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This function creates a “bubble” plot of Studentized residuals versus hat values, with the areas of the circles representing the observations proportional to the value Cook's distance. Vertical reference lines are drawn at twice and three times the average hat value, horizontal reference lines at -2, 0, and 2 on the Studentized-residual scale.
influencePlot(model, ...)# S3 method for lm
influencePlot(model, scale=10,
xlab="Hat-Values", ylab="Studentized Residuals", id=TRUE, ...)
# S3 method for lmerMod
influencePlot(model, ...)
a linear, generalized-linear, or linear mixed model; the "lmerMod" method calls the "lm" method and can take the same arguments.
a factor to adjust the size of the circles.
axis labels.
settings for labelling points; see link{showLabels} for details. To omit point labelling, set
id=FALSE; the default, id=TRUE is equivalent to id=list(method="noteworthy", n=2, cex=1, col=carPalette()[1], location="lr").
The default method="noteworthy" is used only in this function and indicates setting labels for points with large Studentized residuals, hat-values or Cook's distances. Set
id=list(method="identify") for interactive point identification.
arguments to pass to the plot and points functions.
If points are identified, returns a data frame with the hat values, Studentized residuals and Cook's distance of the identified points. If no points are identified, nothing is returned. This function is primarily used for its side-effect of drawing a plot.
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.
# NOT RUN {
influencePlot(lm(prestige ~ income + education, data=Duncan))
# }
# NOT RUN {
influencePlot(lm(prestige ~ income + education, data=Duncan),
id=list(method="identify"))
# }
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