# crPlots

##### Component+Residual (Partial Residual) Plots

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

- Keywords
- hplot, regression

##### Usage

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

crPlot(model, ...)

# S3 method for 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], id.location="lr",
order=1, line=TRUE, smoother=loessLine,
smoother.args=list(), smooth, span,
col=palette()[1], col.lines=palette()[-1],
xlab, ylab, pch=1, lwd=2, grid=TRUE, ...)

##### 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,id.location
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.- smoother
Function to add a nonparametric smooth.

- smoother.args
see

`ScatterplotSmoothers`

for available smooethers and arguments.- smooth, span
these arguments are included for backwards compatility: if

`smooth=TRUE`

then`smoother`

is set to`loessLine`

, and if`span`

is specified, it is added to`smoother.args`

.- 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.

##### 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.

##### See Also

##### Examples

```
# NOT RUN {
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))
# }
```

*Documentation reproduced from package car, version 2.1-6, License: GPL (>= 2)*