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#############################################
# marginal model plots Rev 12/30/09
# 15 March 2010 changed to make
# mmps(lm(longley)) work without specifying data or response
# fixed bug when only one plot is requested --- suppress call to par()
# added 'outerLegend' to label lines
# modified to work correctly with
# 28 May 2010 S. Weisberg, fixed bugs in logistic models
# changed line thickness of mean smooths
# excluded SD smooth from bernoulli models
# added grid lines
# 15 August 2010 fixed colors of points to work properly
# 16 January 2011 improved handling of splines and polynomials in mmps to
# allow plots against base variables (e.g., bs(x, 3) could be
# replaced by just x in the 'terms' argument to mmps.
# 16 June 2011 allow layout=NA, in which case the layout is not set in this
# function, so it is the responsibility of the user
# 14 September 2012 improved the smoothers
# 22 September 2012 added conditioning on one categorical regressor
# 2017-02-13: consolidated smooth and id arguments. J. Fox
# 2017-10-29: Changed line type of smooth of the data to 1 as advertised
# 2017-10-29: Changed default color palette from palette() to carPalette()
# 2019-05-17: in mmp.glm, default horizontal variable when fitted=TRUE is now the
# fitted values for lm and the linear predictor for glm
# 2019-05-17: added ylab arg to mmp() methods. J. Fox
# 2019-11-14: change class(x) == "y" to inherits(x, "y")
#############################################
marginalModelPlot <- function(...){
mmp(...)
}
mmp <- function(model, ...){
UseMethod("mmp")
}
mmp.lm <- function (model, variable, sd = FALSE,
xlab = deparse(substitute(variable)),
smooth=TRUE, key=TRUE, pch, groups=NULL, ...){
smooth <- applyDefaults(smooth, defaults=list(smoother=loessLine, span=2/3), type="smooth")
mmp.default(model, variable, sd, xlab, smooth=smooth, key, pch=pch, groups=groups, ...)
}
mmp.default <- function (model, variable, sd = FALSE,
xlab = deparse(substitute(variable)), ylab, smooth=TRUE, key=TRUE, pch, groups=NULL,
col.line = carPalette()[c(2, 8)], col=carPalette()[1],
id=FALSE, grid=TRUE, ...){
id <- applyDefaults(id, defaults=list(method="y", n=2, cex=1, col=carPalette()[1], location="lr"), type="id")
if (isFALSE(id)){
id.n <- 0
id.method <- "none"
labels <- id.cex <- id.col <- id.location <- NULL
}
else{
labels <- id$labels
if (is.null(labels)) labels <- names((residuals(model)))
id.method <- id$method
id.n <- if ("identify" %in% id.method) Inf else id$n
id.cex <- id$cex
id.col <- id$col
id.location <- id$location
}
smoother.args <- applyDefaults(smooth, defaults=list(smoother=loessLine, span=2/3), type="smooth")
if (!isFALSE(smoother.args)) {
smoother <- smoother.args$smoother
smoother.args$smoother <- NULL
}
else smoother <- "none"
lwd <- match.call(expand.dots=TRUE)$lwd
if(missing(pch)) pch <- 1
groups.col <- col
if (!is.null(groups)){
if(is.data.frame(groups)) {
groups.label <- colnames(groups)[1]
groups <- groups[,1]
} else {
groups.label <- deparse(substitute(groups))
}
groups.levels <- unique(na.omit(groups))
for (j in 1:(length(groups.levels))) {
pch[groups==groups.levels[j]] <- j
groups.col[groups==groups.levels[j]] <- carPalette()[j]}
}
if (!is.null(attr(model$model, "na.action"))) {
if (attr(attr(model$model, "na.action"), "class") == "exclude")
model <- update(model, na.action=na.omit)}
if (missing(variable)) {
xlab <- "Fitted values"
u <- fitted(model)
} else {
u <- variable}
resp <- model.response(model.frame(model))
if (missing(ylab)) ylab <- colnames(model$model[1])
plot(u, resp,
xlab = xlab, ylab = ylab, type="n", ...)
if(grid){
grid(lty=1, equilogs=FALSE)
box()}
points(u, model$model[ , 1], col=groups.col, pch=pch, ...)
