# influencePlot: Regression Influence Plot

## Description

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.

## Usage

influencePlot(model, ...)# S3 method for lm
influencePlot(model, scale=10,
xlab="Hat-Values", ylab="Studentized Residuals", id=TRUE, ...)

# S3 method for lmerMod
influencePlot(model, ...)

## Arguments

model

a linear, generalized-linear, or linear mixed model; the `"lmerMod"`

method calls the `"lm"`

method and can take the same arguments.

scale

a factor to adjust the size of the circles.

id

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.

## Value

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.

## References

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.

## Examples

# NOT RUN {
influencePlot(lm(prestige ~ income + education, data=Duncan))
# }
# NOT RUN {
influencePlot(lm(prestige ~ income + education, data=Duncan),
id=list(method="identify"))
# }