formula = formula(mod), response = response.name(mod), variables = x, fit = effect, x = predict.data[, 1:n.focal, drop=FALSE], model.matrix = mod.matrix, data = X, discrepancy = 0, offset=off) whichFact <- unlist(lapply(result$variables, function(x) x$is.factor)) zeroes <- NULL if(sum(whichFact) > 1){ nameFact <- names(whichFact)[whichFact] counts <- xtabs(as.formula( paste("~", paste(nameFact, collapse="+"))), model.frame(mod)) zeroes <- which(counts == 0) } if(length(zeroes) > 0){ levs <- expand.grid(lapply(result$variables, function(x) x$levels)) good <- rep(TRUE, dim(levs)[1]) for(z in zeroes){ good <- good & apply(levs, 1, function(x) !all(x == levs[z, whichFact])) } result$fit[!good] <- NA } if (se) { z <- if (confidence.type == "pointwise") { qnorm(1 - (1 - confidence.level)/2) } else { p <- length(na.omit(coef(mod))) scheffe(confidence.level, p) } V <- vcov.(mod) eff.vcov <- mod.matrix %*% V %*% t(mod.matrix) rownames(eff.vcov) <- colnames(eff.vcov) <- NULL var <- diag(eff.vcov) result$vcov <- eff.vcov result$se <- sqrt(var) result$lower <- effect - z * result$se result$upper <- effect + z * result$se result$confidence.level <- confidence.level if(length(zeroes) > 0){ result$se[!good] <- NA result$lower[!good] <- NA result$upper[!good] <- NA } } result$transformation <- transformation class(result) <- "eff" result } Effect.svyglm <- function(focal.predictors, mod, fixed.predictors, ...){ Svymean <- function(x){ svymean(x, design=mod$survey.design) } ellipses.list <- list(...) if (!is.null(ellipses.list$partial.residuals) && !isFALSE(ellipses.list$partial.residuals)){ stop("partial residuals are not available for svy.glm models") } if (missing(fixed.predictors)) fixed.predictors <- NULL fixed.predictors <- applyDefaults(fixed.predictors, list(given.values=NULL, typical=Svymean, apply.typical.to.factors=TRUE, offset=Svymean), arg="fixed.predictors") typical <- fixed.predictors$typical apply.typical.to.factors <- fixed.predictors$apply.typical.to.factors offset <- fixed.predictors$offset mod$call <- list(mod$call, data=mod$data) Effect.lm(focal.predictors, mod, typical=typical, apply.typical.to.factors=apply.typical.to.factors, offset=offset, ...) } effects/R/Effect.mlm.R0000644000176200001440000000302413156516710014201 0ustar liggesusers# Calculate Effects for term(s) in a Multivariate Linear Model # 2014-03-12: Introduced allEffects.mlm(). J. Fox Effect.mlm <- function(focal.predictors, mod, response, ...) { if (missing(response)) { mod.frame <- model.frame(mod) response <- colnames(model.response(mod.frame)) } else if (is.numeric(response)) { mod.frame <- model.frame(mod) response.names <- colnames(model.response(mod.frame)) response <- response.names[response] } if (length(response)==1) { mod.1 <- update(mod, as.formula(paste(response, " ~ ."))) result <- Effect(focal.predictors, mod.1, ...) } else { result <- as.list(NULL) for (resp in response) { mod.1 <- update(mod, as.formula(paste(resp, " ~ ."))) lab <- resp result[[lab]] <- Effect(focal.predictors, mod.1, ...) } class(result) <- "efflist" } result } allEffects.mlm <- function(mod, ...){ result <- NextMethod() class(result) <- "mlm.efflist" result } plot.mlm.efflist <- function(x, ...){ x <- do.call(c, x) class(x) <- "efflist" plot(x, ...) } summary.mlm.efflist <- function(object, ...){ object <- do.call(c, object) for (effect in names(object)){ cat("\n\nResponse:", object[[effect]]$response, "\n") print(summary(object[[effect]], ...)) } } print.mlm.efflist <- function(x, ...){ x <- do.call(c, x) for (effect in names(x)){ cat("\n\nResponse:", x[[effect]]$response, "\n") print(x[[effect]], ...) } invisible(x) } effects/vignettes/0000755000176200001440000000000013157030525013702 5ustar liggesuserseffects/vignettes/partial-residuals.bib0000644000176200001440000000153713156306127020016 0ustar liggesusers@Misc{Schumann15, Author = {E. Schumann}, Title = {\emph{Generating Correlated Uniform Variates}}, Note = {\url{http://comisef.wikidot.com/ tutorial:correlateduniformvariates} [Accessed: 2015-05-21]}, year = 2009 } @book{Pearson07, Author={Karl Pearson}, Title={Mathematical Contributions to the Theory of Evolution.---XVI. On Further Methods of Determining Correlation}, Series={Drapers' Company Research Memoirs. Biometric Series. IV.}, Publisher={Cambridge University Press}, Address={London}, year=1907 } @article{FoxWeisberg17, author={John Fox and Sanford Weisberg}, title={Visualizing Fit and Lack of Fit in Complex Regression Models: with Predictor Effect Plots and Partial Residuals}, journal={Journal of Statistical Software}, year=2017, volume={forthcoming} } effects/vignettes/partial-residuals.Rnw0000644000176200001440000004522613157026516020035 0ustar liggesusers%\VignetteEngine{knitr::knitr} %\VignetteIndexEntry{Partial Residuals} \documentclass{article} \usepackage{amsmath,amsfonts,amssymb} \usepackage{natbib} \bibliographystyle{abbrvnat} \usepackage[margin=1in]{geometry} \newcommand{\x}{\mathbf{x}} \newcommand{\code}[1]{\normalfont\texttt{\hyphenchar\font45\relax #1}} \newcommand{\E}{\mathrm{E}} \newcommand{\tild}{\symbol{126}} \newcommand{\Rtilde}{\,\raisebox{-.5ex}{\code{\tild{}}}\,} \newcommand{\captilde}{\mbox{\protect\Rtilde}} % use in figure captions. \newcommand{\Rmod}[2]{\code{#1 \raisebox{-.5ex}{\tild{}} #2}} \newcommand{\Rmoda}[2]{\code{#1} &\code{\raisebox{-.5ex}{\tild{}} #2}} \newcommand{\Rmodb}[2]{\code{#1 &\raisebox{-.5ex}{\tild{}}& #2}} \newcommand{\C}{\mathbf{C}} \newcommand{\betahat}{\widehat{\beta}} \newcommand{\bbetahat}{\widehat{\boldsymbol{\beta}}} \newcommand{\bbeta}{\boldsymbol{\beta}} \newcommand{\xbf}{\x_{\backslash{}f}} \newcommand{\hbf}{h_{\backslash{}f}} \newcommand{\xtb}{\x_{2\backslash{}f}} \newcommand{\xbfi}{\x_{\backslash{}f,i}} \newcommand{\inter}[2]{\mbox{$#1$:$#2$}} \newcommand{\cross}[2]{\mbox{$#1$\code{*}$#2$}} \newcommand{\N}{\mathrm{N}} \newcommand{\fn}{\textbf} \newcommand{\R}{\proglang{R}} \newcommand{\yx}{\widehat{y}(\x)} \newcommand{\lvn}[1]{\mbox{$\log(\mbox{\texttt{#1}})$}} \begin{document} \title{Examples of Effect Displays with Partial Residuals\\ Using Contrived Regression Data} \author{John Fox and Sanford Weisberg} \date{2017-09-13} \maketitle <>= library(knitr) opts_chunk$set( tidy=FALSE,fig.width=5,fig.height=5,cache=FALSE ) @ <>= #options(continue="+ ", prompt="R> ", width=76) options(show.signif.stars=FALSE) options(scipen=3) @ The examples developed in this vignette are meant to supplement \citet{FoxWeisberg17}. \section{Basic Setup} We will analyze contrived data generated according to the following setup: \begin{itemize} \item We sample $n = 5000$ observations from a trivariate distribution for predictors $x_1$, $x_2$, and $x_3$, with uniform margins on the interval $[-2, 2]$, and with a prespecified bivariate correlation $\rho$ between each pair of predictors. The method employed, described by \citet{Schumann15} and traceable to results reported by \citet{Pearson07}, produces predictors that are nearly linearly related. Using 5000 observations allows us to focus on essentially asymptotic behavior of partial residuals in effect plots while still being able to discern individual points in the resulting graphs. \item We then generate the response $y$ according to the model \begin{equation} y = \beta_0 + h\left(\bbeta, \{x_1, x_2, x_3\}\right) + \varepsilon \end{equation} where $\varepsilon \Rtilde \N(0, 1.5^2)$. The regression function $h(\cdot)$ varies from example to example. \end{itemize} The following functions make it convenient to generate data according to this setup. These functions are more general than is strictly necessary so as to encourage further experimentation. <<>>= mvrunif <- function(n, R, min = 0, max = 1){ # method (but not code) from E. Schumann, # "Generating Correlated Uniform Variates" # URL: # # downloaded 2015-05-21 if (!is.matrix(R) || nrow(R) != ncol(R) || max(abs(R - t(R))) > sqrt(.Machine$double.eps)) stop("R must be a square symmetric matrix") if (any(eigen(R, only.values = TRUE)$values <= 0)) stop("R must be positive-definite") if (any(abs(R) - 1 > sqrt(.Machine$double.eps))) stop("R must be a correlation matrix") m <- nrow(R) R <- 2 * sin(pi * R / 6) X <- matrix(rnorm(n * m), n, m) X <- X %*% chol(R) X <- pnorm(X) min + X * (max - min) } gendata <- function(n = 5000, R, min = -2, max = 2, s = 1.5, model = expression(x1 + x2 + x3)){ data <- mvrunif(n = n, min = min, max = max, R = R) colnames(data) <- c("x1", "x2", "x3") data <- as.data.frame(data) data$error <- s * rnorm(n) data$y <- with(data, eval(model) + error) data } R <- function(offdiag = 0, m = 3){ R <- diag(1, m) R[lower.tri(R)] <- R[upper.tri(R)] <- offdiag R } @ \section{Unmodelled Interaction} We begin with uncorrelated predictors and the true regression mean function $\E(y|\x) = x_1 + x_2x_3$, but fit the incorrect additive working model $y \Rtilde x_1 + x_2 + x_3$ to the data. <<>>= set.seed(682626) Data.1 <- gendata(R = R(0), model = expression(x1 + x2 * x3)) round(cor(Data.1), 2) summary(mod.1 <- lm(y ~ x1 + x2 + x3, data = Data.1)) @ For reproducibility, we set a known seed for the pseudo-random number generator; this seed was itself generated pseudo-randomly, and we reuse it in the examples reported below. As well, in this first example, but not for those below, we show the correlation matrix of the randomly generated data along with the fit of the working model to the data. Effect plots with partial residuals corresponding to the terms in the working model are shown in Figure~\ref{fig-contrived-1a}: <>= library(effects) plot(predictorEffects(mod.1, partial.residuals=TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), rows=1, cols=3) @ In these graphs and, unless noted to the contrary, elsewhere in this vignette, the loess smooths are drawn with span 2/3. Because of the large number of points in the graphs, optional arguments to \code{plot} are specified to de-emphasize the partial residuals. To this end, the residuals are plotted as small points (\code{pch="."}) and in a translucent magenta color (\code{col="\#FF00FF80"}). \begin{figure}[tbp] \caption{Effect displays with partial residuals for the individual predictors $x_1$, $x_2$, and $x_3$ in the incorrect model $y \captilde x_1 + x_2 + x_3$ fit to data generated with the mean function $\E(y|\x) = x_1 + x_2x_3$, with uncorrelated predictors.\label{fig-contrived-1a}} \centering \includegraphics[width=1\textwidth]{figure/fig-contrived-1a-1.pdf} \end{figure} The failure of the model is not apparent in these traditional partial residual plots, but it is clear in the term effect plot for $\{x_2, x_3\}$, corresponding to the unmodelled interaction \inter{x_2}{x_3}, and shown in the top panel of Figure~\ref{fig-contrived-1b}, generated using <>= plot(Effect(c("x2", "x3"), mod.1, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ Moreover, the effect plot in the bottom panel of the figure for $\{x_1, x_2\}$, corresponding to a term \emph{not} in the true mean function, correctly indicates lack of interaction between these two predictors: <>= plot(Effect(c("x1", "x2"), mod.1, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ \begin{figure}[tbp] \caption{Term effect displays with partial residuals for $\{x_2, x_3 \}$, corresponding to the missing interaction \inter{x_2}{x_3}, and for $\{x_1, x_2 \}$, corresponding to an interaction not present in the model that generated the data.