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

`crPlots(model, ...)`# S3 method for default
crPlots(model, terms = ~., layout = NULL, ask, main,
...)

crp(...)

crPlot(model, ...)

# S3 method for lm
crPlot(model, variable, id=FALSE,
order=1, line=TRUE, smooth=TRUE,
col=carPalette()[1], col.lines=carPalette()[-1],
xlab, ylab, pch=1, lwd=2, grid=TRUE, ...)

model

model object produced by `lm`

or `glm`

.

terms

A one-sided formula that specifies a subset of the regressors.
One component-plus-residual plot is drawn for each regressor. The default
`~.`

is to plot against all numeric regressors. For example, the
specification `terms = ~ . - X3`

would plot against all regressors
except for `X3`

, while `terms = ~ log(X4)`

would give the plot for the
predictor X4 that is represented in the model by log(X4). 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

controls point identification; if `FALSE`

(the default), no points are identified;
can be a list of named arguments to the `showLabels`

function;
`TRUE`

is equivalent to ```
list(method=list(abs(residuals(model, type="pearson")), "x"), n=2,
cex=1, col=carPalette()[1], location="lr")
```

,
which identifies the 2 points with the largest residuals and the 2 points with the most extreme
horizontal (X) values.

order

order of polynomial regression performed for predictor to be plotted; default `1`

.

line

`TRUE`

to plot least-squares line.

smooth

specifies the smoother to be used along with its arguments; if `FALSE`

, no smoother is shown;
can be a list giving the smoother function and its named arguments; `TRUE`

, the default, is equivalent to
`list(smoother=loessLine)`

. See `ScatterplotSmoothers`

for the smoothers supplied by the
car package and their arguments.

col

color for points; the default is the first entry
in the current car palette (see `carPalette`

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.

`NULL`

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

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.

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

Fox, J. (2016)
*Applied Regression Analysis and Generalized Linear Models*,
Third Edition. Sage.

Fox, J. and Weisberg, S. (2019)
*An R Companion to Applied Regression*, Third Edition, Sage.

# NOT RUN { crPlots(m<-lm(prestige ~ income + education, data=Prestige)) crPlots(m, terms=~ . - education) # get only one plot crPlots(lm(prestige ~ log2(income) + education + poly(women,2), data=Prestige)) crPlots(glm(partic != "not.work" ~ hincome + children, data=Womenlf, family=binomial), smooth=list(span=0.75)) # }