qqfun(x, distribution="norm", ylab=deparse(substitute(x)),
xlab=paste(distribution, "quantiles"), main=NULL, las=par("las"),
envelope=.95, labels=FALSE, col=palette()[4], lcol=palette()[2],
xlim=NULL, ylim=NULL, lwd=1, pch=1, bg=palette()[4], cex=.4,
line=c("quartiles", "robust", "none"), ...)
norm
for the
normal distribution; t
for the t-distribution.FALSE
for no envelope.FALSE
for no labels.0
, ticks labels are drawn parallel to the
axis; set to 1
for horizontal labels (see par
).1
(a circle, see par
)..4
.1
(see par
).
Confidence envelopes are drawn at half this line width."quartiles"
to pass a line through the quartile-pairs, or
"robust"
for a robust-regression line; the latter uses the rlm
function in the MASS
package. Specifying line = "none"
suppredf
to be passed to the appropriate quantile function.NULL
. These functions are used only for their side effect (to make a graph).q
and d
, respectively) may be used.
Studentized residuals are plotted against the appropriate t-distribution.This is adapted from qq.plot of package car with different values for points and lines, more options, more transparent code and examples in the current setting. Another similar but sophisticated function is qqmath of package lattice.
Leemis, L. M., J. T. Mcqueston (2008) Univariate distribution relationships. The American Statistician 62:45-53
qqnorm
, qqunif
, gcontrol2
p <- runif(100)
alpha <- 1/log(10)
qqfun(p,dist="unif")
qqfun(-log10(p),dist="exp",rate=alpha,pch=21)
#library(car)
#qq.plot(p,dist="unif")
#qq.plot(-log10(p),dist="exp",rate=alpha)
#library(lattice)
#qqmath(~ -log10(p), distribution = function(p) qexp(p,rate=alpha))
Run the code above in your browser using DataLab