These functions draw Ceres plots for linear and generalized linear models.
ceresPlots(model, ...)# S3 method for default
ceresPlots(model, terms = ~., layout = NULL, ask, main,
...)
ceresPlot(model, ...)
# S3 method for lm
ceresPlot(model, variable, id=FALSE,
line=TRUE, smooth=TRUE, col=carPalette()[1], col.lines=carPalette()[-1],
xlab, ylab, pch=1, lwd=2, grid=TRUE, ...)
# S3 method for glm
ceresPlot(model, ...)
NULL. These functions are used for their side effect: producing
plots.
model object produced by lm or glm.
A one-sided formula that specifies a subset of the regressors.
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 regressors, the
component-plus-residual plot is drawn for that predictor only.
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.
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.
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.
A quoted string giving the name of a variable for the horizontal axis
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.
TRUE to plot least-squares line.
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. Ceres plots do not support variance smooths.
color for points; the default is the first entry
in the current car palette (see carPalette
and par).
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")
labels for the x and y axes, respectively. If not set appropriate labels are created by the function.
plotting character for points; default is 1
(a circle, see par).
line width; default is 2 (see par).
If TRUE, the default, a light-gray background grid is put on the graph
John Fox jfox@mcmaster.ca
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.
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.
crPlots, avPlots, showLabels
ceresPlots(lm(prestige~income+education+type, data=Prestige), terms= ~ . - type)
Run the code above in your browser using DataLab