R: Component+Residual (Partial Residual) Plots

crPlots {car}

R Documentation

Component+Residual (Partial Residual) Plots

Description

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

Usage

crPlots(model, terms = ~., layout = NULL, ask, main, 
    ...)

crp(...)

crPlot(model, ...)

## S3 method for class 'lm'
crPlot(model, variable, 
  id.method = list(abs(residuals(model, type="pearson")), "x"),
  labels, 
  id.n = if(id.method[1]=="identify") Inf else 0,
  id.cex=1, id.col=palette()[1],
  order=1, line=TRUE, smooth=TRUE,
	iter, span=.5, 
  col=palette()[1], col.lines=palette()[-1],
  xlab, ylab, pch=1, lwd=2, grid=TRUE, ...)

## S3 method for class 'glm'
crPlot(model, ...)

Arguments

`model`	model object produced by `lm` or `glm`.
`terms`	A one-sided formula that specifies a subset of the predictors. One component-plus-residual plot is drawn for each term. The default `~.` is to plot against all numeric predictors. For example, the specification `terms = ~ . - X3` would plot against all predictors except for `X3`. If this argument is a quoted name of one of the predictors, the component-plus-residual plot is drawn for that predictor only.
`layout`	If set to a value like `c(1, 1)` or `c(4, 3)`, the layout of the graph will have this many rows and columns. If not set, the program will select an appropriate layout. If the number of graphs exceed nine, you must select the layout yourself, or you will get a maximum of nine per page. If `layout=NA`, the function does not set the layout and the user can use the `par` function to control the layout, for example to have plots from two models in the same graphics window.
`ask`	If `TRUE`, ask the user before drawing the next plot; if `FALSE`, the default, don't ask. This is relevant only if not all the graphs can be drawn in one window.
`main`	The title of the plot; if missing, one will be supplied.
`...`	`crPlots` passes these arguments to `crPlot`. `crPlot` passes them to `plot`.
`variable`	A quoted string giving the name of a variable for the horizontal axis
`id.method,labels,id.n,id.cex,id.col`	Arguments for the labelling of points. The default is `id.n=0` for labeling no points. See `showLabels` for details of these arguments.
`order`	order of polynomial regression performed for predictor to be plotted; default `1`.
`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.
`col`	color for points; the default is the first entry in the current color palette (see `palette` and `par`).
`col.lines`	a list of at least two colors. The first color is used for the ls line and the second color is used for the fitted lowess line. To use the same color for both, use, for example, `col.lines=c("red", "red")`
`xlab,ylab`	labels for the x and y axes, respectively. If not set appropriate labels are created by the function.
`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

Details

The function intended for direct use is crPlots, for which crp is an abbreviation.

The model cannot contain interactions, but can contain factors. Parallel boxplots of the partial residuals are drawn for the levels of a factor.

Value

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

Author(s)

John Fox jfox@mcmaster.ca

References

Cook, R. D. and Weisberg, S. (1999) Applied Regression, Including Computing and Graphics. Wiley.

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.

Examples

crPlots(m<-lm(prestige~income+education, data=Prestige)) 
# get only one plot
crPlots(m, terms=~ . - education)

crPlots(lm(prestige ~ log2(income) + education + poly(women,2), data=Prestige))

crPlots(glm(partic != "not.work" ~ hincome + children, 
  data=Womenlf, family=binomial))

[Package car version 2.0-12 Index]