\label{fig-contrived-1b}} \centering \includegraphics[width=1\textwidth]{figure/fig-contrived-1b-1.pdf} \\ \includegraphics[width=1\textwidth]{figure/fig-contrived-1c-1.pdf} \end{figure} As a partly contrasting example, we turn to a similar data set, generated with the same regression mean function but with moderately correlated predictors, where the pairwise predictor correlations are $\rho = 0.5$: <<>>= set.seed(682626) Data.2 <- gendata(R = R(0.5), model = expression(x1 + x2 * x3)) mod.2 <- lm(y ~ x1 + x2 + x3, data = Data.2) @ Graphs analogous to those from the preceding example appear in Figures~\ref{fig-contrived-2a} and \ref{fig-contrived-2b}: <>= plot(predictorEffects(mod.2, partial.residuals=TRUE), partial.residual=list(pch=".", col="#FF00FF80",fig.show='hide'), axes=list(x=list(rotate=45)), rows=1, cols=3) @ <>= plot(Effect(c("x2", "x3"), mod.2, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ <>= plot(Effect(c("x1", "x2"), mod.2, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80",fig.show='hide'), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ The predictor effect plots for $x_2$ and $x_3$, and to a much lesser extent, for $x_1$, in the incorrect model in Figure~\ref{fig-contrived-2a} show apparent nonlinearity as a consequence of the unmodelled interaction and the correlations among the predictors. A similar phenomenon was noted in our analysis of the Canadian occupational prestige data in \citet[Section~4.2]{FoxWeisberg17}, where the unmodelled interaction between \code{type} and \code{income} induced nonlinearity in the partial relationship of \code{prestige} to \code{income}. The omitted interaction is clear in the effect plot for $\{x_2, x_3\}$, but also, to a lesser extent, contaminates the effect plot for $\{x_1,x_2\}$, which corresponds to an interaction that does not enter the model generating the data. These artifacts become more prominent if we increase the predictor correlations, say to $\rho = 0.9$ (as we invite the reader to do). \begin{figure}[tbp] \caption{Predictor effect displays with partial residuals for the individual predictors $x_1$, $x_2$, and $x_3$ in the incorrect model $y \captilde x_1 + x_2 + x_3$ fit to data generated with the mean function $\E(y|\x) = x_1 + x_2x_3$, with moderately correlated predictors.\label{fig-contrived-2a}} \centering \includegraphics[width=1\textwidth]{figure/fig-contrived-2a-1.pdf} \end{figure} \begin{figure}[tbp] \caption{Term effect displays with partial residuals for $\{x_2, x_3 \}$, corresponding to the missing interaction \inter{x_2}{x_3}, and for $\{x_1, x_2 \}$, corresponding to an interaction not present in the model that generated the data.\label{fig-contrived-2b}} \centering \includegraphics[width=1\textwidth]{figure/fig-contrived-2b-1.pdf}\\ \includegraphics[width=1\textwidth]{figure/fig-contrived-2c-1.pdf} \end{figure} \section{Unmodelled Nonlinearity} We generate data as before, but from the true model $\E(y|\x) = x_1^2 + x_2 + x_3$, where the predictors are moderately correlated, with pairwise correlations $\rho = 0.5$, but fit the incorrect additive working model $y \Rtilde x_1 + x_2 + x_3$ to the data: <<>>= set.seed(682626) Data.3 <- gendata(R = R(0.5), model = expression(x1^2 + x2 + x3)) mod.3 <- lm(y ~ x1 + x2 + x3, data = Data.3) @ Effect plots with residuals for the predictors in the working model appear in Figure~\ref{fig-contrived-3a}. The unmodelled nonlinearity in the partial relationship of $y$ to $x_1$ is clear, but there is some contamination of the plots for $x_2$ and $x_3$. The contamination is much more dramatic if the correlations among the predictors are increased to, say, $\rho = 0.9$ (as the reader may verify). <>= plot(predictorEffects(mod.3, partial.residuals=TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), rows=1, cols=3) @ \begin{figure}[tbp] \caption{Predictor effect displays with partial residuals for the individual predictors $x_1$, $x_2$, and $x_3$ in the incorrect model $y \captilde x_1 + x_2 + x_3$ fit to data generated with the mean function $\E(y|\x) = x_1^2 + x_2 + x_3$, with moderately correlated predictors.\label{fig-contrived-3a}} \centering \includegraphics[width=1\textwidth]{figure/fig-contrived-3a-1.pdf} \end{figure} Effect plots for $\{x_1, x_2 \}$ and $\{x_2, x_3 \}$ are shown in Figure~\ref{fig-contrived-3b}: <>= plot(Effect(c("x2", "x3"), mod.3, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ <>= plot(Effect(c("x1", "x2"), mod.3, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ Neither of these graphs corresponds to a term in the model generating the data nor in the working model, and the effect plots largely confirm the absence of \inter{x_1}{x_2} and \inter{x_2}{x_3} interactions, along with the nonlinearity of the partial effect of $x_1$, apparent in the top panel. \begin{figure}[tbp] \caption{Term effect displays with partial residuals for $\{x_1, x_2 \}$ and for $\{x_2, x_3 \}$, neither of which corresponds to an interaction in the model generating the data.\label{fig-contrived-3b}} \centering \includegraphics[width=1\textwidth]{figure/fig-contrived-3c-1.pdf} \\ \includegraphics[width=1\textwidth]{figure/fig-contrived-3b-1.pdf} \end{figure} \section{Simultaneous Unmodelled Nonlinearity and Interaction} This last example also appears in \citet[Section~4.3]{FoxWeisberg17}. We consider a true model that combines nonlinearity and interaction, $\E(y|\x) = x_1^2 + x_2 x_3$; the predictors are moderately correlated, with $\rho = 0.5$. We then fit the incorrect working model $y \Rtilde x_1 + x_2 + x_3$ to the data, producing the predictor effect displays with partial residuals in Figure~\ref{fig-contrived-4a}, for the predictors $x_1$, $x_2$, and $x_3$, which appear additively in the working model, and the term effect displays in Figure~\ref{fig-contrived-4b} for $\{x_2, x_3 \}$ and $\{x_1, x_2 \}$, corresponding respectively to the incorrectly excluded \inter{x_2}{x_3} term and the correctly excluded \inter{x_1}{x_2} interaction. <<>>= set.seed(682626) Data.4 <- gendata(R = R(0.5), model = expression(x1^2 + x2 * x3)) mod.4 <- lm(y ~ x1 + x2 + x3, data = Data.4) @ <>= plot(predictorEffects(mod.4, partial.residuals=TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), rows=1, cols=3) @ <>= plot(Effect(c("x2", "x3"), mod.4, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ <>= plot(Effect(c("x1", "x2"), mod.4, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ The nonlinearity in the partial relationship of $y$ to $x_1$ shows up clearly. The nonlinearity apparent in the plots for $x_2$ and $x_3$ is partly due to contamination with $x_1$, but largely to the unmodelled interaction between $x_2$ and $x_3$, coupled with the correlation between these predictors. The plot corresponding to the missing \inter{x_2}{x_3} term (in the top panel of Figure~\ref{fig-contrived-4b}) does a good job of detecting the unmodelled interaction, and curvature in this plot is slight. The plot for the \inter{x_1}{x_2} term (in the bottom panel of Figure~\ref{fig-contrived-4b}), a term neither in the true model nor in the working model, primarily reveals the unmodelled nonlinearity in the partial relationship of $y$ to $x_1$. \begin{figure}[tbp] \caption{Effect displays with partial residuals for the predictors $x_1$, $x_2$, and $x_3$ in the incorrect model $y \captilde x_1 + x_2 + x_3$ fit to data generated with the mean function $\E(y|\x) = x_1^2 + x_2x_3$, with moderately correlated predictors.\label{fig-contrived-4a}} \centering \includegraphics[width=1\textwidth]{figure/fig-contrived-4a-1.pdf} \end{figure} \begin{figure}[tbp] \caption{Term effect displays with partial residuals for $\{x_2, x_3 \}$ (top) and for $\{x_1, x_2 \}$ (bottom), the first of which corresponds to the missing \inter{x_2}{x_3} interaction in the model generating the data.\label{fig-contrived-4b}} \centering \includegraphics[width=1\textwidth]{figure/fig-contrived-4b-1.pdf} \\ \includegraphics[width=1\textwidth]{figure/fig-contrived-4c-1.pdf} \end{figure} If we fit the correct model, $y \Rtilde{} x_1^2 + x_2*x_3$, to the data, we obtain the plots shown in Figure~\ref{fig-contrived-5}. As theory suggests, the partial residuals in these effect displays validate the model, supporting the exclusion of the \inter{x_1}{x_2} interaction, the linear-by-linear interaction between $x_1$ and $x_2$, and the quadratic partial relationship of $y$ to $x_1$. <>= mod.5 <- lm(y ~ poly(x1, 2) + x2*x3, data=Data.4) plot(Effect("x1", mod.5, partial.residuals=TRUE), partial.residual=list(pch=".", col="#FF00FF80", span=0.2)) @ <>= plot(Effect(c("x2", "x3"), mod.5, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80"), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1)), span=0.5) @ <>= plot(Effect(c("x1", "x2"), mod.5, partial.residuals = TRUE), partial.residual=list(pch=".", col="#FF00FF80", span=0.35), axes=list(x=list(rotate=45)), lattice=list(layout=c(4, 1))) @ \noindent In these graphs, we adjust the span of the loess smoother to the approximately smallest value that produces a smooth fit to the partial residuals in each case. \begin{figure}[tbp] \caption{Effect displays with partial residuals for $x_1$ and $\{x_2, x_3 \}$, which correspond to terms in the model generating \emph{and} fitted to the data, $y \captilde x_1^2 + x_2 * x_3$, and for $\{x_1, x_2 \}$, which corresponds to an interaction that is not in the model.\label{fig-contrived-5}} \centering \includegraphics[width=0.45\textwidth]{figure/fig-contrived-5a-1.pdf} \\ \includegraphics[width=1\textwidth]{figure/fig-contrived-5b-1.pdf} \\ \includegraphics[width=1\textwidth]{figure/fig-contrived-5c-1.pdf} \end{figure} \bibliography{partial-residuals} \end{document} effects/MD50000644000176200001440000000317413157035277012220 0ustar liggesusers929b3cebf545c403a43487e17caad1e0 *DESCRIPTION 7a097e9b0a8432ecda6eae47293600c4 *NAMESPACE 3f9cc8a4e5ee93819d2c2cb6d9e23ca9 *NEWS 264ffdb8f9feee1a4c566e98555a8603 *R/Effect.R 4b1903a2166741e653b6c0e9744a5048 *R/Effect.mlm.R f63b9b2c061b414b9589001aea272ce9 *R/effects.R e3647d76ad2627320220010d5633cb33 *R/effectsclmm.R 21d80a8e199223fe375f241e54adf072 *R/effectsmer.R 1c32285c1760cbc4e22cb59eb78fb2e4 *R/effectspoLCA.R 7580484d7921dad22be55b76135c0ebe *R/plot-methods.R 45b3095e91b1c64bb6426f3efe533dc6 *R/plot.effpoly.R 613dede3b13e4105ba8edb466dd2eeb7 *R/predictorEffects.R fe921e301e606d944a33cb05445029c5 *R/summary-print-methods.R 872326a2059f22842a7f87a9f4d5dbf4 *R/utilities.R 349921327ffbb6ea0fe8cdfbbae889e1 *build/vignette.rds 809fa4f25bbbd3e6d146c26d7ae3907e *inst/CHANGES e6b77c2338408b6fdf1a617eaa014a2b *inst/CITATION 1a1e74cd6752a3a9d2ec07264f1c715d *inst/doc/partial-residuals.R 4f7486832fd72dd38382b5cb7bf96da9 *inst/doc/partial-residuals.Rnw b296c7a7711cfbc687576d4878e1d3d0 *inst/doc/partial-residuals.pdf 84ce6b8f10af320363351861b1d8ce79 *man/LegacyArguments.Rd 951598b619927a4c25267b37eb0a09b9 *man/effect.Rd ea56f30408977890a63e84544b4728fe *man/effects-package.Rd 0c2a04a7ba1f848e109de46b49f54ba7 *man/effectsTheme.Rd 76cdcb609f9d1dd81871516714bfb854 *man/plot.predictoreff.Rd 75d86bd0821bd4a3b2ef1f07576b1140 *man/predictorEffects.Rd 57e32e5de4016dad34d9990d5efa9dc4 *man/summary.effect.Rd c0e3bc933c20142624ff79e8f5ee6928 *tests/effect-tests-1.R 9dabcc36ad5771401aaf77f578024393 *tests/effect-tests-2.R 4f7486832fd72dd38382b5cb7bf96da9 *vignettes/partial-residuals.Rnw d20da429b1254ad53cbe3ddef488f7d9 *vignettes/partial-residuals.bib effects/build/0000755000176200001440000000000013157030525012771 5ustar liggesuserseffects/build/vignette.rds0000644000176200001440000000031713157030525015331 0ustar liggesusersb```b`fad`b2 1# '-H,*L-J-L)M) +GS$QSDؔ44EX:PX%榢[Z]?4-ީE0=(jؠjX2sRad9.nP&c0Gq?gQ~hݣ9JI,IK+K^effects/DESCRIPTION0000644000176200001440000000263213157035276013413 0ustar liggesusersPackage: effects Version: 4.0-0 Date: 2017-09-14 Title: Effect Displays for Linear, Generalized Linear, and Other Models Authors@R: c(person("John", "Fox", role = c("aut", "cre"), email = "jfox@mcmaster.ca"), person("Sanford", "Weisberg", role = "aut", email = "sandy@umn.edu"), person("Michael", "Friendly", role = "aut", email = "friendly@yorku.ca"), person("Jangman", "Hong", role = "aut"), person("Robert", "Andersen", role = "ctb"), person("David", "Firth", role = "ctb"), person("Steve", "Taylor", role = "ctb"), person("R Core Team", role="ctb")) Depends: R (>= 3.2.0), carData Suggests: pbkrtest (>= 0.4-4), nlme, MASS, poLCA, heplots, splines, ordinal, car, knitr Imports: