car (version 2.0-21)

qqPlot: 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(...)

## S3 method for class 'default':
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 othe

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" supp

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

Value

• These functions return the labels of identified points.

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. When plotting a vector, the confidence envelope is based on the SEs of the order statistics of an independent random sample from the comparison distribution (see Fox, 2008). Studentized residuals from linear models are plotted against the appropriate t-distribution with a point-wise confidence envelope computed by default by a parametric bootstrap, as described by Atkinson (1985). The function qqp is an abbreviation for qqPlot.

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.

See Also

qqplot, qqnorm, qqline, showLabels

Examples

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

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