car (version 1.2-16)

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, ...)

## S3 method for class 'lm':
cr.plot(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", ...)

## S3 method for class 'glm':
cr.plot(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

cr.plots(lm(prestige~income+education, data=Prestige), 
  variable="income")
  cr.plots(glm(partic != "not.work" ~ hincome + children, 
  data=Womenlf, family=binomial))