lme4, nnet, lattice, grid, colorspace, graphics, grDevices, stats, survey, utils LazyLoad: yes LazyData: yes Description: Graphical and tabular effect displays, e.g., of interactions, for various statistical models with linear predictors. License: GPL (>= 2) URL: https://www.r-project.org, http://socserv.socsci.mcmaster.ca/jfox/ VignetteBuilder: knitr NeedsCompilation: no Packaged: 2017-09-15 20:00:21 UTC; jfox Author: John Fox [aut, cre], Sanford Weisberg [aut], Michael Friendly [aut], Jangman Hong [aut], Robert Andersen [ctb], David Firth [ctb], Steve Taylor [ctb], R Core Team [ctb] Maintainer: John Fox Repository: CRAN Date/Publication: 2017-09-15 20:40:30 UTC effects/man/0000755000176200001440000000000013156515040012444 5ustar liggesuserseffects/man/summary.effect.Rd0000644000176200001440000005657313155541511015704 0ustar liggesusers\name{summary.eff} \alias{print.eff} \alias{print.effpoly} \alias{print.efflatent} \alias{print.efflist} \alias{print.mlm.efflist} \alias{print.summary.eff} \alias{summary.eff} \alias{summary.effpoly} \alias{summary.efflatent} \alias{summary.efflist} \alias{summary.mlm.efflist} \alias{plot.eff} \alias{plot.effpoly} \alias{plot.efflist} \alias{plot.mlm.efflist} \alias{setStrip} \alias{restoreStrip} \alias{[.efflist} \title{Summarizing, Printing, and Plotting Effects} \description{ \code{summary}, \code{print}, \code{plot}, and \code{[} methods for \code{eff}, \code{effpoly}, \code{efflist}, and \code{mlm.efflist} objects. The plot arguments were substantially changed in mid-2017. } \usage{ \method{print}{eff}(x, type=c("response", "link"), ...) \method{print}{effpoly}(x, type=c("probability", "logits"), ...) \method{print}{efflatent}(x, ...) \method{print}{efflist}(x, ...) \method{print}{mlm.efflist}(x, ...) \method{print}{summary.eff}(x, ...) \method{summary}{eff}(object, type=c("response", "link"), ...) \method{summary}{effpoly}(object, type=c("probability", "logits"), ...) \method{summary}{efflatent}(object, ...) \method{summary}{efflist}(object, ...) \method{summary}{mlm.efflist}(object, ...) \method{plot}{eff}(x, x.var, z.var=which.min(levels), main=paste(effect, "effect plot"), symbols=TRUE, lines=TRUE, axes, confint, partial.residuals, id, lattice, ..., # legacy arguments: multiline, rug, xlab, ylab, colors, cex, lty, lwd, ylim, xlim, factor.names, ci.style, band.transparency, band.colors, type, ticks, alternating, rotx, roty, grid, layout, rescale.axis, transform.x, ticks.x, show.strip.values, key.args, use.splines, residuals.color, residuals.pch, residuals.cex, smooth.residuals, residuals.smooth.color, show.fitted, span) \method{plot}{effpoly}(x, x.var=which.max(levels), main=paste(effect, "effect plot"), symbols=TRUE, lines=TRUE, axes, confint, lattice, ..., # legacy arguments: type, multiline, rug, xlab, ylab, colors, cex, lty, lwd, factor.names, show.strip.values, ci.style, band.colors, band.transparency, style, transform.x, ticks.x, xlim, ticks, ylim, rotx, roty, alternating, grid, layout, key.args, use.splines) \method{plot}{efflist}(x, selection, rows, cols, ask=FALSE, graphics=TRUE, lattice, ...) \method{plot}{mlm.efflist}(x, ...) } \arguments{ \item{x}{an object of class \code{"eff"}, \code{"effpoly"}, \code{"efflist"}, \code{"mlm.efflist"}, or \code{"summary.eff"}, as appropriate.} \item{object}{an object of class \code{"eff"}, \code{"effpoly"}, \code{"efflist"}, or \code{"mlm.efflist"}, as appropriate.} \item{type}{for printing or summarizing linear and generalized linear models, if \code{"response"} (the default), effects are printed on the scale of the response variable; if \code{"link"}, effects are printed on the scale of the linear predictor. For polytomous logit models, this argument takes either \code{"probability"} or \code{"logit"}, with the former as the default. The \code{type} argument is also a legacy argument for \code{plot} methods.} \item{x.var}{the index (number) or quoted name of the covariate or factor to place on the horizontal axis of each panel of the effect plot. The default is the predictor with the largest number of levels or values.} \item{z.var}{for linear, generalized linear or mixed models, the index (number) or quoted name of the covariate or factor for which individual lines are to be drawn in each panel of the effect plot. The default is the predictor with the smallest number of levels or values. This argument is only used for multipline plots --- see the \code{lines} argument.} \item{main}{the title for the plot, printed at the top; the default title is constructed from the name of the effect.} \item{symbols}{\code{TRUE}, \code{FALSE}, or an optional list of specifications for plotting symbols; if not given, symbol properties are taken from \code{superpose.symbol} in the lattice theme. See Detailed Argument Descriptions under Details for more information.} \item{lines}{\code{TRUE}, \code{FALSE}, or an optional list of specifications for plotting lines (and possibly areas); if not given, line properties are taken from \code{superpose.line} in the lattice theme. See Detailed Argument Descriptions under Details for more information.} \item{axes}{an optional list of specifications for the x and y axes; if not given, axis properties take generally reasonable default values. See Details for more information.} \item{confint}{an optional list of specifications for plotting confidence regions and intervals; if not given, generally reasonable default values are used. See Detailed Argument Descriptions under Details for more information.} \item{partial.residuals}{an optional list of specifications for plotting partial residuals for linear and generalized linear models; if not given, generally reasonable default values are used. See Detailed Argument Descriptions under Details for more information.} \item{id}{an optional list of specifications for identifying points when partial residuals are plotted; if not specified, no points are labelled. See Detailed Argument Descriptions under Details for more information.} \item{lattice}{an optional list of specifications for various lattice properties, such as legend placement; if not given, generally reasonable default values are used. See Detailed Argument Descriptions under Details for more information.} \item{selection}{the optional index (number) or quoted name of the effect in an effect list to be plotted; if not supplied, a menu of high-order terms is presented or all effects are plotted.} \item{rows, cols}{Number of rows and columns in the ``meta-array'' of plots produced for an \code{efflist} object; if either argument is missing, then the meta-layout will be computed by the \code{plot} method.} \item{ask}{if \code{selection} is not supplied and \code{ask} is \code{TRUE}, a menu of high-order terms is presented; if \code{ask} is \code{FALSE} (the default), effects for all high-order terms are plotted in an array.} \item{graphics}{if \code{TRUE} (the default), then the menu of terms to plot is presented in a dialog box rather than as a text menu.} \item{...}{arguments to be passed down.} \item{multiline, rug, xlab, ylab, colors, cex, lty, lwd, ylim, xlim, factor.names, ci.style, band.transparency, band.colors, ticks, alternating, rotx, roty, grid, layout, rescale.axis, transform.x, ticks.x, show.strip.values, key.args, use.splines, residuals.color, residuals.pch, residuals.cex, smooth.residuals, residuals.smooth.color, show.fitted, span, style}{legacy arguments retained for backwards compatibility; if specified, these will take precedence over the newer list-style arguments described above. See \code{\link{LegacyArguments}} for details.} } \details{ In a generalized linear model, by default, the \code{print} and \code{summary} methods for \code{eff} objects print the computed effects on the scale of the response variable using the inverse of the link function. In a logit model, for example, this means that the effects are expressed on the probability scale. By default, effects in a GLM are plotted on the scale of the linear predictor, but the vertical axis is labelled on the response scale. This preserves the linear structure of the model while permitting interpretation on what is usually a more familiar scale. This approach may also be used with linear models, for example to display effects on the scale of the response even if the data are analyzed on a transformed scale, such as log or square-root. When a factor is on the x-axis, the \code{plot} method for \code{eff} objects connects the points representing the effect by line segments, creating a response ``profile.'' If you wish to suppress these lines, add \code{lty=0} to the \code{lines} argument to the call to \code{plot} (see below and the examples). In a polytomous (multinomial or proportional-odds) logit model, by default effects are plotted on the probability scale; they may alternatively be plotted on the scale of the individual-level logits. \bold{Detailed Argument Descriptions} Maximizing the flexibility of these plot commands requires inclusion of a myriad of options. In an attempt to simplify the use of these options, they have been organized into just a few arguments that each accept a list of specifications as an argument. In a few cases the named entries in the list are themselves lists. Each of the following arguments takes an optional list of specifications; any specification absent from the list assumes its default value. Some of the list elements are themselves lists, so in complex cases, the argument can take the form of nested lists. All of these arguments can also be used on objects created with \code{\link{predictorEffects}}. \describe{ \item{\code{symbols}}{\code{TRUE}, \code{FALSE}, or a list of options that controls the plotting symbols and their sizes for use with factors; if \code{FALSE} symbols are suppressed; if \code{TRUE} default values are used: \describe{ \item{\code{pch}}{ploting symbols, a vector of plotting characters, with the default taken from \code{trellis.par.get("superpose.symbol")$pch}, typically a vector of 1s (circles).} \item{\code{cex}}{plotting character sizes, a vector of values, with the default taken from \code{trellis.par.get("superpose.symbol")$cex}, typically a vector of 0.8s.} } } \item{\code{lines}}{\code{TRUE}, \code{FALSE}, or a list that controls the chacteristics of lines drawn on a plot, and also whether or not multiple lines should be drawn in the same panel in the plot; if \code{FALSE} lines are suppressed; if \code{TRUE} default values are used: \describe{ \item{\code{multiline}}{display a multiline plot in each panel; the default is \code{TRUE} if there are no standard errors in the \code{"eff"} object, \code{FALSE} otherwise. For an \code{"effpoly"} object \code{multline=TRUE} causes all of the response levels to be shown in the same panel rather than in separate panels.} \item{\code{lty}}{vector of line types, with the default taken from \code{trellis.par.get("superpose.line")$lty}, typically a vector of 1s (solid lines).} \item{\code{lwd}}{vector of line widths, with the default taken from \code{trellis.par.get("superpose.line")$lwd}, typically a vector with 2 in the first position followed by 1s.} \item{\code{col}}{a vector of line colors, with the default taken from from \code{trellis.par.get("superpose.line")$col}, used both for lines and for areas in stacked area plots for \code{"effpoly"} objects; in the latter case, the default colors for an ordered response are instead generated by \code{\link[colorspace]{sequential_hcl}} in the \pkg{colorspace} package.} \item{\code{splines}}{use splines to smooth plotted effect lines; the default is \code{TRUE}.} } } \item{\code{axes}}{a list with elements \code{x}, \code{y}, \code{alternating}, and \code{grid} that control axis limits, ticks, and labels. The \code{x} and \code{y} elements may themselves be lists. The \code{x} entry is a list with elements named for predictors, with each predictor element itself a list with the following elements: \describe{ \item{\code{lab}}{axis label, defaults to the name of the predictor.