dfbetaPlots(model, ...)
dfbetasPlots(model, ...)
## S3 method for class 'lm':
dfbetaPlots(model, terms= ~ ., intercept=FALSE, layout=NULL, ask,
main, xlab, ylab, labels=rownames(dfbeta),
id.method="y",
id.n=if(id.method[1]=="identify") Inf else 0, id.cex=1,
id.col=palette()[1], col=palette()[1], grid=TRUE, ...)
## S3 method for class 'lm':
dfbetasPlots(model, terms=~., intercept=FALSE, layout=NULL, ask,
main, xlab, ylab,
labels=rownames(dfbeta), id.method="y",
id.n=if(id.method[1]=="identify") Inf else 0, id.cex=1,
id.col=palette()[1], col=palette()[1], grid=TRUE, ...)
lm
or glm
.~.
is to plot against all terms in the model with the exception of an intercept. For example, the
FALSE
.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 la"Index"
.TRUE
, ask the user before drawing the next plot; if FALSE
, the default, don't ask.plot
, points
, and showLabels
id.n=0
for labeling no points. See
showLabels
for details of these arguments.palette
.TRUE
, the default, a light-gray background grid is put on the
graphNULL
. These functions are used for their side effect: producing
plots.dfbeta
,dfbetas
dfbetaPlots(lm(prestige ~ income + education + type, data=Duncan))
dfbetasPlots(glm(partic != "not.work" ~ hincome + children,
data=Womenlf, family=binomial))
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