qq.plot(x, ...)
qqp(...)
## S3 method for class 'default':
qq.plot(x, distribution="norm", ylab=deparse(substitute(x)),
xlab=paste(distribution, "quantiles"), main=NULL, las=par("las"),
envelope=.95, labels=FALSE, col=palette()[2], lwd=2, pch=1, cex=1,
line=c("quartiles", "robust", "none"), ...)
## S3 method for class 'lm':
qq.plot(x, main=NULL, xlab=paste(distribution, "Quantiles"),
ylab=paste("Studentized Residuals(", deparse(substitute(x)), ")",
sep = ""),
distribution=c("t", "norm"), line=c("quartiles", "robust", "none"),
las=par('las'), simulate=FALSE, envelope=0.95, labels=names(rstudent),
reps=100, col=palette()[2], lwd=2, pch=1, cex=1, ...)lm object.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).1.2 (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" suppTRUE calculate confidence envelope by parametric bootstrap;
for lm object only. The method is due to Atkinson (1985).df 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.
The function qqp is an abbreviation for qq.plot.qqplot, qqnorm,
qqlinex<-rchisq(100, df=2)
qq.plot(x)
qq.plot(x, dist="chisq", df=2)
qq.plot(lm(interlocks~assets+sector+nation, data=Ornstein), sim=TRUE)Run the code above in your browser using DataLab