} \item{\code{lim}}{a two-element vector giving the axis limits, with the default determined from the data.} \item{\code{ticks}}{a list with either element \code{at}, a vector specifying locations for the ticks marks, or \code{n}, the number of tick marks.} \item{\code{transform}}{transformations to be applied to the horizontal axis of a numeric predictor, in the form of a list of two functions, with element names \code{trans} and \code{inverse}. The \code{trans} function is applied to the values of the predictor, and \code{inverse} is used for computing proper axis tick labels. The default is not to transform the predictor axis.} } Two additional elements may appear in the \code{x} list, and apply to all predictors: \describe{ \item{\code{rotate}}{angle in degrees to rotate tick labels; the default is 0.} \item{\code{rug}}{display a rug plot showing the marginal distribution of a numeric predictor; the default is \code{TRUE}.} } The \code{y} list contains \code{lab}, \code{lim}, \code{ticks}, and \code{rotate} elements (similar to those specified for individual predictors in the \code{x} list), along with the additional \code{type} element: \describe{ \item{\code{type}}{for plotting linear or generalized linear models, \code{"rescale"} (the default) plots the vertical axis on the link scale (e.g., the logit scale for a logit model) but labels the axis on the response scale (e.g., the probability scale for a logit model); \code{"response"} plots and labels the vertical axis on the scale of the response (e.g., the probability scale for a logit model); and \code{"link"} plots and labels the vertical axis on the scale of the link (e.g., the logit scale for a logit model). For polytomous logit models, this element is either \code{"probability"} or \code{"logit"}, with the former as the default.} } Other elements: \describe{ \item{\code{alternating}}{if \code{TRUE} (the default), the tick labels alternate by panels in multi-panel displays from left to right and top to bottom; if \code{FALSE}, tick labels appear at the bottom and on the left.} \item{\code{grid}}{if \code{TRUE} (the default is \code{FALSE}), add grid lines to the plot.} } } \item{\code{confint}}{specifications to add/remove confidence intervals or regions from a plot, and to set the nominal confidence level. \describe{ \item{\code{style}}{one of \code{"auto"}, \code{"bars"}, \code{"lines"}, \code{"bands"}, and \code{"none"}; the default is \code{"bars"} for factors, \code{"bands"} for numeric predictors, and \code{"none"} for multiline plots; \code{"auto"} also produces \code{"bars"} for factors and \code{"bands"} for numeric predictors, even in multiline plots.} \item{\code{alpha}}{transparency of confidence bands; the default is 0.15.} \item{\code{col}}{colors; the default is taken from the line colors.} } } \item{\code{partial.residuals}}{specifications concerning the addition of partial residuals to the plot. \describe{ \item{\code{plot}}{display the partial residuals; the default is \code{TRUE} if residuals are present in the \code{"eff"} object, \code{FALSE} otherwise.} \item{\code{fitted}}{show fitted values as well as residuals; the default is \code{FALSE}.} \item{\code{col}}{color for partial residuals; the default is the second line color.} \item{\code{pch}}{plotting symbols for partial residuals; the default is 1, a circle.} \item{\code{cex}}{size of symbols for partial residuals; the default is 1.} \item{\code{smooth}}{draw a loess smooth of the partial residuals; the default is \code{TRUE}.} \item{\code{span}}{span for the loess smooth; the default is 2/3.} \item{\code{smooth.col}}{color for the loess smooth; the default is the second line color.} \item{\code{lty}}{line type for the loess smooth; the default is the first line type, normally 1 (a solid line).} \item{\code{lwd}}{line width for the loess smooth; the default is the first line width, normally 2.} } } \item{\code{id}}{specifications for optional point identification when partial residuals are plotted. \describe{ \item{\code{n}}{number of points to identify; default is \code{2} if \code{id=TRUE} and \code{0} if \code{id=FALSE}. Points are selected based on the Mahalanobis distances of the pairs of x-values and partial residuals from their centroid.} \item{\code{col}}{color for the point labels; default is the same as the color of the partial residuals.} \item{\code{cex}}{relative size of text for point labels; default is \code{0.75}.} \item{\code{labels}}{vector of point labels; the default is the names of the residual vector, which is typically the row names of the data frame to which the model is fit.} } } \item{\code{lattice}}{the plots are drawn with the \pkg{\link{lattice}} package, generally by the \code{\link{xyplot}} function. These specifications are passed as arguments to the functions that actually draw the plots. \describe{ \item{\code{layout}}{the \code{layout} argument to the \pkg{lattice} function \code{\link{xyplot}} (or, in some cases \code{\link{densityplot}}), which is used to draw the effect display; if not specified, the plot will be formatted so that it appears on a single page.} \item{\code{key.args}}{additional arguments that control the appearance or location of the key, or legend, on the plot. There are many options, and they are documented by searching for "\code{key:}" in \code{\link{xyplot}}. For example, \code{lattice=list(key.args=list(cex= .75, cex.title=.75, between.columns=0))} reduces the size of the labels and title to .75 of nominal, and reduces the extra space between columns to 0, and \code{lattice=list(key.args = list(x = 0.75, y = 0.75, corner = c(0, 0)))} moves the key onto the plot, with the origin \code{corner=c(0,0)} at the point \code{(.75, .75)}, thinking of the total plotting area as a unit square. To conserve space, by default we set \code{between.columns=0}.} \item{\code{strip}}{a list with two elements: \code{factor.names}, which if \code{TRUE}, the default, shows conditioning variable names in the panel headers; and \code{values}, which if \code{TRUE}, the default unless partial residuals are plotted, displays conditioning variable values in the panel headers.} \item{\code{array}}{a list with elements \code{row}, \code{col}, \code{nrow}, \code{ncol}, and \code{more}, used to graph an effect as part of an array of plots; \code{row}, \code{col}, \code{nrow}, and \code{ncol} are used to compose the \code{split} argument and \code{more} the \code{more} argument to \code{\link{print.trellis}}. The \code{array} argument is automatically by \code{plot.efflist} and will be ignored if used with that function.} } } } } \value{ The \code{summary} method for \code{"eff"} objects returns a \code{"summary.eff"} object with the following components (those pertaining to confidence limits need not be present): \item{header}{a character string to label the effect.} \item{effect}{an array containing the estimated effect.} \item{lower.header}{a character string to label the lower confidence limits.} \item{lower}{an array containing the lower confidence limits.} \item{upper.header}{a character string to label the upper confidence limits.} \item{upper}{an array containing the upper confidence limits.} The \code{plot} method for \code{"eff"} objects returns a \code{"plot.eff"} object (an enhanced \code{"trellis"} object); the provided \code{\link{print}} method plots the object. The \code{[} method for \code{"efflist"} objects is used to subset an \code{"efflist"} object and returns an object of the same class. } \author{John Fox \email{jfox@mcmaster.ca} and Jangman Hong.} \seealso{\code{\link{LegacyArguments}}, \code{\link{effect}}, \code{\link{allEffects}}, \code{\link{effectsTheme}}, \code{\link{xyplot}}, \code{\link{densityplot}}, \code{\link{print.trellis}}, \code{\link{loess}}, \code{\link[colorspace]{sequential_hcl}} } \examples{ # also see examples in ?effect mod.cowles <- glm(volunteer ~ sex + neuroticism*extraversion, data=Cowles, family=binomial) eff.cowles <- allEffects(mod.cowles, xlevels=list(extraversion=seq(0, 24, 6))) eff.cowles as.data.frame(eff.cowles[[2]]) # neuroticism*extraversion interaction plot(eff.cowles, 'sex', axes=list(y=list(lab="Prob(Volunteer)"), x=list(rotate=90)), lines=list(lty=0), grid=TRUE) plot(eff.cowles, 'neuroticism:extraversion', axes=list(y=list(lab="Prob(Volunteer)", ticks=list(at=c(.1,.25,.5,.75,.9))))) plot(Effect(c("neuroticism", "extraversion"), mod.cowles, confint=list(type="Scheffe"), xlevels=list(extraversion=seq(0, 24, 6))), axes=list(y=list(lab="Prob(Volunteer)", ticks=list(at=c(.1,.25,.5,.75,.9))))) \donttest{ # change color of the confidence bands to 'black' with .15 transparency plot(eff.cowles, 'neuroticism:extraversion', axes=list(y=list(lab="Prob(Volunteer)", ticks=list(at=c(.1,.25,.5,.75,.9)))), confint=list(col="red", alpha=.3)) plot(eff.cowles, 'neuroticism:extraversion', lines=list(multiline=TRUE), axes=list(y=list(lab="Prob(Volunteer)")), lattice=list(key.args = list(x = 0.75, y = 0.75, corner = c(0, 0)))) # use probability scale in place of logit scale, all lines are black. plot(eff.cowles, 'neuroticism:extraversion', lines=list(multiline=TRUE, lty=1:8, col="black"), axes=list(y=list(type="response", lab="Prob(Volunteer)")), lattice=list(key.args = list(x = 0.75, y = 0.75, corner = c(0, 0))), confint=list(style="bands")) plot(effect('sex:neuroticism:extraversion', mod.cowles, xlevels=list(extraversion=seq(0, 24, 6))), lines=list(multiline=TRUE)) plot(effect('sex:neuroticism:extraversion', mod.cowles, xlevels=list(extraversion=seq(0, 24, 6))), lines=list(multiline=TRUE), axes=list(y=list(type="response")), confint=list(style="bands"), lattice=list(key.args = list(x=0.75, y=0.75, corner=c(0, 0)))) } if (require(nnet)){ mod.beps <- multinom(vote ~ age + gender + economic.cond.national + economic.cond.household + Blair + Hague + Kennedy + Europe*political.knowledge, data=BEPS) \donttest{ plot(effect("Europe*political.knowledge", mod.beps, xlevels=list(political.knowledge=0:3))) } plot(effect("Europe*political.knowledge", mod.beps, xlevels=list(political.knowledge=0:3), fixed.predictors=list(given.values=c(gendermale=0.5))), axes=list(y=list(style="stacked"), x=list(rug=FALSE), grid=TRUE), lines=list(col=c("blue", "red", "orange"))) } if (require(MASS)){ mod.wvs <- polr(poverty ~ gender + religion + degree + country*poly(age,3), data=WVS) plot(effect("country*poly(age, 3)", mod.wvs)) \donttest{ plot(effect("country*poly(age, 3)", mod.wvs), lines=list(multiline=TRUE)) plot(effect("country*poly(age, 3)", mod.wvs), axes=list(y=list(style="stacked")), lines=list(col=c("gray75", "gray50", "gray25"))) plot(effect("country*poly(age, 3)", latent=TRUE, mod.wvs)) } } mod.pres <- lm(prestige ~ log(income, 10) + poly(education, 3) + poly(women, 2), data=Prestige) eff.pres <- allEffects(mod.pres) \donttest{ plot(eff.pres) plot(eff.pres[1:2]) } plot(eff.pres[1], axes=list(x=list(income=list(transform=list( trans=log10, inverse=function(x) 10^x), ticks=list(at=c(1000, 2000, 5000, 10000, 20000)))))) } \keyword{hplot} \keyword{models} effects/man/predictorEffects.Rd0000644000176200001440000001055113155030716016231 0ustar liggesusers\name{predictorEffects} \alias{predictorEffect} \alias{predictorEffect.svyglm} \alias{predictorEffect.default} \alias{predictorEffects} \title{ Functions For Computing Predictor Effects } \description{ Alternatives to the \code{Effect} and \code{allEffects} functions that use a different paradigm for conditioning in an effects display. The user specifies one predictor, either continuous or a factor, for the horizontal axis of a plot, and the function determines the appropriate plot to display (which is drawn by \code{plot}). } \usage{ predictorEffect(predictor, mod, xlevels, ...) \method{predictorEffect}{svyglm}(predictor, mod, xlevels, ...) \method{predictorEffect}{default}(predictor, mod, xlevels=list(), ...) predictorEffects(mod, predictors = ~., ...) } \arguments{ \item{mod}{ A model object. Supported models include all those described on the help page for \code{\link{Effect}}. } \item{predictor}{quoted name of the focal predictor} \item{xlevels}{this argument is used to set the values for any predictor in the effect that is not a factor. For a predictor effect, the default is to use 50 quantiles of the focal predictor on the x-axis between the 0.01 and 0.98 quantiles. See \code{\link{Effect}} for details and for how other defaults are set.