qqplot2 {copula} | R Documentation |
Q-Q Plot with Rugs and Pointwise Asymptotic Confidence Intervals
Description
A Q-Q plot (possibly) with rugs and pointwise approximate (via the
Central Limit Theorem) two-sided 1-\alpha
confidence intervals.
Usage
qqplot2(x, qF, log = "", qqline.args = if(log=="x" || log=="y")
list(untf=TRUE) else list(),
rug.args = list(tcl=-0.6*par("tcl")),
alpha = 0.05, CI.args = list(col="gray40"),
CI.mtext = list(text=paste0("Pointwise asymptotic ", 100*(1-alpha),
"% confidence intervals"), side=4,
cex=0.6*par("cex.main"), adj=0, col="gray40"),
main = quote(bold(italic(F)~~"Q-Q plot")),
main.args = list(text=main, side=3, line=1.1, cex=par("cex.main"),
font=par("font.main"), adj=par("adj"), xpd=NA),
xlab = "Theoretical quantiles", ylab = "Sample quantiles",
file="", width=6, height=6, ...)
Arguments
x |
|
qF |
(theoretical) quantile function against which the Q-Q plot is created. |
log |
|
qqline.args |
argument |
rug.args |
argument |
alpha |
significance level. |
CI.args |
argument |
CI.mtext |
argument |
main |
title (can be an expression; use "" for no title). |
main.args |
argument |
xlab |
x axis label. |
ylab |
y axis label. |
file |
file name including the extension “.pdf”. |
width |
width parameter of |
height |
height parameter of |
... |
additional arguments passed to |
Details
See the source code for how the confidence intervals are constructed precisely.
Value
invisible()
.
See Also
plot()
for the underlying plot function,
qqline()
for how the Q-Q line is implemented,
rug()
for how the rugs are constructed,
lines()
for how the confidence intervals are drawn, and
mtext()
for how the title and information about the
confidence intervals is printed.
pdf()
for plotting to pdf.
Examples
n <- 250
df <- 7
set.seed(1)
x <- rchisq(n, df=df)
## Q-Q plot against the true quantiles (of a chi^2_3 distribution)
qqplot2(x, qF = function(p) qchisq(p, df=df),
main = substitute(bold(italic(chi[NU])~~"Q-Q Plot"), list(NU=df)))
## in log-log scale
qqplot2(x, qF = function(p) qchisq(p, df=df), log="xy",
main = substitute(bold(italic(chi[NU])~~"Q-Q Plot"), list(NU=df)))
## Q-Q plot against wrong quantiles (of an Exp(1) distribution)
qqplot2(x, qF=qexp, main = quote(bold(Exp(1)~~"Q-Q Plot")))