distrMod: qqplot – R documentation

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qqplot

Methods for Function qqplot in Package ‘distrMod’

Description

We generalize function qqplot from package stats to be applicable to distribution and probability model objects, as well as to estimate objects. In this context, qqplot produces a QQ plot of data (argument x) against a (model) distribution. If the second argument is of class 'Estimate', qqplot looks at the estimate.call-slot and checks whether it can use an argument ParamFamily to conclude on the model distribution. Graphical parameters may be given as arguments to qqplot. In all title and 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 'ANY,UnivariateDistribution'
qqplot(x,y,
    n = length(x), withIdLine = TRUE,
    withConf = TRUE, withConf.pw  = withConf, withConf.sim = withConf,
    plot.it = TRUE, datax = FALSE, xlab = deparse(substitute(x)),
    ylab = deparse(substitute(y)),
    ..., width = 10, height = 5.5, withSweave = getdistrOption("withSweave"),
    mfColRow = TRUE, n.CI = n, with.lab = FALSE, lab.pts = NULL, which.lbs = NULL,
    which.Order = NULL, which.nonlbs = NULL, attr.pre = FALSE, order.traf = NULL,
    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"), added.points.CI = TRUE,
    cex.pch = par("cex"), col.pch = par("col"),
    cex.pts = 1, col.pts = par("col"), pch.pts = 19,
    cex.npts = 1, col.npts = grey(.5), pch.npts = 20,
    cex.lbs = par("cex"), col.lbs = par("col"), adj.lbs = par("adj"),
    alpha.trsp = NA, jit.fac = 0, jit.tol = .Machine$double.eps,
    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,ProbFamily'
qqplot(x, y,
   n = length(x), withIdLine = TRUE, withConf = TRUE,
   withConf.pw  = withConf,  withConf.sim = withConf,
    plot.it = TRUE, xlab = deparse(substitute(x)),
    ylab = deparse(substitute(y)), ...)
## S4 method for signature 'ANY,Estimate'
qqplot(x, y,
   n = length(x), withIdLine = TRUE, withConf = TRUE,
   withConf.pw  = withConf,  withConf.sim = withConf,
    plot.it = TRUE, xlab = deparse(substitute(x)),
    ylab = deparse(substitute(y)), ...)

Arguments

`x`	data to be checked for compatibility with distribution/model `y`.
`y`	object of class `"UnivariateDistribution"` or of class `"ProbFamily"`.
`n`	numeric; assumed sample size (by default length of `x`).
`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`)?
`datax`	logical; shall data be plotted on x-axis?
`xlab`	x-label
`ylab`	y-label
`...`	further parameters for method `qqplot` with signature `ANY,UnivariateDistribution` or with 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
`with.lab`	logical; shall observation labels be plotted in?
`lab.pts`	character or `NULL`; observation labels to be used
`attr.pre`	logical; do graphical attributes for plotted data refer to indices prior (`TRUE`) or posterior to selection via arguments `which.lbs`, `which.Order`, `which.nonlbs` (`FALSE`)?
`which.lbs`	integer or `NULL`; which observations shall be labelled
`which.Order`	integer or `NULL`; which of the ordered (remaining) observations shall be labelled
`which.nonlbs`	indices of the observations which should be plotted but not labelled; either an integer vector with the indices of the observations to be plotted into graph or `NULL` — then all non-labelled observations are plotted.
`order.traf`	function or `NULL`; an optional trafo by which the observations are ordered (as order(trafo(obs)).
`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
`added.points.CI`	logical; should CIs be plotted through additional points (and not only through data points)?
`cex.pch`	magnification factor for the plotted symbols (for backward compatibility); it is ignored once `col.pts` is specified.
`col.pch`	color for the plotted symbols (for backward compatibility); it is ignored once `col.pts` is specified.
`cex.pts`	size of the points of the second argument plotted, can be a vector; if argument `attr.pre` is `TRUE`, it is recycled to the length of all observations and determines the sizes of all plotted symbols, i.e., the selection is done within this argument; in this case argument `col.npts` is ignored. If `attr.pre` is `FALSE`, `cex.pts` is recycled to the number of the observations selected for labelling and refers to the index ordering after the selection. Then argument `cex.npts` deteremines the sizes of the shown but non-labelled observations as given in argument `which.nonlbs`.
`col.pts`	color of the points of the second argument plotted, can be a vector as in `cex.pts` (with `col.npts` as counterpart).
`pch.pts`	symbol of the points of the second argument plotted, can be a vector as in `cex.pts` (with `pch.npts` as counterpart).
`col.npts`	color of the non-labelled points of the `data` argument plotted; (may be a vector).
`pch.npts`	symbol of the non-labelled points of the `data` argument plotted (may be a vector).
`cex.npts`	size of the non-labelled points of the `data` argument plotted (may be a vector).
`cex.lbs`	magnification factor for the plotted observation labels
`col.lbs`	color for the plotted observation labels
`adj.lbs`	adj parameter for the plotted observation labels
`alpha.trsp`	alpha transparency to be added ex post to colors `col.pch` and `col.lbs`; if one-dim and NA all colors are left unchanged. Otherwise, with usual recycling rules `alpha.trsp` gets shorted/prolongated to length the data-symbols to be plotted. Coordinates of this vector `alpha.trsp` with NA are left unchanged, while for the remaining ones, the alpha channel in rgb space is set to the respective coordinate value of `alpha.trsp`. The non-NA entries must be integers in [0,255] (0 invisible, 255 opaque).
`jit.fac`	jittering factor used for discrete distributions.
`jit.tol`	threshold for jittering: if distance between points is smaller than `jit.tol`, points are considered replicates.
`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 axis lables is used; otherwise no substitution is used.

Details

qqplot: signature(x = "ANY", y = "UnivariateDistribution"): produces a QQ plot of a dataset x against the theoretical quantiles of distribution y.
qqplot: signature(x = "ANY", y = "ProbFamily"): produces a QQ plot of a dataset x against the theoretical quantiles of the model distribution of model y. Passed through the ... argument, all arguments valid for signature(x = "ANY", y = "UnivariateDistribution") are also valid for this signature.
qqplot: signature(x = "ANY", y = "Estimate"): produces a QQ plot of a dataset x against the theoretical quantiles of the model distribution of the model that can be reconstructed from the estimator y; more specifically, it tries to get hand at the argument 'ParamFamily' of the esimator's call; if this is available, internally this model is shifted to the estimated parameter by a call to modifyModel, and then this shifted model is used in a call to the (x = "ANY", y = "UnivariateDistribution")-method. Passed through the ... argument, all arguments valid for signature(x = "ANY", y = "UnivariateDistribution") are also valid for this signature.

Value

As for function qqplot from package stats: a list with components

`x`	The x coordinates of the points that were/would be plotted
`y`	The corresponding quantiles of the second distribution, including `NA`s.
`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).

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.

Examples

set.seed(123)
x <- rnorm(40,mean=15,sd=30)
qqplot(x, Chisq(df=15))
NF <- NormLocationScaleFamily(mean=15, sd=30)
qqplot(x, NF, with.lab=TRUE, which.Order=1:5, cex.lbs=1.3)
mlE <- MLEstimator(x, NF)
qqplot(x, mlE)

distrMod

Object Oriented Implementation of Probability Models

v2.8.4

LGPL-3

Authors

Matthias Kohl [aut, cph], Peter Ruckdeschel [cre, cph], R Core Team [ctb, cph] (for source file 'format.perc')

Initial release

2020-03-06