car (version 2.1-4)

spreadLevelPlot: Spread-Level Plots

Description

Creates plots for examining the possible dependence of spread on level, or an extension of these plots to the studentized residuals from linear models.

Usage

spreadLevelPlot(x, ...)

slp(...)

# S3 method for formula spreadLevelPlot(x, data=NULL, subset, na.action, main=paste("Spread-Level Plot for", varnames[response], "by", varnames[-response]), ...)

# S3 method for default spreadLevelPlot(x, by, robust.line=TRUE, start=0, xlab="Median", ylab="Hinge-Spread", point.labels=TRUE, las=par("las"), main=paste("Spread-Level Plot for", deparse(substitute(x)), "by", deparse(substitute(by))), col=palette()[1], col.lines=palette()[2], pch=1, lwd=2, grid=TRUE, ...)

# S3 method for lm spreadLevelPlot(x, robust.line=TRUE, smoother=loessLine, smoother.args=list(), xlab="Fitted Values", ylab="Absolute Studentized Residuals", las=par("las"), main=paste("Spread-Level Plot for\n", deparse(substitute(x))), pch=1, col=palette()[1], col.lines=palette()[2], col.smoother=palette()[3], lwd=2, grid=TRUE, labels, id.method = "mahal", id.n = if(id.method[1]=="identify") Inf else 0, id.cex=1, id.col=palette()[1], id.location="lr", ...) # S3 method for spreadLevelPlot print(x, ...)

Arguments

x

a formula of the form y ~ x, where y is a numeric vector and x is a factor, or an lm object to be plotted; alternatively a numeric vector.

data

an optional data frame containing the variables to be plotted. By default the variables are taken from the environment from which spreadLevelPlot is called.

subset

an optional vector specifying a subset of observations to be used.

na.action

a function that indicates what should happen when the data contain NAs. The default is set by the na.action setting of options.

by

a factor, numeric vector, or character vector defining groups.

robust.line

if TRUE a robust line is fit using the rlm function in the MASS package; if FALSE a line is fit using lm.

smoother

a function to draw a nonparametric-regression smooth; the default is loessLine, which does loess smoothing. The function gamLine fits a generalized additive model and allows including a link and error function. See ScatterplotSmoothers. Setting this argument to something other than a function, e.g., FALSE suppresses the smoother.

smoother.args

a list of named values to be passed to the smoother function; the specified elements of the list depend upon the smoother (see ScatterplotSmoothers).

start

add the constant start to each data value.

main

title for the plot.

xlab

label for horizontal axis.

ylab

label for vertical axis.

point.labels

if TRUE label the points in the plot with group names.

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; default is the second entry in the current palette

col.smoother

color for smooth line; default is third entry in the current palette.

pch

plotting character for points; default is 1 (a circle, see par).

lwd

line width; default is 2 (see par).

grid

If TRUE, the default, a light-gray background grid is put on the graph

id.method,labels,id.n,id.cex,id.col,id.location

Arguments for the labelling of points. The default is id.n=0 for labeling no points with the lm method. See showLabels for details of these arguments.

arguments passed to plotting functions.

Value

An object of class spreadLevelPlot containing:

Statistics

a matrix with the lower-hinge, median, upper-hinge, and hinge-spread for each group. (Not for an lm object.)

PowerTransformation

spread-stabilizing power transformation, calculated as \(1 - slope\) of the line fit to the plot.

Details

Except for linear models, computes the statistics for, and plots, a Tukey spread-level plot of log(hinge-spread) vs. log(median) for the groups; fits a line to the plot; and calculates a spread-stabilizing transformation from the slope of the line.

For linear models, plots log(abs(studentized residuals) vs. log(fitted values). Point labeling was added in November, 2016.

The function slp is an abbreviation for spreadLevelPlot.

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.

Hoaglin, D. C., Mosteller, F. and Tukey, J. W. (Eds.) (1983) Understanding Robust and Exploratory Data Analysis. Wiley.

See Also

hccm, ncvTest

Examples

Run this code
spreadLevelPlot(interlocks + 1 ~ nation, data=Ornstein)
slp(lm(interlocks + 1 ~ assets + sector + nation, data=Ornstein))

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