Nothing
`plotlogistic.fit.fnc` <-
function(x, data, method = "cut", where = seq(0, 1, by=0.1), scalesize=NA, ...) {
requireNamespace("lme4", quietly = TRUE)
requireNamespace("rms", quietly = TRUE)
if (class(x)[1]=="mer") {
y=attr(x@frame, "terms")
depvar = names(attr(stats::terms(y),"dataClasses")[attr(stats::terms(y),"response")])
probs = stats::fitted(x) # this used to be the fitted logit, but is now the fitted probability
} else {
if (class(x)[1] == "lrm") {
depvar = as.character(stats::formula(x$call))[2]
probs = stats::predict(x,type="fitted")
} else {
stop("first argument is not an lmer or lrm model")
}
}
if (method == "cut") {
classes = Hmisc::cut2(probs, where, levels.mean = TRUE)
classCounts = table(classes)
means = tapply(as.numeric(data[,depvar])-1, classes, mean)
means = means[!is.na(means)]
} else {
if (method == "shingle") {
sh = lattice::equal.count(probs)
means = rep(0, length(levels(sh)))
midpoints = rep(0, length(means))
for (i in 1:length(levels(sh))) {
means[i] = mean(probs[probs>levels(sh)[[i]][1] & probs < levels(sh)[[i]][2]])
midpoints[i] = as.character(mean(levels(sh)[[i]]))
}
names(means) = as.character(midpoints)
}
}
graphics::plot(as.numeric(names(means)), means,
xlab = "mean predicted probabilities",
ylab = "observed proportions", type="n")
graphics::abline(0, 1, col = "grey")
if ((method=="cut") & (!is.na(scalesize))) {
graphics::symbols(as.numeric(names(means)),as.numeric(means), circles=as.numeric(classCounts),
inches=scalesize, main=" ", add=T)
} else {
graphics::points(as.numeric(names(means)), means)
}
graphics::mtext(paste("R-squared: ", round(stats::cor(as.numeric(names(means)), means)^2,2),
sep=""), 3, 1.5)
}
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