} \item{predictors}{ If the default \code{~ .}, a predictor effecrts plot is drawn for each predictor (not regressor) in a model. Otherwise, this should be a one-sided formula listing the first-order predictors for which predictor effects plots should be drawn. } \item{\dots}{ Additional arguments passed to \code{\link{Effect}}. } } \details{ Effects plots view a fitted regression function E(Y|X) in (sequences of) two-dimensional plots using conditioning and slicing. The functions describe here use a different method of determining the conditioning and slicing than \code{Effects} uses. The predictor effects a focal predictor say \code{x1} will be the the usual effect for the generalized interaction of \code{x1} with all the other predictors in a model. When a predictor effects object is plotted, the focal predictor is by default plotted on the horizontal axis. For example, in the model \code{mod} with formula \code{y ~ x1 + x2 + x3}, then \code{p1 <- predictorEffects(mod, ~ x1)} is essentially equilavent to \code{p2 <- Effect("x1", mod)}. When plotted, these objects may be different because \code{plot(p1)} will always put \code{x1} on the horizontal axis while \code{plot(p2)} uses a rule to determine the horizontal axis based on the characteristics of all the predictors, preferring continuous predictors over factors. If \code{mod} has the formula \code{y ~ x1 + x2 + x3 + x1:x2}, then \code{p1 <- predictorEffects(mod, ~ x1)} is essentially equilavent to \code{p2 <- Effect(c("x1", "x2"), mod)}. As in the last example, the plotted versions of these objects may differ because of rules used to determine the horizontal axis. If \code{mod} has the formula \code{y ~ x1 + x2 + x3 + x1:x2 + x1:x3}, then \code{p1 <- predictorEffects(mod, ~ x1)} is essentially equilavent to \code{p2 <- Effect(c("x1", "x2", "x3"), mod)}. The plotted versions of these objects may differ because of rules used to determine the horizontal axis. } \value{ \code{predictorEffect} returns an object of class \code{c(predictoreff, eff)}. The components of the object are described under the detalis at \code{\link{Effect}}. \code{predictorEffects} returns an object of class \code{predictorefflist}, which is a list whose elements are of class \code{c(predictoreff, eff)} } \references{ See \code{\link{Effect}}. } \author{ S. Weisberg, \email{sandy@umn.edu} } \seealso{ \code{\link{Effect}}, \code{\link{plot.predictoreff}} } \examples{ mod <- lm(prestige ~ type*(education + income + women), Prestige) plot(predictorEffect("income", mod)) plot(predictorEffects(mod, ~ education + income + women)) # svyglm() example (adapting an example from the survey package) \donttest{ if (require(survey)){ data(api) dstrat<-svydesign(id=~1, strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) mod <- svyglm(sch.wide ~ ell + meals + mobility, design=dstrat, family=quasibinomial()) plot(predictorEffects(mod), axes=list(y=list(lim=log(c(0.4, 0.99)/c(0.6, 0.01)), ticks=list(at=c(0.4, 0.75, 0.9, 0.95, 0.99))))) } } } \keyword{hplot} \keyword{models} effects/man/effectsTheme.Rd0000644000176200001440000000332013151255450015334 0ustar liggesusers\name{effectsTheme} \alias{effectsTheme} \title{Set the lattice Theme for Effect Plots} \description{ Set the \pkg{lattice} theme (see \code{\link[lattice]{trellis.device}}) appropriately for effect plots. This function is invoked automatically when the \pkg{effects} package is loaded \emph{if} the \pkg{lattice} package hasn't previously been loaded. A typical call is \code{lattice::trellis.par.set(effectsTheme())}. } \usage{ effectsTheme(strip.background = list(col = gray(seq(0.95, 0.5, length = 3))), strip.shingle = list(col = "black"), clip = list(strip = "off"), superpose.line = list(lwd = c(2, rep(1, 6)))) } \arguments{ \item{strip.background}{colors for the background of conditioning strips at the top of each panel; the default uses shades of gray and makes allowance for up to three conditioning variables.} \item{strip.shingle}{when lines rather than numeric values are used to indicate the values of conditioning variables, the default sets the color of the lines to black.} \item{clip}{the default allows lines showing values of conditioning variables to extend slightly beyond the boundaries of the strips---making the lines more visible at the extremes.} \item{superpose.line}{the default sets the line width of the first (of seven) lines to 2.} } \value{ a list suitable as an argument for \code{\link[lattice]{trellis.par.set}}; current values of modified parameters are supplied as an attribute. } \author{John Fox \email{jfox@mcmaster.ca}} \seealso{\code{\link[lattice]{trellis.device}}, \code{\link[lattice]{trellis.par.set}}} \examples{ \dontrun{ lattice::trellis.par.set(effectsTheme()) } } \keyword{utilities} \keyword{device} effects/man/LegacyArguments.Rd0000644000176200001440000001142613155030716016032 0ustar liggesusers\name{LegacyArguments} \alias{LegacyArguments} \alias{Legacy Arguments} \title{Legacy Arguments for \code{plot} and \code{Effect} Methods} \description{ Prior to verson 4.0-0 of the \pkg{effects} package, there were many (literally dozens) of arguments to the \code{plot} methods for \code{"eff"} and \code{"effpoly"} objects. In version 4.0-0 of the package, we have consolidated these arguments into a much smaller number of arguments (e.g., \code{lines}, \code{points}, \code{axes}) that take lists of specifications. We have similarly consolidated some of the arguments to \code{Effect} methods into the \code{confint} and \code{fixed.predictors} arguments. For backwards compatibility, we have to the extent possible retained the older arguments. If specified, these legacy arguments take precedence over the newer list-style arguments } \details{ Here is the correspondence between the old and new arguments. For \code{plots} methods: \describe{ \item{\code{multiline=TRUE/FALSE}}{\code{lines=list(multiline=TRUE/FALSE)}} \item{\code{colors={vector of colors}}}{\code{lines=list(col={vector of colors})}} \item{\code{lty={vector of line types}}}{\code{lines=list(lty={vector of line types})}} \item{\code{lwd={vector of line widths}}}{\code{lines=list(lwd={vector of line widths})}} \item{\code{use.splines=TRUE/FALSE}}{\code{lines=list(splines=TRUE/FALSE)}} \item{\code{cex={number}}}{\code{points=list(cex={number})}} \item{\code{rug=TRUE/FALSE}}{\code{axes=list(x=list(rug=TRUE/FALSE)}} \item{\code{xlab={"axis title"}}}{\code{axes=list(x=list(lab={"axis title"}))}} \item{\code{xlim={c(min, max)}}}{\code{axes=list(x=list(lim={c(min, max)}))}} \item{\code{rotx={degrees}}}{\code{axes=list(x=list(rot={degrees}))}} \item{\code{ticks.x=list({tick specifications})}}{\code{axes=list(x=list(ticks=list({tick specifications})))}} \item{\code{transform.x=list(link={function}, inverse={function})}}{\code{axes=list(x=list(transform=list({lists of transformations by predictors})))}} \item{\code{ylab={"axis title"}}}{\code{axes=list(y=list(lab={"axis title"}))}} \item{\code{ylim={c(min, max)}}}{\code{axes=list(y=list(lim={c(min, max)}))}} \item{\code{roty={degrees}}}{\code{axes=list(y=list(rot={degrees}))}} \item{\code{ticks=list({tick specifications})}}{\code{axes=list(y=list(ticks=list({tick specifications})))}} \item{\code{alternating=TRUE/FALSE}}{\code{axes=list(alternating=TRUE/FALSE)}} \item{\code{grid=TRUE/FALSE}}{\code{axes=list(grid=TRUE/FALSE)}} \item{\code{ci.style="bands"/"lines"/"bars"/"none"}}{\code{confint=list(style="bands"/"lines"/"bars"/"none"})} \item{\code{se=TRUE/FALSE}}{for \code{Effect}, \code{confint=list(compute=TRUE/FALSE)}} \item{\code{confidence.level={number}}}{for \code{Effect}, \code{confint=list(level={number})}} \item{\code{band.transparency={number}}}{\code{confint=list(alpha={number})}} \item{\code{band.colors={vector of colors}}}{\code{confint=list(col={vector of colors})}} \item{\code{residuals.color={color}}}{\code{partial.residuals=list(col={color})}} \item{\code{residuals.pch={plotting character}}}{\code{partial.residuals=list(pch={plotting character})}} \item{\code{residuals.cex={number}}}{\code{partial.residuals=list(cex={number})}} \item{\code{smooth.residuals=TRUE/FALSE}}{\code{partial.residuals=list(smooth=TRUE/FALSE)}} \item{\code{residuals.smooth.color={color}}}{\code{partial.residuals=list(smooth.col={color})}} \item{\code{span={number}}}{\code{partial.residuals=list(span={number})}} \item{\code{show.fitted=TRUE/FALSE}}{\code{partial.residuals=list(fitted=TRUE/FALSE)}} \item{\code{factor.names=TRUE/FALSE}}{\code{lattice=list(strip=list(factor.names=TRUE/FALSE))}} \item{\code{show.strip.values=TRUE/FALSE}}{\code{lattice=list(strip=list(values=TRUE/FALSE))}} \item{\code{layout={lattice layout}}}{\code{lattice=list(layout={lattice layout})}} \item{\code{key.args={lattice key args}}}{\code{lattice=list(key.args={lattice key args})}} \item{\code{style="lines"/"stacked"}}{for \code{plot.effpoly}, \code{axes=list(y=list(style="lines"/"stacked"))}} \item{\code{rescale.axis=TRUE/FALSE}}{\code{type="rescale"/"response"/"link"}} } For \code{Effect} methods: \describe{ \item{\code{se=TRUE/FALSE}}{\code{confint=list(compute=TRUE/FALSE)}} \item{\code{confidence.level={number}}}{\code{confint=list(level={number})}} \item{\code{given.values={named vector}}}{\code{fixed.predictors=list(given.values={named vector})}} \item{\code{typical={function}}}{\code{fixed.predictors=list(typical={function})}} \item{\code{offset={function}}}{\code{fixed.predictors=list(offset={function})}} } } \author{John Fox \email{jfox@mcmaster.ca}} \seealso{ \code{\link{Effect}}, \code{\link{plot.eff}}, \code{\link{plot.effpoly}} } \keyword{hplot} effects/man/effects-package.Rd0000644000176200001440000000611213156515040015743 0ustar liggesusers\name{effects-package} \Rdversion{1.1} \alias{effects-package} \alias{effects} \docType{package} \title{ Effect Displays for Linear, Generalized Linear, and Other Models } \description{ Graphical and tabular effect displays, e.g., of interactions, for various statistical models with linear predictors. } \details{ \tabular{ll}{ Package: \tab effects\cr Version: \tab 4.0-0\cr Date: \tab 2017-09-14\cr Depends: \tab R (>= 3.2.0), carData\cr Suggests: \tab pbkrtest (>= 0.4-4), nlme, MASS, poLCA, heplots, splines, ordinal, car\cr Imports: \tab lme4, nnet, lattice, grid, colorspace, graphics, grDevices, stats, utils\cr LazyLoad: \tab yes\cr LazyData: \tab yes\cr License: \tab GPL (>= 2)\cr URL: \tab https://www.r-project.org, http://socserv.socsci.mcmaster.ca/jfox/\cr } This package creates effect displays for various kinds of models, as partly explained in the references. Typical usage is \code{plot(allEffects(model))} or \code{plot(predictorEffects(model))}, where \code{model} is an appropriate fitted-model object. Additional arguments to \code{allEffects}, \code{predictorEffects} and \code{plot} can be used to customize the resulting displays. The function \code{effect} can be employed to produce an effect display for a particular term in the model, or to which terms in the model are marginal. The function \code{predictorEffect} can be used to construct an effect display for a particularly predictor. The function \code{Effect} may similarly be used to produce an effect display for any combination of predictors. In any of the cases, use \code{plot} to graph the resulting effect object. For linear and