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, ...)lm or glm.~. is to plot against all numeric predictors. For example, the
specification terms = ~ . - X3c(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 layoTRUE, 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.ceresPlots passes these arguments to ceresPlot.
ceresPlot passes them to plot.id.n=0 for labeling no points. See
showLabels for details of these arguments.TRUE to plot least-squares line.TRUE to plot nonparametric-regression (lowess) line.col.lines=c("red", "red")1
(a circle, see par).2 (see par).NULL. These functions are used for their side effect: producing
plots.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.crPlots, avPlots, showLabelsceresPlots(lm(prestige~income+education+type, data=Prestige), terms= ~ . - type)Run the code above in your browser using DataLab