qqplot: Methods for Function qqplot in Package `distr'

Description

We generalize function qqplot from package stats to be applicable to distribution objects. In this context, qqplot produces a QQ plot of two distributions, i.e.; argument x is the distribution to be checked for compatibility, and y is the model (\(H_0\)-)distribution. Graphical parameters may be given as arguments to qqplot. The stats function is just the method for signature x=ANY,y=ANY. In all title and axis label arguments, if withSubst is TRUE, the following patterns are substituted:

"%C": class of argument x
"%A": deparsed argument x
"%D": time/date-string when the plot was generated

Usage

qqplot(x, y, ...)
# S4 method for UnivariateDistribution,UnivariateDistribution
qqplot(x, y,
    n = 30, withIdLine = TRUE, withConf = TRUE,
    withConf.pw  = withConf,  withConf.sim = withConf,
    plot.it = TRUE, xlab = deparse(substitute(x)),
    ylab = deparse(substitute(y)), ...,
    width = 10, height = 5.5, withSweave = getdistrOption("withSweave"),
    mfColRow = TRUE, n.CI = n, col.IdL = "red", lty.IdL = 2, lwd.IdL = 2,
    alpha.CI = .95, exact.pCI = (n

Value

A list of elements containing the information needed to compute the respective QQ plot, in particular it extends the elements of the return value of function qqplot

from package stats, i.e., a list with components x and y for x and y coordinates of the plotted points; more specifically it contains

x: The x coordinates of the points that were/would be plotted
y: The corresponding quantiles of the second distribution, including NAs.
crit: A matrix with the lower and upper confidence bounds (computed by qqbounds).
err: logical vector of length 2.

(elements crit and err are taken from the return value(s) of qqbounds). The return value allows to recover all information used to produce the plot for later use in enhanced graphics (e.g. with ggplot).

Arguments

x: object of class "ANY" (stats-method) or of code "UnivariateDistribution"; to be compared to y.
y: object of class "ANY" (stats-method) or of class "UnivariateDistribution".
n: numeric; number of quantiles at which to do the comparison.
withIdLine: logical; shall line y = x be plotted in?
withConf: logical; shall confidence lines be plotted?
withConf.pw: logical; shall pointwise confidence lines be plotted?
withConf.sim: logical; shall simultaneous confidence lines be plotted?
plot.it: logical; shall be plotted at all (inherited from qqplot)?
xlab: x-label
ylab: y-label
...: further parameters for function plot
width: width (in inches) of the graphics device opened
height: height (in inches) of the graphics device opened
withSweave: logical: if TRUE (for working with Sweave) no extra device is opened and height/width are not set
mfColRow: shall default partition in panels be used --- defaults to TRUE
n.CI: numeric; number of points to be used for confidence interval
col.IdL: color for the identity line
lty.IdL: line type for the identity line
lwd.IdL: line width for the identity line
alpha.CI: confidence level
exact.pCI: logical; shall pointwise CIs be determined with exact Binomial distribution?
exact.sCI: logical; shall simultaneous CIs be determined with exact kolmogorov distribution?
nosym.pCI: logical; shall we use (shortest) asymmetric CIs?
col.pCI: color for the pointwise CI
lty.pCI: line type for the pointwise CI
lwd.pCI: line width for the pointwise CI
pch.pCI: symbol for points (for discrete mass points) in pointwise CI
cex.pCI: magnification factor for points (for discrete mass points) in pointwise CI
col.sCI: color for the simultaneous CI
lty.sCI: line type for the simultaneous CI
lwd.sCI: line width for the simultaneous CI
pch.sCI: symbol for points (for discrete mass points) in simultaneous CI
cex.sCI: magnification factor for points (for discrete mass points) in simultaneous CI
cex.pch: magnification factor for the plotted symbols
col.pch: color for the plotted symbols
jit.fac: jittering factor used for discrete distributions
check.NotInSupport: logical; shall we check if all x-quantiles lie in support(y)?
col.NotInSupport: logical; if preceding check TRUE color of x-quantiles if not in support(y)
with.legend: logical; shall a legend be plotted?
legend.bg: background color for the legend
legend.pos: position for the legend
legend.cex: magnification factor for the legend
legend.pref: character to be prepended to legend text
legend.postf: character to be appended to legend text
legend.alpha: nominal coverage probability
debug: logical; if TRUE additional output to debug confidence bounds.
withSubst: logical; if TRUE (default) pattern substitution for titles and lables is used; otherwise no substitution is used.

Author

Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de

Details

qqplot: signature(x = "ANY", y = "ANY"): function qqplot from package stats.
qqplot: signature(x = "UnivariateDistribution", y = "UnivariateDistribution"): produces a QQ plot for two univariate distributions.

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

Examples

Run this code

## IGNORE_RDIFF_BEGIN
qqplot(Norm(15,sqrt(30)), Chisq(df=15))
## some discrete Distributions:
P <- Pois(5)
B <- Binom(size=2000,prob=5/2000)
qqplot(B,P)
## IGNORE_RDIFF_END
# \donttest{
## takes too much time for R CMD check --as-cran
qqplot(B,P, nosym.pCI=TRUE)
# }
## some Lebesgue-Decomposed distributions:
mylist <- UnivarLebDecDistribution(discretePart=Binom(3,.3), acPart=Norm(2,2),
               acWeight=11/20)
mylist2 <- mylist+0.1

## IGNORE_RDIFF_BEGIN
qqplot(mylist,mylist2)
qqplot(mylist,mylist2,exact.pCI=FALSE,exact.sCI=FALSE)
## IGNORE_RDIFF_END

# \donttest{
## takes too much time for R CMD check --as-cran
qqplot(mylist,mylist2,nosym.pCI=TRUE)
## some ac. distribution with a gap
mylist3 <- UnivarMixingDistribution(Unif(0,0.3),Unif(0.6,1),mixCoeff=c(0.8,0.2))
gaps(mylist3)
mylist4 <- UnivarMixingDistribution(Unif(0,0.3),Unif(0.6,1),mixCoeff=c(0.6,0.4))
qqplot(mylist3,mylist4)
qqplot(mylist3,mylist4,nosym.pCI=TRUE)
# }

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