qq.plot(x, ...)
qqp(...)
qq.plot.default(x, distribution="norm",
ylab=deparse(substitute(x)),
xlab=paste(distribution, "quantiles"), main="", las=par('las'),
envelope=0.95, labels=FALSE, col=palette()[2], lwd=2, pch=1,
line=c("quartiles", "robust", "none"), ...)
qq.plot.lm(x, main="", 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=FALSE, reps=100,
col=palette()[2], lwd=2, pch=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
).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 bookstrap;
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
,
qqline
x<-rchisq(100, df=2)
qq.plot(x)
qq.plot(x, dist="chisq", df=2)
data(Ornstein)
mod<-lm(interlocks~assets+sector+nation, data=Ornstein)
qq.plot(mod, sim=TRUE)
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