if (!(is.character(smoother) && smoother == "none")){
ow <- options(warn=-1)
on.exit(options(ow))
smoother.args$lty.spread <- 1
smoother.args$lwd <- smoother.args$lwd.spread <- if(is.null(lwd)) 2 else lwd
if(is.null(groups)) {
smoother.args$lty.smooth <- 1
smoother(u, resp, col.line[1], log.x=FALSE, log.y=FALSE, spread=sd,
smoother.args=smoother.args)
smoother.args$lty.smooth <- smoother.args$lty.spread <- 2
# 11/21/14: SD smooth under the model corrected by adding the 'offset'
smoother(u, predict(model), col.line[2], log.x=FALSE, log.y=FALSE, spread=sd,
smoother.args=smoother.args, offset=sigmaHat(model))
# smoother.args=smoother.args)
if(key){
outerLegend(c("Data", "Model"), lty=1:2, col=col.line,
bty="n", cex=0.75, fill=col.line, border=col.line, horiz=TRUE,
offset=0)
}
} else {
for (j in 1:length(groups.levels)) {
smoother.args$lwd <- if(is.null(lwd)) 1.75 else lwd
smoother.args$lty.smooth <- 1
sel <- groups == groups.levels[j]
smoother(u[sel], resp[sel], carPalette()[j], log.x=FALSE, log.y=FALSE, spread=FALSE,
smoother.args=smoother.args)
smoother.args$lty.smooth <- 2
smoother(u[sel], predict(model)[sel], carPalette()[j], log.x=FALSE, log.y=FALSE, spread=FALSE,
smoother.args=smoother.args)
}
items <- paste(groups.label, groups.levels, sep= " = ")
col.items <- carPalette()[1:length(groups.levels)]
lty.items <- 1
if(key) plotArrayLegend(location="top",
items=items, col.items=col.items,
lty.items=lty.items , lwd.items=2, title="Legend")
}
}
showLabels(u, resp, labels=labels,
method=id.method, n=id.n, cex=id.cex,
col=id.col, location=id.location)
}
mmp.glm <- function (model, variable, sd = FALSE,
xlab = deparse(substitute(variable)), ylab,
smooth=TRUE, key=TRUE, pch, groups=NULL,
col.line = carPalette()[c(2, 8)], col=carPalette()[1],
id=FALSE, grid=TRUE, ...){
id <- applyDefaults(id, defaults=list(method="y", n=2, cex=1, col=carPalette()[1], location="lr"), type="id")
if (isFALSE(id)){
id.n <- 0
id.method <- "none"
labels <- id.cex <- id.col <- id.location <- NULL
}
else{
labels <- id$labels
if (is.null(labels)) labels <- names((residuals(model)))
id.method <- id$method
id.n <- if ("identify" %in% id.method) Inf else id$n
id.cex <- id$cex
id.col <- id$col
id.location <- id$location
}
smoother.args <- applyDefaults(smooth, defaults=list(smoother=gamLine, k=3), type="smooth")
if (!isFALSE(smoother.args)) {
smoother <- smoother.args$smoother
smoother.args$smoother <- NULL
}
else smoother <- "none"
lwd <- match.call(expand.dots=TRUE)$lwd
if(missing(pch)) pch <- 1
groups.col <- col
groups.pch <- match.call(expand.dots=TRUE)$pch
if(is.null(groups.pch)) groups.pch <- 1
if (!is.null(groups)){
if(is.data.frame(groups)) {
groups.label <- colnames(groups)[1]
groups <- groups[,1]
} else {
groups.label <- deparse(substitute(groups))
}
groups.levels <- unique(na.omit(groups))
for (j in 1:(length(groups.levels))) {
pch[groups==groups.levels[j]] <- j
groups.col[groups==groups.levels[j]] <- carPalette()[j]}
}
if (missing(variable)) {
xlab <- "Linear Predictor"
u <- predict(update(model, na.action=na.omit), type="link")
# u <- fitted(update(model, na.action=na.omit)) #deleted 5/17/2019
} else {
u <- variable }
response <- model.response(model.frame(model))
fam <- model$family$family
lin <- model$family$link
pw <- model$prior.weights # relevant only for binomial
bernoulli <- FALSE
if(fam == "binomial") {
if(!any(pw > 1.1)) bernoulli <- TRUE
if (is.factor(response)) {response <- as.numeric(response) - 1}
if (is.matrix(response)){response <- response[, 1]/pw}
}
if (missing(ylab)) ylab <- colnames(model$model[1])
plot(u, response, type="n", xlab = xlab, ylab = ylab)
if(grid){
grid(lty=1, equilogs=FALSE)
box()}
points(u, response, col=col, pch=pch, ...)
