# Modification of plot.lm from the stats package for R.
#
# Copyright (C) 1995-2005 The R Core Team
# Copyright (C) 2005, 2006, 2008 Heather Turner
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software Foundation; either version 2 or 3 of the License
# (at your option).
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# A copy of the GNU General Public License is available at
# http://www.r-project.org/Licenses/
plot.gnm <- function (x, which = c(1:3, 5),
caption = c("Residuals vs Fitted", "Normal Q-Q",
"Scale-Location", "Cook's distance",
"Residuals vs Leverage"),
panel = if (add.smooth) panel.smooth else points,
sub.caption = NULL, main = "", ask = prod(par("mfcol")) <
length(which) && dev.interactive(), ...,
id.n = 3, labels.id = names(residuals(x)), cex.id = 0.75,
qqline = TRUE, cook.levels = c(0.5, 1.0),
add.smooth = getOption("add.smooth"),
label.pos = c(4, 2), cex.caption = 1)
{
if (!is.numeric(which) || any(which < 1) || any(which > 5))
stop("'which' must be in 1:5")
show <- rep(FALSE, 5)
show[which] <- TRUE
r <- residuals(x)
yh <- predict(x) # != fitted() for glm
w <- weights(x)
if (!is.null(w)) { # drop obs with zero wt: PR#6640
wind <- w != 0
r <- r[wind]
yh <- yh[wind]
w <- w[wind]
labels.id <- labels.id[wind]
}
n <- length(r)
if (any(show[2:5])) {
s <- sqrt(deviance(x)/df.residual(x))
hii <- c(hatvalues(x))
if (any(show[4:5])) {
cook <- c(cooks.distance(x))
}
}
if (any(show[2:3])) {
ylab23 <- "Std. deviance resid."
r.w <- if (is.null(w)) r else sqrt(w) * r
}
if (show[5]) {
ylab5 <- "Std. Pearson resid."
r.w <- residuals(x, "pearson")
if(!is.null(w)) r.w <- r.w[wind] # drop 0-weight cases
r.hat <- range(hii, na.rm = TRUE) # though should never have NA
isConst.hat <- all(r.hat == 0) || diff(r.hat) < 1e-10 * mean(hii)
}
dropInf <- function(x) {
if(any(isInf <- is.infinite(x))) {
warning("Not plotting observations with leverage one:\n ",
paste(which(isInf), collapse=", "))
x[isInf] <- NaN
}
x
}
if (any(show[c(2:3,5)]))
rs <- dropInf( r.w/(s * sqrt(1 - hii)) )
if (any(show[c(1, 3)]))
l.fit <- "Predicted values"
if (is.null(id.n))
id.n <- 0
else {
id.n <- as.integer(id.n)
if (id.n < 0 || id.n > n)
stop(gettextf("'id.n' must be in {1,..,%d}", n), domain = NA)
}
if (id.n > 0) { ## label the largest residuals
if (is.null(labels.id))
labels.id <- paste(1:n)
iid <- 1:id.n
show.r <- sort.list(abs(r), decreasing = TRUE)[iid]
if (any(show[2:3]))
show.rs <- sort.list(abs(rs), decreasing = TRUE)[iid]
text.id <- function(x, y, ind, adj.x = TRUE) {
labpos <-
if (adj.x) label.pos[1 + as.numeric(x > mean(range(x)))] else 3
text(x, y, labels.id[ind], cex = cex.id, xpd = TRUE,
pos = labpos, offset = 0.25)
}
}
getCaption <- function(k) # allow caption = "" , plotmath etc
as.graphicsAnnot(unlist(caption[k]))
if (is.null(sub.caption)) { ## construct a default:
cal <- x$call
if (!is.na(m.f <- match("formula", names(cal)))) {
cal <- cal[c(1, m.f)]
names(cal)[2] <- "" # drop " formula = "
}
cc <- deparse(cal, 80) # (80, 75) are ``parameters''
nc <- nchar(cc[1], "c")
abbr <- length(cc) > 1 || nc > 75
sub.caption <-
if (abbr) paste(substr(cc[1], 1, min(75, nc)), "...") else cc[1]
}
one.fig <- prod(par("mfcol")) == 1
if (ask) {
oask <- devAskNewPage(TRUE)
on.exit(devAskNewPage(oask))
}
##---------- Do the individual plots : ----------
if (show[1]) {
ylim <- range(r, na.rm = TRUE)
if (id.n > 0)
ylim <- extendrange(r = ylim, f = 0.08)
plot(yh, r, xlab = l.fit, ylab = "Residuals", main = main,
ylim = ylim, type = "n", ...)
panel(yh, r, ...)
if (one.fig)
title(sub = sub.caption, ...)
mtext(getCaption(1), 3, 0.25, cex = cex.caption)
if (id.n > 0) {
y.id <- r[show.r]
y.id[y.id < 0] <- y.id[y.id < 0] - strheight(" ")/3
text.id(yh[show.r], y.id, show.r)
}
abline(h = 0, lty = 3, col = "gray")
}
if (show[2]) { ## Normal
ylim <- range(rs, na.rm = TRUE)
ylim[2] <- ylim[2] + diff(ylim) * 0.075
qq <- qqnorm(rs, main = main, ylab = ylab23, ylim = ylim, ...)
if (qqline) qqline(rs, lty = 3, col = "gray50")
if (one.fig)
title(sub = sub.caption, ...)
