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lmplot2 <- function(
x,
which = 1:5,
caption = c("Residuals vs Fitted", "Normal Q-Q plot",
"Scale-Location plot", "Cook's distance plot"),
panel = panel.smooth,
sub.caption = deparse(x$call),
main = "",
ask = interactive() && nb.fig < length(which)
&& .Device != "postscript",
...,
id.n = 3,
labels.id = names(residuals(x)),
cex.id = 0.75,
band=TRUE,
rug=TRUE,
width=1/10,
max.n=5000
)
{
if (!inherits(x, "lm"))
stop("Use only with 'lm' objects")
show <- rep(FALSE, 5)
if(!is.numeric(which) || any(which < 1) || any(which > 5))
stop("`which' must be in 1:5")
show[which] <- TRUE
r <- residuals(x)
n <- length(r)
if(inherits(x,"glm"))
yh <- predict(x) # != fitted() for glm
else
yh <- fitted(x)
if (any(show[2:4]))
s <- if(inherits(x, "rlm")) x$s else sqrt(deviance(x)/df.residual(x))
if (any(show[2:3]))
{
ylab23 <- if(inherits(x, "glm"))
"Std. deviance resid." else "Standardized residuals"
hii <- lm.influence(x)$hat
w <- weights(x)
# r.w := weighted.residuals(x):
r.w <- if(is.null(w)) r else (sqrt(w)*r)[w!=0]
rs <- r.w/(s * sqrt(1 - hii))
}
if (any(show[c(1,3)]))
l.fit <- if(inherits(x,"glm"))
"Predicted values" else "Fitted values"
if (is.null(id.n))
id.n <- 0
else {
id.n <- as.integer(id.n)
if(id.n < 0 || id.n > n)
stop(paste("`id.n' must be in { 1,..,",n,"}"))
}
if(id.n > 0) {
if(is.null(labels.id))
labels.id <- paste(1:n)
iid <- 1:id.n
show.r <- order(-abs(r))[iid]
if(any(show[2:3]))
show.rs <- order(-abs(rs))[iid]
text.id <- function(x,y, ind, adj.x = FALSE)
text(x - if(adj.x) strwidth(" ")*cex.id else 0, y, labels.id[ind],
cex = cex.id, xpd = TRUE, adj = if(adj.x) 1)
}
nb.fig <- prod(par("mfcol"))
one.fig <- prod(par("mfcol")) == 1
if (ask) {
op <- par(ask = TRUE)
on.exit(par(op))
}
##---------- Do the individual plots : ----------
if (show[1]) {
ylim <- range(r)
if(id.n > 0)
ylim <- ylim + c(-1,1)* 0.08 * diff(ylim)
plot(yh, r, xlab = l.fit, ylab = "Residuals", main = main,
ylim = ylim, type = "n", ...)
panel(yh, r, ...)
if(rug) rug(yh) ## GRW 2001-06-08
if(band) bandplot(yh,r,add=TRUE,width=width) ## GRW 2001-06-08
if (one.fig)
title(sub = sub.caption, ...)
mtext(caption[1], 3, 0.25)
if(id.n > 0) {
y.id <- r[show.r]
y.id[y.id < 0] <- y.id[y.id < 0] - strheight(" ")/3
text.id(yh[show.r], y.id, show.r, adj.x = TRUE)
}
abline(h = 0, lty = 3, col = "gray")
}
if (show[2]) {
ylim <- range(rs)
ylim[2] <- ylim[2] + diff(ylim) * 0.075
qq <- qqnorm(rs, main = main, ylab = ylab23, ylim = ylim, ...)
qqline(rs)
if (one.fig)
title(sub = sub.caption, ...)
mtext(caption[2], 3, 0.25)
if(id.n > 0)
text.id(qq$x[show.rs], qq$y[show.rs], show.rs, adj.x = TRUE)
}
if (show[3]) {
sqrtabsr <- sqrt(abs(rs))
ylim <- c(0, max(sqrtabsr))
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, ...)
abline(h=mean(sqrtabsr),lty = 3, col = "gray")
if(rug) rug(yh) ## GRW 2001-06-08
if(band) bandplot(yhn0,sqrtabsr,add=TRUE) ## GRW 2001-06-08
if (one.fig)
title(sub = sub.caption, ...)
mtext(caption[3], 3, 0.25)
if(id.n > 0)
text.id(yhn0[show.rs], sqrtabsr[show.rs], show.rs, adj.x = TRUE)
}
if (show[4]) {
cook <- cooks.distance(x, sd=s)
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)
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(caption[4], 3, 0.25)
if(id.n > 0)
text.id(show.r, cook[show.r] + 0.4*cex.id * strheight(" "), show.r)
}
if (show[5])
{
## plot residuals against each predictor ##
data <- model.frame(x)
for( i in 1:ncol(data) )
{
test <- try(
{
plot.default( x=data[,i], y=r,
xlab=names(data)[i], ylab="Residuals", type="n")
panel( data[,i], r, ... )
if(rug) rug(data[,i])
if(band) bandplot(data[,i],r,add=TRUE)
abline(h=0,lty = 3, col = "gray")
}
)
}
}
if (!one.fig && par("oma")[3] >= 1)
mtext(sub.caption, outer = TRUE, cex = 1.25)
invisible()
}
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