# influencePlot

##### Regression Influence Plot

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.

- Keywords
- regression

##### Usage

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

##### Arguments

- model
a linear or generalized-linear model.

- scale
a factor to adjust the size of the circles.

- xlab, ylab
axis labels.

- 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.

##### See Also

##### Examples

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

*Documentation reproduced from package car, version 3.0-0, License: GPL (>= 2)*