if (!(is.character(smoother) && smoother == "none")){
ow <- options(warn=-1)
on.exit(options(ow))
smoother.args$lty.smooth <- 1
smoother.args$family <- fam
smoother.args$link <- lin
smoother.args$weights <- pw
model.fit <- if(fam=="binomial") predict(model, type="response")/pw
else predict(model, type="response")
if(is.null(groups)) {
smoother(u, response, col.line[1], log.x=FALSE, log.y=FALSE, spread=FALSE,
smoother.args=smoother.args)
smoother.args$lty.smooth <- 2
smoother(u, model.fit, col.line[2], log.x=FALSE, log.y=FALSE, spread=FALSE,
smoother.args=smoother.args)
if(key){ outerLegend(c("Data", "Model"), lty=1:2, col=col.line,
bty="n", cex=0.75, fill=col.line, border=col.line,
horiz=TRUE, offset=0)
}
} else {
for (j in 1:length(groups.levels)) {
smoother.args$lwd <- if(is.null(lwd)) 1.75 else lwd
smoother.args$lty.smooth <- 1
sel <- groups == groups.levels[j]
smoother(u[sel], response[sel], carPalette()[j], log.x=FALSE, log.y=FALSE,
spread=FALSE, smoother.args=smoother.args)
smoother.args$lty.smooth <- 2
smoother(u[sel], model.fit[sel], carPalette()[j], log.x=FALSE, log.y=FALSE,
spread=FALSE, smoother.args=smoother.args)
}
items <- paste(groups.label, groups.levels, sep= " = ")
col.items <- carPalette()[1:length(groups.levels)]
lty.items <- 1
if(key) plotArrayLegend(location="top",
items=items, col.items=col.items,
lty.items=lty.items , lwd.items=2, title="Legend")
}
}
showLabels(u, as.numeric(model$model[, 1]), labels=labels,
method=id.method, n=id.n, cex=id.cex,
col=id.col, location=id.location)
}
marginalModelPlots <- function(...) mmps(...)
mmps <- function(model, terms= ~ ., fitted=TRUE, layout=NULL, ask,
main, groups, key=TRUE, ...){
mf <- if(!is.null(terms)) termsToMf(model, terms) else NULL
labels2 <- attr(attr(mf$mf.vars, "terms"), "term.labels")
order2 <- attr(attr(mf$mf.vars, "terms"), "order")
type2 <- rep("good", length(labels2))
if(length(labels2) > 0) {
for (j in 1:length(labels2)){
if(order2[j] > 1) type2[j] <- NA #exclude interatctions
if(inherits(mf$mf.vars[[labels2[j]]], "factor")) type2[j] <- NA #no factors
if(inherits(mf$mf.vars[[labels2[j]]], "matrix")) type2[j] <- "original"
}
if (any( type2=="original", na.rm=TRUE )){
p1 <- try(predict(model, type="terms"), silent=TRUE)
if(inherits(p1, "try-error")) {type2[type2=="original"] <- NA} else
warning("Splines and/or polynomials replaced by a fitted linear combination")
}
}
groups <- if (!missing(groups)) {
termsToMf(model, as.formula(paste("~",deparse(substitute(groups)))))$mf.vars[, 2, drop=FALSE]
} else {
if(is.null(mf$mf.groups)) NULL else
mf$mf.groups[, 2, drop=FALSE]
}
# If key=TRUE, determine the coordinates of the key:
oma3 <- 1.5 # room for title in the outer margin ALWAYS
mar3 <- if (is.null(groups)) 1.5 else .2 +
if(is.data.frame(groups)) length(unique(groups[, 1])) else
length(unique(groups))
nt <- sum(!is.na(type2)) + fitted
if (missing(main)) main <- if (nt == 1)
"Marginal Model Plot" else "Marginal Model Plots"
if (nt == 0) stop("No plots specified")
if (nt > 1 & (is.null(layout) || is.numeric(layout))) {
if(is.null(layout)){
layout <- switch(min(nt, 9), c(1, 1), c(1, 2), c(2, 2), c(2, 2),
c(3, 2), c(3, 2), c(3, 3), c(3, 3), c(3, 3))
}
ask <- if(missing(ask) || is.null(ask)) prod(layout)<nt else ask
}
op <- if(nt > 1) par(mfrow=layout, ask=ask, no.readonly=TRUE,
oma=c(0, 0, oma3 , 0), mar=c(5.1, 4.1, mar3, 2.1)) else
par(oma=c(0, 0, oma3 , 0), mar=c(5.1, 4.1, mar3, 2.1))
on.exit(par(op))
legend2 <- function(){
usr <- par("usr")
coords <-list(x=usr[1], y=usr[3])
leg <- legend( coords, c("Data", "Model"), lty=1:2, lwd=2, bty="n",
cex=0.9, plot=FALSE)
coords <- list(x = usr[2] - leg$rect$w, y=usr[4] + leg$rect$h)
legend( coords, c("Data", "Model"), lty=1:2, lwd=2, bty="n", xpd=NA, cex=0.9)
}
if (length(labels2) > 0) {
for (j in 1:length(labels2)) {
if(!is.na(type2[j])) {
horiz <- if(type2[j] == "original"){p1[, labels2[j]]} else {
if(type2[j] == "good") mf$mf.vars[ , labels2[j]] else NULL}
lab <- labels2[j]
mmp(model, horiz, xlab=lab, groups=groups, key=key, ...)
if(!is.null(groups)) legend2()}
}
}
if(fitted==TRUE) mmp(model, groups=groups, key=key, ...)
if(!is.null(groups)) legend2()
mtext(side=3, outer=TRUE, main, line=0.1, cex=1.2)
if(any(is.na(type2))) warning("Interactions and/or factors skipped")
invisible()
}
|
