dfbetaPlots  R Documentation 
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(dfbetas), 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 onesided 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.
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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