ceresPlots {car} | R Documentation |
These functions draw Ceres plots for linear and generalized linear models.
ceresPlots(model, terms = ~., layout = NULL, ask, main, ...) ceresPlot(model, ...) ## S3 method for class 'lm' ceresPlot(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], line=TRUE, smooth=TRUE, span=.5, iter, col=palette()[1], col.lines=palette()[-1], xlab, ylab, pch=1, lwd=2, grid=TRUE, ...) ## S3 method for class 'glm' ceresPlot(model, ...)
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 . Factors and nonstandard predictors such as B-splines are
skipped. 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. |
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 |
Overall title for any array of cerers plots; if missing a default is provided. |
... |
ceresPlots passes these arguments to ceresPlot .
ceresPlot 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.
|
line |
TRUE to plot least-squares line.
|
smooth |
TRUE to plot nonparametric-regression (lowess) line.
|
span |
span for lowess smoother. |
iter |
number of robustness iterations for nonparametric-regression smooth; defaults to 3 for a linear model and to 0 for a non-Gaussian glm. |
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 |
Ceres plots are a generalization of component+residual (partial residual) plots that are less prone to leakage of nonlinearity among the predictors.
The function intended for direct use is ceresPlots
.
The model cannot contain interactions, but can contain factors. Factors may be present in the model, but Ceres plots cannot be drawn for them.
NULL
. These functions are used for their side effect: producing
plots.
John Fox jfox@mcmaster.ca
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.
Weisberg, S. (2005) Applied Linear Regression, Third Edition. Wiley.
ceresPlots(lm(prestige~income+education+type, data=Prestige), terms= ~ . - type)