Regression Influence Plot

This function creates a “bubble” plot of studentized residuals versus hat values, with size of the points representing Cook's distances.

influence_plot(x, alpha = 0.4, n.labels = 2)

Arguments

x

an object of type "lm".

alpha

numeric; transparency of points (0 to 1, default=0.4).

n.labels

integer; the number of points to label (default=2).

Value

a ggplot2 graph

Details

This function is a modification of the influencePlot function in the car package, using ggplot2 rather than base graphics.

Note

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. The n.label parameter controls the number of highest residuals, highest leverage points, and most influential points to label. For example n.label=2, the default, will identify the two points meeting each criterion, for a maximum of 6 labeled points. Points meeting more than one criterion are only labeled once.

Color is used to identify points that are not influential (D < 0.5), possibly influential (0.5 <= D < 1), and likely to be influential (D >= 1).

See also

influencePlot

Examples

mtcars$am <- factor(mtcars$am)
fit <- lm(mpg ~ wt + am + disp + hp, mtcars)
influence_plot(fit)