generalized linear models it is also possible to plot partial residuals to obtain (multidimensional) component+residual plots. See \code{?effect}, \code{?Effect}, \code{?predictorEffect}, and \code{?plot.eff} for details. } \author{ John Fox , Sanford Weisberg, Michael Friendly, and Jangman Hong. We are grateful to Robert Andersen and David Firth for various suggestions. Maintainer: John Fox } \references{ Fox, J. (1987) Effect displays for generalized linear models. \emph{Sociological Methodology} \bold{17}, 347--361. Fox, J. (2003) Effect displays in R for generalised linear models. \emph{Journal of Statistical Software} \bold{8:15}, 1--27, <\url{http://www.jstatsoft.org/v08/i15/}>. Fox, J. and R. Andersen (2006) Effect displays for multinomial and proportional-odds logit models. \emph{Sociological Methodology} \bold{36}, 225--255. Fox, J. and J. Hong (2009). Effect displays in R for multinomial and proportional-odds logit models: Extensions to the effects package. \emph{Journal of Statistical Software} \bold{32:1}, 1--24, <\url{http://www.jstatsoft.org/v32/i01/}>. Fox, J. and S. Weisberg (forthcoming). Visualizing Fit and Lack of Fit in Comples Regression Models: Effect Plots with Partial Residuals. \emph{Journal of Statistical Software}. } \keyword{ package } effects/man/plot.predictoreff.Rd0000644000176200001440000000507513152015717016375 0ustar liggesusers\name{plot.predictoreff} \alias{plot.predictoreff} \alias{plot.predictorefflist} \title{ Draw Predictor Effect Plots } \description{ These functions call \code{\link{plot.eff}} and \code{\link{plot.efflist}} to draw predictor effect plots. } \usage{ \method{plot}{predictoreff}(x, x.var, main = paste(names(x$variables)[1], "predictor effect plot"), ...) \method{plot}{predictorefflist}(x, selection, rows, cols, ask = FALSE, graphics = TRUE, lattice, ...) } \arguments{ \item{x}{ An object of class \code{predictoreff} or \code{predictorefflist}. } \item{x.var}{ the index (number) or quoted name of the covariate or factor to place on the horizontal axis of each panel of the effect plot. The default is the predictor with the largest number of levels or values. } \item{main}{ the title for the plot, printed at the top; the default title is constructed from the name of the effect. } \item{\dots}{ arguments to be passed to \code{\link{plot.eff}} or \code{\link{plot.efflist}}. } \item{selection}{ the optional index (number) or quoted name of the effect in an effect list to be plotted; if not supplied, a menu of high-order terms is presented or all effects are plotted. } \item{rows, cols}{Number of rows and columns in the "meta-array"" of plots produced for an efflist object; if either argument is missing, then the meta-layout will be computed by the plot method. } \item{ask}{if selection is not supplied and ask is \code{TRUE}, a menu of high-order terms is presented; if ask is \code{FALSE} (the default), effects for all high-order terms are plotted in an array. } \item{graphics}{ if \code{TRUE} (the default), then the menu of terms to plot is presented in a dialog box rather than as a text menu. } \item{lattice}{ argument passed to \code{\link{plot.efflist}}. } } \details{ The \code{plot.predictoreff} calls the method \code{plot.eff} and \code{plot.predictorefflist} calls \code{plot.efflist}. Both of these functions are documented at \code{\link{plot.eff}}. Warning: By default, the functions documented here use the argument \code{lines=list(multiline=TRUE)} while direct calls to the underlying functions use \code{lines=list(multiline = FALSE)} if standard errors were computed by the call to create the object \code{x}. } \value{ See the documentation for \code{\link{plot.eff}}. } \author{ S. Weisberg, \email{sandy@umn.edu} } \seealso{ \code{\link{predictorEffect}}, \code{\link{plot.eff}}. } \examples{ mod <- lm(prestige ~ type*(education + income + women), Prestige) plot(predictorEffects(mod, ~ income)) } \keyword{hplot} \keyword{models} effects/man/effect.Rd0000644000176200001440000010313613155030716014174 0ustar liggesusers\name{effect} \alias{effect} \alias{Effect} \alias{Effect.default} \alias{Effect.lm} \alias{Effect.mer} \alias{Effect.merMod} \alias{Effect.lme} \alias{Effect.gls} \alias{Effect.multinom} \alias{Effect.polr} \alias{Effect.poLCA} \alias{Effect.clm2} \alias{Effect.clm} \alias{Effect.clmm} \alias{Effect.mlm} \alias{Effect.svyglm} \alias{allEffects} \alias{as.data.frame.eff} \alias{as.data.frame.effpoly} \alias{as.data.frame.efflatent} \alias{vcov.eff} \title{Functions For Constructing Effect Displays} \description{ \code{Effect} and \code{effect} construct an \code{"eff"} object for a term (usually a high-order term) in a linear model (fit by \code{\link{lm}} or \code{\link[nlme]{gls}}) or generalized linear model (fit by \code{\link{glm}}), or an \code{"effpoly"} object for a term in a multinomial or proportional-odds logit model (fit respectively by \code{\link[nnet]{multinom}} or \code{\link[MASS]{polr}}), absorbing the lower-order terms marginal to the term in question, and averaging over other terms in the model. For multivariate linear models (of class \code{"mlm"}, fit by \code{\link{lm}}), the function constructs a list of \code{"eff"} objects separately for the various response variables. \code{effect} builds the required object by specifying explicity a focal term like \code{"a:b"} for an \code{a} by \code{b} interaction. \code{Effect} specifies the predictors in the term, for example \code{c("a", "b")}, rather than the term itself. \code{Effect} is consequently more flexible and robust than \code{effect}, and will succeed with some models for which \code{effect} fails. The \code{effect} function works by constructing a call to \code{Effect}. The \code{Effect} and \code{effect} functions can also be used with some mixed-effects models, including linear and generalized linear mixed-effects models fit by \code{\link[lme4]{lmer}} and \code{\link[lme4]{glmer}} from the \pkg{lme4} package and \code{\link[nlme]{lme}} from the \pkg{nlme} package; ordinal logit mixed effects fit with \code{\link[ordinal]{clm2}} or \code{\link[ordinal]{clmm}} from the \pkg{ordinal} package, polytomous latent-class models fit by the \code{\link[poLCA]{poLCA}} function in the \pkg{poLCA} package, and generalized linear models fit to data from complex surveys using the \code{\link[survey]{svyglm}} function in the \pkg{survey} package. The displays for mixed-effects models are for the fixed effects only, not for random effects. There is a default method for \code{Effect} that should work with most model objects that have a linear predictor and that respond to the \code{\link{coef}}, \code{\link{model.frame}}, \code{\link{formula}}, and \code{\link{vcov}} functions. \code{allEffects} identifies all of the high-order terms in a model and returns a list of \code{"eff"} or \code{"effpoly"} objects (i.e., an object of type \code{"efflist"}). For information on computing and displaying \emph{predictor effects}, see \code{\link{predictorEffect}} and \code{\link{plot.predictoreff}}. For further information about plotting effects, see \code{\link{plot.eff}}. } \usage{ effect(term, mod, vcov.=vcov, ...) Effect(focal.predictors, mod, ...) \method{Effect}{lm}(focal.predictors, mod, xlevels=list(), fixed.predictors, vcov. = vcov, confint=TRUE, transformation = list(link = family(mod)$linkfun, inverse = family(mod)$linkinv), partial.residuals=FALSE, quantiles=seq(0.2, 0.8, by=0.2), x.var=NULL, ..., #legacy arguments: given.values, typical, offset, se, confidence.level) \method{Effect}{gls}(focal.predictors, mod, xlevels = list(), fixed.predictors, vcov. = vcov, confint=TRUE, transformation = NULL, ..., #legacy arguments: given.values, typical, se, confidence.level) \method{Effect}{multinom}(focal.predictors, mod, xlevels=list(), fixed.predictors, vcov. = vcov, confint=TRUE, ..., #legacy arguments: se, confidence.level, given.values, typical) \method{Effect}{polr}(focal.predictors, mod, xlevels=list(), fixed.predictors, vcov.=vcov, confint=TRUE, latent=FALSE, ..., #legacy arguments: se, confidence.level, given.values, typical) \method{Effect}{clm2}(focal.predictors, mod, ...) \method{Effect}{clmm}(focal.predictors, mod, ...) \method{Effect}{clm}(focal.predictors, mod, ...) \method{Effect}{mer}(focal.predictors, mod, KR=FALSE, ...) \method{Effect}{merMod}(focal.predictors, mod, KR=FALSE, ...) \method{Effect}{lme}(focal.predictors, mod, ...) \method{Effect}{poLCA}(focal.predictors, mod, ...) \method{Effect}{mlm}(focal.predictors, mod, response, ...) \method{Effect}{svyglm}(focal.predictors, mod, fixed.predictors, ...) \method{Effect}{default}(focal.predictors, mod, xlevels = list(), fixed.predictors, vcov. = vcov, confint=TRUE, transformation = list(link = I, inverse = I), ..., #legacy arguments: se, confidence.level, given.values, typical, offset) allEffects(mod, ...) \method{as.data.frame}{eff}(x, row.names=NULL, optional=TRUE, transform=x$transformation$inverse, ...) \method{as.data.frame}{effpoly}(x, row.names=NULL, optional=TRUE, ...) \method{as.data.frame}{efflatent}(x, row.names=NULL, optional=TRUE, ...) \method{vcov}{eff}(object, ...) } \arguments{ \item{term}{the quoted name of a term, usually, but not necessarily, a high-order term in the model. The term must be given exactly as it appears in the printed model, although either colons (\code{:}) or asterisks (\code{*}) may be used for interactions.} \item{focal.predictors}{a character vector of one or more predictors in the model in any order.} \item{mod}{an object of class \code{"lm"}, \code{"gls"}, \code{"glm"}, \code{"multinom"}, \code{"polr"}, \code{"mer"} (or \code{"merMod"}), \code{"lme"}, or \code{"poLCA"}. } \item{xlevels}{this argument is used to set the number of levels for any focal predictor that is not a factor. If \code{xlevels=NULL}, then each numeric predictor is represented by five values equally spaced over its range and then rounded to 'nice' numbers. If \code{xlevels=n} is an integer, then each numeric predictor is represented by \code{n} equally spaced values rounded to 'nice' numbers. More generally, \code{xlevels} can be a named list of values at which to set each numeric predictor. For example, \code{xlevels=list(x1=c(2, 4, 7), x2=5)} would use the values 2, 4 and 7 for the levels of \code{x1}, use 5 equally spaced levels for the levels of \code{x2}, and use the default for any other numeric predictors. If partial residuals are computed, then the focal predictor that is to appear on the horizontal axis of an effect plot is evaluated at 100 equally spaced values along its full range, and, by default, other numeric predictors are evaluated at the quantiles specified in the \code{quantiles} argument, unless their values are given explicitly in \code{xlevels}.} \item{fixed.predictors}{an optional list of specifications affecting the values at which fixed predictors for an effect are set, potentially including: \describe{ \item{given.values}{a numeric vector of named elements, setting particular columns of the model matrix to specific values for predictors that are \emph{not} focal predictors; if specified, these takes precedence over the application of the function given in the \code{typical} list element (below). Care must be taken in specifying these values --- e.g., for a factor, the values of all contrasts should be given and these should be consistent with each other.} \item{typical}{a function to be applied to the columns of the model matrix over which the effect is "averaged"; with the exception of the \code{"svyglm"} method, the default is \code{\link{mean}}. For\code{"svyglm"} objects, the default is to use the survey-design weighted mean.} \item{apply.typical.to.factors}{It generally doesn't make sense to apply typical values that aren't means (e.g., medians) to the columns of the model-matrix representing contrasts for factors. This value generally defaults to \code{FALSE} except for \code{"svyglm"} objects, for which the default is \code{TRUE}, using the the survey-design weighted mean.