qqplot {distr} | R Documentation |
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 signature '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<100), exact.sCI = (n<100), nosym.pCI = FALSE,
col.pCI = "orange", lty.pCI = 3, lwd.pCI = 2, pch.pCI = par("pch"),
cex.pCI = par("cex"),
col.sCI = "tomato2", lty.sCI = 4, lwd.sCI = 2, pch.sCI = par("pch"),
cex.sCI = par("cex"),
cex.pch = par("cex"), col.pch = par("col"),
jit.fac = 0, check.NotInSupport = TRUE,
col.NotInSupport = "red", with.legend = TRUE, legend.bg = "white",
legend.pos = "topleft", legend.cex = 0.8, legend.pref = "",
legend.postf = "", legend.alpha = alpha.CI, debug = FALSE, withSubst = TRUE)
## S4 method for signature 'ANY,ANY'
qqplot(x, y,
plot.it = TRUE, xlab = deparse(substitute(x)),
ylab = deparse(substitute(y)), ...)
Arguments
x |
object of class |
y |
object of class |
n |
numeric; number of quantiles at which to do the comparison. |
withIdLine |
logical; shall line |
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 |
xlab |
x-label |
ylab |
y-label |
... |
further parameters for function |
width |
width (in inches) of the graphics device opened |
height |
height (in inches) of the graphics device opened |
withSweave |
logical: if |
mfColRow |
shall default partition in panels be used — defaults to |
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 |
col.NotInSupport |
logical; if preceding check |
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 |
withSubst |
logical; if |
Details
- qqplot
signature(x = "ANY", y = "ANY")
: functionqqplot
from package stats.- qqplot
signature(x = "UnivariateDistribution", y = "UnivariateDistribution")
: produces a QQ plot for two univariate distributions.
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 |
crit |
A matrix with the lower and upper confidence bounds
(computed by |
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).
Author(s)
Peter Ruckdeschel peter.ruckdeschel@uni-oldenburg.de
References
Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.
See Also
qqplot
from package stats – the standard QQ plot
function and
qqbounds
, used by qqplot
to produce confidence
intervals.
Examples
## 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
## 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
## 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)