R: Quantile-Comparison Plots

qqPlot {car}

R Documentation

Quantile-Comparison Plots

Description

Plots empirical quantiles of a variable, or of studentized residuals from a linear model, against theoretical quantiles of a comparison distribution.

Usage

qqPlot(x, ...)

qqp(...)

## Default S3 method:
qqPlot(x, distribution="norm", ...,
	ylab=deparse(substitute(x)), xlab=paste(distribution, "quantiles"), 
	main=NULL, las=par("las"),
	envelope=.95,  
	col=palette()[1], col.lines=palette()[2], lwd=2, pch=1, cex=par("cex"), 
	line=c("quartiles", "robust", "none"),  
	labels = if(!is.null(names(x))) names(x) else seq(along=x),
	id.method = "y", 
	id.n =if(id.method[1]=="identify") Inf else 0,
	id.cex=1, id.col=palette()[1], grid=TRUE)

## S3 method for class 'lm'
qqPlot(x, xlab=paste(distribution, "Quantiles"),
	ylab=paste("Studentized Residuals(", deparse(substitute(x)), ")",
		sep=""), main=NULL,
	distribution=c("t", "norm"), line=c("robust", "quartiles", "none"),
	las=par("las"), simulate=TRUE, envelope=.95,  
	reps=100, col=palette()[1], col.lines=palette()[2], lwd=2, 
	pch=1, cex=par("cex"),
	labels, id.method = "y", 
	id.n = if(id.method[1]=="identify") Inf else 0,
	id.cex=1, id.col=palette()[1], grid=TRUE, ...)

Arguments

`x`	vector of numeric values or `lm` object.
`distribution`	root name of comparison distribution – e.g., `"norm"` for the normal distribution; `t` for the t-distribution.
`ylab`	label for vertical (empirical quantiles) axis.
`xlab`	label for horizontal (comparison quantiles) axis.
`main`	label for plot.
`envelope`	confidence level for point-wise confidence envelope, or `FALSE` for no envelope.
`las`	if `0`, ticks labels are drawn parallel to the axis; set to `1` for horizontal labels (see `par`).
`col`	color for points; the default is the first entry in the current color palette (see `palette` and `par`).
`col.lines`	color for lines; the default is the second entry in the current color palette.
`pch`	plotting character for points; default is `1` (a circle, see `par`).
`cex`	factor for expanding the size of plotted symbols; the default is `1`.
`labels`	vector of text strings to be used to identify points, defaults to `names(x)` or observation numbers if `names(x)` is `NULL`.
`id.method`	point identification method. The default `id.method="y"` will identify the `id.n` points with the largest value of `abs(y-mean(y))`. See `showLabels` for other options.
`id.n`	number of points labeled. If `id.n=0`, the default, no point identification.
`id.cex`	set size of the text for point labels; the default is `cex` (which is typically `1`).
`id.col`	color for the point labels.
`lwd`	line width; default is `2` (see `par`).
`line`	`"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"` suppresses the line.
`simulate`	if `TRUE` calculate confidence envelope by parametric bootstrap; for `lm` object only. The method is due to Atkinson (1985).
`reps`	integer; number of bootstrap replications for confidence envelope.
`...`	arguments such as `df` to be passed to the appropriate quantile function.
`grid`	If TRUE, the default, a light-gray background grid is put on the graph

Details

Draws theoretical quantile-comparison plots for variables and for studentized residuals from a linear model. A comparison line is drawn on the plot either through the quartiles of the two distributions, or by robust regression.

Any distribution for which quantile and density functions exist in R (with prefixes q and d, respectively) may be used. Studentized residuals from linear models are plotted against the appropriate t-distribution.

The function qqp is an abbreviation for qqPlot.

Value

These functions return the labels of identified points.

Author(s)

John Fox jfox@mcmaster.ca

References

Fox, J. (2008) Applied Regression Analysis and Generalized Linear Models, Second Edition. Sage.

Fox, J. and Weisberg, S. (2011) An R Companion to Applied Regression, Second Edition, Sage.

Atkinson, A. C. (1985) Plots, Transformations, and Regression. Oxford.

Examples

x<-rchisq(100, df=2)
qqPlot(x)
qqPlot(x, dist="chisq", df=2)

qqPlot(lm(prestige ~ income + education + type, data=Duncan), 
	envelope=.99)

[Package car version 2.0-12 Index]