} \item{offset}{a function to be applied to the offset values (if there is an offset) in a linear or generalized linear model, or a mixed-effects model fit by \code{\link[lme4]{lmer}} or \code{\link[lme4]{glmer}}; or a numeric value, to which the offset will be set. The default is the \code{\link{mean}} function, and thus the offset will be set to its mean; in the case of \code{"svyglm"} objects, the default is to use the survey-design weighted mean. \emph{Note:} Only offsets defined by the \code{offset} argument to \code{\link{lm}}, \code{\link{glm}}, \code{\link[survey]{svyglm}}, \code{\link[lme4]{lmer}}, or \code{\link[lme4]{glmer}} will be handled correctly; use of the \code{offset} function in the model formula is not supported.} } } \item{vcov.}{A function or the name of a function that will be used to get the estimated variance-covariance matrix of the estimated coefficients. This will ordinarily be the default, \code{\link{vcov}}, which will result in the function call \code{vcov(mod)} to get the variance-covariance matrix. You can use the name of any function that takes the model object as its first argument and returns an estimated sample covariance matrix, such as the \code{\link[car]{hccm}} function in the \pkg{car} package, which returns a heteroscedasticity corrected estimate for a linear model.} \item{confint}{a list with any or all of the following elements, controlling whether and how standard errors and confidence limits are computed for the effects: \code{compute} (default \code{TRUE}), whether or not to compute standard errors and confidence limits; \code{level} (default \code{0.95}), confidence level for confidence limits; \code{type}, one of \code{"pointwise"} (the default), \code{"Scheffe"}, or \code{"scheffe"}, whether to compute confidence limits with specified coverage at each point for an effect or to compute limits for a Scheffe-type confidence envelope. For \code{mer}, \code{merMod}, and \code{lme} objects, the normal distribution is used to get confidence limits.} \item{KR}{if \code{TRUE} and the \pkg{pbkrtest} package is installed, use the Kenward-Roger coefficient covariance matrix to compute effect standard errors for linear mixed models fit with \code{\link[lme4]{lmer}} in the \pkg{lme4} package. The default is \code{FALSE} because the computation can be very slow.} \item{transformation}{a two-element list with elements \code{link} and \code{inverse}. For a generalized linear model, these are by default the link function and inverse-link (mean) function. For a linear model, these default to \code{NULL}. If \code{NULL}, the identity (or inhibit) function, \code{\link{I}}, is used; this effect can also be achieved by setting the argument to \code{NULL}. The inverse-link may be used to transform effects when they are printed or plotted; the link may be used in positioning axis labels (see below). If the link is not given, an attempt will be made to approximate it from the inverse-link.} \item{partial.residuals}{if \code{TRUE}, residuals for a linear or generalized linear model will be computed and saved; if \code{FALSE} (the default), residuals are suppressed. If residuals are saved, partial residuals are computed when the effect is plotted: see \code{\link{plot.eff}}. This argument may also be used for mixed-effects models.} \item{quantiles}{quantiles at which to evaluate numeric focal predictors \emph{not} on the horizontal axis, used only when partial residuals are displayed; superceded if the \code{xlevels} argument gives specific values for a predictor.} \item{x.var}{the name or index of the numeric predictor to define the horizontal axis of an effect plot for a linear or generalized linear model; the default is \code{NULL}, in which case the first numeric predictor in the effect will be used \emph{if} partial residuals are to be computed. This argument is intended to be used when \code{partial.residuals} is \code{TRUE}; otherwise, the variable on the horizontal axis can be chosen when the effect object is plotted: see \code{\link{plot.eff}}.} \item{latent}{if \code{TRUE}, effects in a proportional-odds logit model are computed on the scale of the latent response; if \code{FALSE} (the default) effects are computed as individual-level probabilities and logits.} \item{x}{an object of class \code{"eff"}, \code{"effpoly"}, or \code{"efflatent"}.} \item{transform}{a transformation to be applied to the effects and confidence limits, by default taken from the inverse link function saved in the \code{"eff"} object.} \item{row.names, optional}{not used.} \item{response}{for an \code{"mlm"} object, a vector containing the name(s) or indices of one or more response variable(s). The default is to use all responses in the model.} \item{object}{an object of class \code{"eff"} for which the covariance matrix of the effects is desired.} \item{...}{arguments to be passed down.} \item{se, confidence.level, given.values, typical, offset}{legacy arguments retained for backwards compatability; if present, these arguments take precedence of the \code{compute} and \code{level} elements over the \code{confint} list argument and the \code{given.values}, \code{typical}, and \code{offset} elements of the \code{fixed.predictors} list argument. See \code{\link{LegacyArguments}} for details.} } \details{ Normally, the functions to be used directly are \code{allEffects}, to return a list of high-order effects, and the generic \code{plot} function to plot the effects. (see \code{\link{plot.efflist}}, \code{\link{plot.eff}}, and \code{\link{plot.effpoly}}). Alternatively, \code{Effect} can be used to vary a subset of predictors over their ranges, while other predictors are held to typical values. Plots are drawn using the \code{\link{xyplot}} (or in some cases, the \code{\link{densityplot}}) function in the \pkg{lattice} package. Effects may also be printed (implicitly or explicitly via \code{print}) or summarized (using \code{summary}) (see \code{\link{print.efflist}}, \code{\link{summary.efflist}}, \code{\link{print.eff}}, \code{\link{summary.eff}}, \code{\link{print.effpoly}}, and \code{\link{summary.effpoly}}). If asked, the \code{effect} function will compute effects for terms that have higher-order relatives in the model, averaging over those terms (which rarely makes sense), or for terms that do not appear in the model but are higher-order relatives of terms that do. For example, for the model \code{Y ~ A*B + A*C + B*C}, one could compute the effect corresponding to the absent term \code{A:B:C}, which absorbs the constant, the \code{A}, \code{B}, and \code{C} main effects, and the three two-way interactions. In either of these cases, a warning is printed. The \code{as.data.frame} methods convert effect objects to data frames to facilitate the construction of custom displays. In the case of \code{"eff"} objects, the \code{se} element in the data frame is always on the scale of the linear predictor, and the transformation used for the fit and confidence limits is saved in a \code{"transformation"} attribute. See \code{\link{predictorEffects}} for an alternative paradigm for getting effects. } \value{ For \code{lm}, \code{glm},\code{svyglm}, \code{mer} and \code{lme}, \code{effect} and \code{Effect} return an \code{"eff"} object, and for \code{multinom}, \code{polr}, \code{clm}, \code{clmm} and \code{clm2}, an \code{"effpoly"} object, with the components listed below. For an \code{"mlm"} object with one response specified, an \code{"eff"} object is returned, otherwise an \code{"efflist"} object is returned, containing one \code{"eff"} object for each \code{response}. \item{term}{the term to which the effect pertains.} \item{formula}{the complete model formula.} \item{response}{a character string giving the name of the response variable.} \item{y.levels}{(for \code{"effpoly"} objects) levels of the polytomous response variable.} \item{variables}{a list with information about each predictor, including its name, whether it is a factor, and its levels or values.} \item{fit}{(for \code{"eff"} objects) a one-column matrix of fitted values, representing the effect on the scale of the linear predictor; this is a ravelled table, representing all combinations of predictor values.} \item{prob}{(for \code{"effpoly"} objects) a matrix giving fitted probabilities for the effect for the various levels of the the response (columns) and combinations of the focal predictors (rows).} \item{logit}{(for \code{"effpoly"} objects) a matrix giving fitted logits for the effect for the various levels of the the response (columns) and combinations of the focal predictors (rows).} \item{x}{a data frame, the columns of which are the predictors in the effect, and the rows of which give all combinations of values of these predictors.} \item{model.matrix}{the model matrix from which the effect was calculated.} \item{data}{a data frame with the data on which the fitted model was based.} \item{discrepancy}{the percentage discrepancy for the `safe' predictions of the original fit; should be very close to 0. Note: except for \code{gls} models, this is now necessarily 0.} \item{offset}{value to which the offset is fixed; \code{0} if there is no offset.} \item{model}{(for \code{"effpoly"} objects) \code{"multinom"} or \code{"polr"}, as appropriate.} \item{vcov}{(for \code{"eff"} objects) a covariance matrix for the effect, on the scale of the linear predictor.} \item{se}{(for \code{"eff"} objects) a vector of standard errors for the effect, on the scale of the linear predictor.} \item{se.prob, se.logit}{(for \code{"effpoly"} objects) matrices of standard errors for the effect, on the probability and logit scales.} \item{lower, upper}{(for \code{"eff"} objects) one-column matrices of confidence limits, on the scale of the linear predictor.} \item{lower.prob, upper.prob, lower.logit, upper.logit}{(for \code{"effpoly"} objects) matrices of confidence limits for the fitted logits and probabilities; the latter are computed by transforming the former.} \item{confidence.level}{for the confidence limits.} \item{transformation}{(for \code{"eff"} objects) a two-element list, with element \code{link} giving the link function, and element \code{inverse} giving the inverse-link (mean) function.} \item{residuals}{(working) residuals for linear or generalized linear models, to be used by \code{\link{plot.eff}} to plot partial residuals.} \item{x.var}{the name of the predictor to appear on the horizontal axis of an effect plot made from the returned object; will usually be \code{NULL} if partial residuals aren't computed.} \item{family}{for a \code{"glm"} model, the name of the distributional family of the model; for an \code{"lm"} model, this is \code{"gaussian"}; otherwise \code{NULL}. The \code{family} controls how partial residuals are smoothed in plots.