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 = ~ . - X3
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 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
, showLabels
ceresPlots(lm(prestige~income+education+type, data=Prestige), terms= ~ . - type)
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