dfbeta and dfbetas Index Plots
These functions display index plots of dfbeta (effect on coefficients of deleting each observation in turn) and dfbetas (effect on coefficients of deleting each observation in turn, standardized by a deleted estimate of the coefficient standard error). In the plot of dfbeta, horizontal lines are drawn at 0 and +/- one standard error; in the plot of dfbetas, horizontal lines are drawn and 0 and +/- 1.
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=carPalette()[1], id.location="lr", col=carPalette()[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=carPalette()[1], id.location="lr", col=carPalette()[1], grid=TRUE, ...)
model |
model object produced by |
terms |
A one-sided formula that specifies a subset of the terms in the model.
One dfbeta or dfbetas plot is drawn for each regressor. The default
|
intercept |
Include the intercept in the plots; default is |
layout |
If set to a value like |
main |
The title of the graph; if missing, one will be supplied. |
xlab |
Horizontal axis label; defaults to |
ylab |
Vertical axis label; defaults to coefficient name. |
ask |
If |
... |
optional additional arguments to be passed to |
.
id.method, labels, id.n, id.cex, id.col, id.location |
Arguments for the labelling of
points. The default is |
col |
color for points; defaults to the first entry in the color |
grid |
If |
NULL
. These functions are used for their side effect: producing
plots.
John Fox jfox@mcmaster.ca
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.
dfbetaPlots(lm(prestige ~ income + education + type, data=Duncan)) dfbetasPlots(glm(partic != "not.work" ~ hincome + children, data=Womenlf, family=binomial))
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