} \code{allEffects} returns an \code{"efflist"} object, a list of \code{"eff"} or \code{"effpoly"} objects corresponding to the high-order terms of the model. If \code{mod} is of class \code{"poLCA"} (from the \code{poLCA} package), representing a polytomous latent class model, effects are computed for the predictors given the estimated latent classes. The result is of class \code{"eff"} if the latent class model has 2 categories and of class \code{"effpoly"} with more than 2 categories. } \section{Warnings and Limitations}{ The \code{Effect} function handles factors and covariates differently, and is likely to be confused if one is changed to the other in a model formula. Consequently, formulas that include calls to \code{as.factor}, \code{factor}, or \code{numeric} (as, e.g., in \code{y ~ as.factor(income)}) will cause errors. Instead, create the modified variables outside of the model formula (e.g., \code{fincome <- as.factor(income)}) and use these in the model formula. Factors cannot have colons in level names (e.g., \code{"level:A"}); the \code{effect} function will confuse the colons with interactions; rename levels to remove or replace the colons (e.g., \code{"level.A"}). The functions in the \pkg{effects} package work properly with predictors that are numeric or factors; consequently, e.g., convert logical predictors to factors, and dates to numeric. Empty cells in crossed-factors are now permitted for \code{"lm"}, \code{"glm"}, and \code{"multinom"} models. For \code{"multinom"} models with two or more crossed factors with an empty cell, stacked area plots apparently do not work because of a bug in the \code{\link[lattice]{barchart}} function in the \pkg{lattice} package. However, the default line plots do work. Offsets in linear and generalized linear models are supported, as are offsets in mixed models fit by \code{lmer} or \code{glmer}, but must be supplied through the \code{offset} argument to \code{lm}, \code{glm}, \code{lmer} or \code{glmer}; offsets supplied via calls to the \code{offset} function on the right-hand side of the model formula are not supported. Fitting ordinal mixed-models using \code{\link[ordinal]{clmm}} or \code{\link[ordinal]{clmm2}} permits many options, including a variety of link functions, scale functions, nominal regressors, and various methods for setting thresholds. Effects are currently generated only for the default values of the arguments \code{scale}, \code{nominal}, \code{link} and \code{threshold}, which is equivalent to fitting an ordinal response mixed effects model with a logit link. The effect methods can also be used with objects created using \code{\link[ordinal]{clm}} or \code{\link[ordinal]{clm2}} fitting ordinal response logit models with no random effects, with results similar to those from \code{\link[MASS]{polr}} in the \pkg{MASS} package. Calling any of these functions from within a user-written function may result in errors due to R's scoping rules. See the vignette \code{embedding.pdf} for the \pkg{car} package for a solution to this problem. } \references{ Fox, J. (1987). Effect displays for generalized linear models. \emph{Sociological Methodology} \bold{17}, 347--361. Fox, J. (2003) Effect displays in R for generalised linear models. \emph{Journal of Statistical Software} \bold{8:15}, 1--27, <\url{http://www.jstatsoft.org/v08/i15/}>. Fox, J. and R. Andersen (2006). Effect displays for multinomial and proportional-odds logit models. \emph{Sociological Methodology} \bold{36}, 225--255. Fox, J. and J. Hong (2009). Effect displays in R for multinomial and proportional-odds logit models: Extensions to the effects package. \emph{Journal of Statistical Software} \bold{32:1}, 1--24, <\url{http://www.jstatsoft.org/v32/i01/}>. Fox, J. and S. Weisberg (forthcoming). Visualizing Fit and Lack of Fit in Complex Regression Models: Effect Plots with Partial Residuals. \emph{Journal of Statistical Software}. Hastie, T. J. (1992). Generalized additive models. In Chambers, J. M., and Hastie, T. J. (eds.) \emph{Statistical Models in S}, Wadsworth. Weisberg, S. (2014). \emph{Applied Linear Regression}, 4th edition, Wiley, \url{http://z.umn.edu/alr4ed}. } \author{John Fox \email{jfox@mcmaster.ca}, Sanford Weisberg \email{sandy@umn.edu} and Jangman Hong.} \seealso{\code{\link{LegacyArguments}}. For information on printing, summarizing, and plotting effects: \code{\link{print.eff}}, \code{\link{summary.eff}}, \code{\link{plot.eff}}, \code{\link{print.summary.eff}}, \code{\link{print.effpoly}}, \code{\link{summary.effpoly}}, \code{\link{plot.effpoly}}, \code{\link{print.efflist}}, \code{\link{summary.efflist}}, \code{\link{plot.efflist}}, \code{\link{xyplot}}, \code{\link{densityplot}}.} \examples{ mod.cowles <- glm(volunteer ~ sex + neuroticism*extraversion, data=Cowles, family=binomial) eff.cowles <- allEffects(mod.cowles, xlevels=list(extraversion=seq(0, 24, 6)), fixed.predictors=list(given.values=c(sexmale=0.5))) eff.cowles as.data.frame(eff.cowles[[2]]) \donttest{ # the following are equivalent: eff.ne <- effect("neuroticism*extraversion", mod.cowles) Eff.ne <- Effect(c("neuroticism", "extraversion"), mod.cowles) all.equal(eff.ne$fit, Eff.ne$fit) plot(eff.cowles, 'sex', axes=list(y=list(lab="Prob(Volunteer)"))) plot(eff.cowles, 'neuroticism:extraversion', axes=list(y=list(lab="Prob(Volunteer)", ticks=list(at=c(.1,.25,.5,.75,.9))))) plot(Effect(c("neuroticism", "extraversion"), mod.cowles, confint=list(type="Scheffe"), xlevels=list(extraversion=seq(0, 24, 6)), fixed.predictors=list(given.values=c(sexmale=0.5))), axes=list(y=list(lab="Prob(Volunteer)", ticks=list(at=c(.1,.25,.5,.75,.9))))) plot(eff.cowles, 'neuroticism:extraversion', lines=list(multiline=TRUE), axes=list(y=list(lab="Prob(Volunteer)"))) plot(effect('sex:neuroticism:extraversion', mod.cowles, xlevels=list(extraversion=seq(0, 24, 6))), lines=list(multiline=TRUE)) } # a nested model: mod <- lm(log(prestige) ~ income:type + education, data=Prestige) plot(Effect(c("income", "type"), mod, transformation=list(link=log, inverse=exp)), axes=list(y=list(lab="prestige"))) if (require(nnet)){ mod.beps <- multinom(vote ~ age + gender + economic.cond.national + economic.cond.household + Blair + Hague + Kennedy + Europe*political.knowledge, data=BEPS) \donttest{ plot(effect("Europe*political.knowledge", mod.beps, xlevels=list(political.knowledge=0:3))) } plot(Effect(c("Europe", "political.knowledge"), mod.beps, xlevels=list(Europe=1:11, political.knowledge=0:3), fixed.predictors=list(given.values=c(gendermale=0.5))), lines=list(col=c("blue", "red", "orange")), axes=list(x=list(rug=FALSE), y=list(style="stacked"))) \donttest{ plot(effect("Europe*political.knowledge", mod.beps, # equivalent xlevels=list(Europe=1:11, political.knowledge=0:3), fixed.predictors=list(given.values=c(gendermale=0.5))), lines=list(col=c("blue", "red", "orange")), axes=list(x=list(rug=FALSE), y=list(style="stacked"))) } } if (require(MASS)){ mod.wvs <- polr(poverty ~ gender + religion + degree + country*poly(age,3), data=WVS) \donttest{ plot(effect("country*poly(age, 3)", mod.wvs)) } plot(Effect(c("country", "age"), mod.wvs), axes=list(y=list(style="stacked"))) \donttest{ plot(effect("country*poly(age, 3)", mod.wvs), axes=list(y=list(style="stacked"))) # equivalent plot(effect("country*poly(age, 3)", latent=TRUE, mod.wvs)) plot(effect("country*poly(age, 3)", latent=TRUE, mod.wvs, confint=list(type="scheffe"))) # Scheffe-type confidence envelopes } } mod.pres <- lm(prestige ~ log(income, 10) + poly(education, 3) + poly(women, 2), data=Prestige) eff.pres <- allEffects(mod.pres, xlevels=50) plot(eff.pres) plot(eff.pres[1], axes=list(x=list(income=list( transform=list(trans=log10, inverse=function(x) 10^x), ticks=list(at=c(1000, 2000, 5000, 10000, 20000)) )))) \donttest{ # linear model with log-response and log-predictor # to illustrate transforming axes and setting tick labels mod.pres1 <- lm(log(prestige) ~ log(income) + poly(education, 3) + poly(women, 2), data=Prestige) # effect of the log-predictor eff.log <- Effect("income", mod.pres1) # effect of the log-predictor transformed to the arithmetic scale eff.trans <- Effect("income", mod.pres1, transformation=list(link=log, inverse=exp)) #variations: # y-axis: scale is log, tick labels are log # x-axis: scale is arithmetic, tick labels are arithmetic plot(eff.log) # y-axis: scale is log, tick labels are log # x-axis: scale is log, tick labels are arithmetic plot(eff.log, axes=list(x=list(income=list( transform=list(trans=log, inverse=exp), ticks=list(at=c(5000, 10000, 20000)), lab="income, log-scale")))) # y-axis: scale is log, tick labels are arithmetic # x-axis: scale is arithmetic, tick labels are arithmetic plot(eff.trans, axes=list(y=list(lab="prestige"))) # y-axis: scale is arithmetic, tick labels are arithmetic # x-axis: scale is arithmetic, tick labels are arithmetic plot(eff.trans, axes=list(y=list(type="response", lab="prestige"))) # y-axis: scale is log, tick labels are arithmetic # x-axis: scale is log, tick labels are arithmetic plot(eff.trans, axes=list( x=list(income=list( transform=list(trans=log, inverse=exp), ticks=list(at=c(1000, 2000, 5000, 10000, 20000)), lab="income, log-scale")), y=list(lab="prestige, log-scale")), main="Both response and X in log-scale") # y-axis: scale is arithmetic, tick labels are arithmetic # x-axis: scale is log, tick labels are arithmetic plot(eff.trans, axes=list( x=list( income=list(transform=list(trans=log, inverse=exp), ticks=list(at=c(1000, 2000, 5000, 10000, 20000)), lab="income, log-scale")), y=list(type="response", lab="prestige"))) } if (require(nlme)){ # for gls() mod.hart <- gls(fconvict ~ mconvict + tfr + partic + degrees, data=Hartnagel, correlation=corARMA(p=2, q=0), method="ML") plot(allEffects(mod.hart)) detach(package:nlme) } if (require(lme4)){ data(cake, package="lme4") fm1 <- lmer(angle ~ recipe * temperature + (1|recipe:replicate), cake, REML = FALSE) plot(Effect(c("recipe", "temperature"), fm1)) \donttest{ plot(effect("recipe:temperature", fm1), axes=list(grid=TRUE)) # equivalent (plus grid) } if (any(grepl("pbkrtest", search()))) detach(package:pbkrtest) detach(package:lme4) } \donttest{ if (require(nlme) && length(find.package("lme4", quiet=TRUE)) > 0){ data(cake, package="lme4") cake$rep <- with(cake, paste( as.character(recipe), as.character(replicate), sep="")) fm2 <- lme(angle ~ recipe * temperature, data=cake, random = ~ 1 | rep, method="ML") plot(Effect(c("recipe", "temperature"), fm2)) plot(effect("recipe:temperature", fm2), axes=list(grid=TRUE)) # equivalent (plus grid) } detach(package:nlme) } \donttest{ if (require(poLCA)){ data(election) f2a <- cbind(MORALG,CARESG,KNOWG,LEADG,DISHONG,INTELG, MORALB,CARESB,KNOWB,LEADB,DISHONB,INTELB)~PARTY*AGE nes2a <- poLCA(f2a,election,nclass=3,nrep=5) plot(Effect(c("PARTY", "AGE"), nes2a), axes=list(y=list(style="stacked"))) } } # mlm example if (require(heplots)) { data(NLSY, package="heplots") mod <- lm(cbind(read,math) ~ income+educ, data=NLSY) eff.inc <- Effect("income", mod) plot(eff.inc) eff.edu <- Effect("educ", mod) plot(eff.edu, axes=list(x=list(rug=FALSE), grid=TRUE)) \donttest{ plot(Effect("educ", mod, response="read")) } detach(package:heplots) } # svyglm() example (adapting an example from the survey package) \donttest{ if (require(survey)){ data(api) dstrat<-svydesign(id=~1, strata=~stype, weights=~pw, data=apistrat, fpc=~fpc) mod <- svyglm(sch.wide ~ ell + meals + mobility, design=dstrat, family=quasibinomial()) plot(allEffects(mod), axes=list(y=list(lim=log(c(0.4, 0.99)/c(0.6, 0.01)), ticks=list(at=c(0.4, 0.75, 0.9, 0.95, 0.99))))) } } # component + residual plot examples \donttest{ Prestige$type <- factor(Prestige$type, levels=c("bc", "wc", "prof")) mod.prestige.1 <- lm(prestige ~ income + education, data=Prestige) plot(allEffects(mod.prestige.1, partial.residuals=TRUE)) # standard C+R plots plot(allEffects(mod.prestige.1, partial.residuals=TRUE, confint=list(type="scheffe"))) # with Scheffe-type confidence bands mod.prestige.2 <- lm(prestige ~ type*(income + education), data=Prestige) plot(allEffects(mod.prestige.2, partial.residuals=TRUE)) mod.prestige.3 <- lm(prestige ~ type + income*education, data=Prestige) plot(Effect(c("income", "education"), mod.prestige.3, partial.residuals=TRUE), partial.residuals=list(span=1)) } # artificial data set.seed(12345) x1 <- runif(500, -75, 100) x2 <- runif(500, -75, 100) y <- 10 + 5*x1 + 5*x2 + x1^2 + x2^2 + x1*x2 + rnorm(500, 0, 1e3) Data <- data.frame(y, x1, x2) mod.1 <- lm(y ~ poly(x1, x2, degree=2, raw=TRUE), data=Data) # raw=TRUE necessary for safe prediction mod.2 <- lm(y ~ x1*x2, data=Data) mod.3 <- lm(y ~ x1 + x2, data=Data) plot(Effect(c("x1", "x2"), mod.1, partial.residuals=TRUE)) # correct model plot(Effect(c("x1", "x2"), mod.2, partial.residuals=TRUE)) # wrong model plot(Effect(c("x1", "x2"), mod.3, partial.residuals=TRUE)) # wrong model } \keyword{hplot} \keyword{models}