mtext(getCaption(2), 3, 0.25, cex = cex.caption)
if (id.n > 0)
text.id(qq$x[show.rs], qq$y[show.rs], show.rs)
}
if (show[3]) {
sqrtabsr <- sqrt(abs(rs))
ylim <- c(0, max(sqrtabsr, na.rm = TRUE))
yl <- as.expression(substitute(sqrt(abs(YL)),
list(YL = as.name(ylab23))))
yhn0 <- if (is.null(w)) yh else yh[w != 0]
plot(yhn0, sqrtabsr, xlab = l.fit, ylab = yl, main = main,
ylim = ylim, type = "n", ...)
panel(yhn0, sqrtabsr, ...)
if (one.fig)
title(sub = sub.caption, ...)
mtext(getCaption(3), 3, 0.25, cex = cex.caption)
if (id.n > 0)
text.id(yhn0[show.rs], sqrtabsr[show.rs], show.rs)
}
if (show[4]) {
if (id.n > 0) {
show.r <- order(-cook)[iid]# index of largest 'id.n' ones
ymx <- cook[show.r[1]] * 1.075
}
else ymx <- max(cook, na.rm = TRUE)
plot(cook, type = "h", ylim = c(0, ymx), main = main,
xlab = "Obs. number", ylab = "Cook's distance", ...)
if (one.fig)
title(sub = sub.caption, ...)
mtext(getCaption(4), 3, 0.25, cex = cex.caption)
if (id.n > 0)
text.id(show.r, cook[show.r], show.r, adj.x = FALSE)
}
if (show[5]) {
ylim <- range(rs, na.rm = TRUE)
if (id.n > 0) {
ylim <- extendrange(r = ylim, f = 0.08)
show.r <- order(-cook)[iid]
}
do.plot <- TRUE
if (isConst.hat) {## leverages are all the same
caption[5] <- "Constant Leverage:\n Residuals vs Factor Levels"
## plot against factor-level combinations instead
aterms <- attributes(terms(x))
## classes w/o response
dcl <- aterms$dataClasses[-aterms$response]
facvars <- names(dcl)[dcl %in% c("factor", "ordered")]
mf <- model.frame(x)[facvars]# better than x$model
if(ncol(mf) > 0) {
## now re-order the factor levels *along* factor-effects
## using a "robust" method {not requiring dummy.coef}:
effM <- mf
for(j in seq_len(ncol(mf)))
effM[, j] <- vapply(split(yh, mf[, j]), mean, 1)[mf[, j]]
ord <- do.call(order, effM)
dm <- data.matrix(mf)[ord, , drop = FALSE]
## #{levels} for each of the factors:
nf <- length(nlev <- unlist(unname(lapply(x$xlevels, length))))
ff <- if(nf == 1) 1 else rev(cumprod(c(1, nlev[nf:2])))
facval <- ((dm-1) %*% ff)
## now reorder to the same order as the residuals
facval[ord] <- facval
xx <- facval # for use in do.plot section.
plot(facval, rs, xlim = c(-1/2, sum((nlev-1) * ff) + 1/2),
ylim = ylim, xaxt = "n",
main = main, xlab = "Factor Level Combinations",
ylab = ylab5, type = "n", ...)
axis(1, at = ff[1]*(1:nlev[1] - 1/2) - 1/2,
labels= x$xlevels[[1]][order(vapply(split(yh,mf[,1]),
mean, 1))])
mtext(paste(facvars[1],":"), side = 1, line = 0.25, adj=-.05)
abline(v = ff[1]*(0:nlev[1]) - 1/2, col="gray", lty="F4")
panel(facval, rs, ...)
abline(h = 0, lty = 3, col = "gray")
}
else { # no factors
message("hat values (leverages) are all = ",
format(mean(r.hat)),
"\n and there are no factor predictors; no plot no. 5")
frame()
do.plot <- FALSE
}
}
else { ## Residual vs Leverage
xx <- hii
## omit hatvalues of 1.
xx[xx >= 1] <- NA
plot(xx, rs, xlim = c(0, max(xx, na.rm = TRUE)), ylim = ylim,
main = main, xlab = "Leverage", ylab = ylab5, type = "n",
...)
panel(xx, rs, ...)
abline(h = 0, v = 0, lty = 3, col = "gray")
if (one.fig)
title(sub = sub.caption, ...)
if (length(cook.levels)) {
p <- length(coef(x))
usr <- par("usr")
hh <- seq.int(min(r.hat[1], r.hat[2]/100), usr[2],
length.out = 101)
for (crit in cook.levels) {
cl.h <- sqrt(crit * p * (1 - hh)/hh)
lines(hh, cl.h, lty = 2, col = 2)
lines(hh, -cl.h, lty = 2, col = 2)
}
legend("bottomleft", legend = "Cook's distance",
lty = 2, col = 2, bty = "n")
xmax <- min(0.99, usr[2])
ymult <- sqrt(p * (1 - xmax)/xmax)
aty <- c(-sqrt(rev(cook.levels)) * ymult,
sqrt(cook.levels) * ymult)
axis(4, at = aty,
labels = paste(c(rev(cook.levels), cook.levels)),
mgp = c(.25, .25, 0), las = 2, tck = 0,
cex.axis = cex.id, col.axis = 2)
}
} # if(const h_ii) .. else ..
if (do.plot) {
mtext(getCaption(5), 3, 0.25, cex = cex.caption)
if (id.n > 0) {
y.id <- rs[show.r]
y.id[y.id < 0] <- y.id[y.id < 0] - strheight(" ")/3
text.id(xx[show.r], y.id, show.r)
}
}
}
if (!one.fig && par("oma")[3] >= 1)
mtext(sub.caption, outer = TRUE, cex = 1.25)
invisible()
}
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