car (version 1.0-7)

cr.plots: Component+Residual (Partial Residual) Plots

Description

These functions construct component+residual plots (also called partial-residual plots) for linear and generalized linear models.

Usage

cr.plots(model, variable, ask=missing(variable), one.page=!ask, 
  span=0.5, ...)

crp(...)

cr.plot(model, ...)

cr.plot.lm(model, variable, order=1, line=TRUE, smooth=TRUE, iter, 
  span=0.5, las=par('las'), col=palette()[2], pch=1, lwd=2,
  main="Component+Residual Plot", ...)

cr.plot.glm(model, ...)

Arguments

model
model object produced by lm or glm.
variable
variable (if it exists in the search path) or name of variable. This argument usually is omitted for crp or cr.plots.
ask
if TRUE, a menu is provided in the R Console for the user to select the variable(s) to plot, and to modify the span for the smoother used to draw a nonparametric-regression line on the plot.
one.page
if TRUE (and ask=FALSE), put all plots on one graph.
order
order of polynomial regression performed for predictor to be plotted.
line
TRUE to plot least-squares line.
smooth
TRUE to plot nonparametric-regression (lowess) line.
iter
number of robustness iterations for nonparametric-regression smooth; defaults to 3 for a linear model and to 0 for a non-Gaussian glm.
span
span for lowess smoother.
las
if 0, ticks labels are drawn parallel to the axis; set to 1 for horizontal labels (see par).
col
color for points and lines; the default is the second entry in the current color palette (see palette and par).
pch
plotting character for points; default is 1 (a circle, see par).
lwd
line width; default is 2 (see par).
main
title for plot.
...
pass arguments down.

Value

  • NULL. These functions are used for their side effect: producing plots.

Details

The function intended for direct use is cr.plots (for which crp is an abbreviation). By default, these functions are used interactively through a text menu. The model cannot contain interactions, but can contain factors. Parallel boxplots of the partial residuals are drawn for the levels of a factor.

References

Cook, R. D. and Weisberg, S. (1999) Applied Regression, Including Computing and Graphics. Wiley. Fox, J. (1997) Applied Regression, Linear Models, and Related Methods. Sage.

See Also

ceres.plots, av.plots

Examples

Run this code
data(Womenlf)
cr.plots(glm(partic != "not.work" ~ hincome + children, 
  data=Womenlf, family=binomial))

data(Prestige)
cr.plots(lm(prestige~income+education, data=Prestige), 
  variable="income")

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