influence_plot: Regression Influence Plot

In Rkabacoff/qacReg: Multiple Regression and Logistic Regression

Regression Influence Plot

Description

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

Usage

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).

Details

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

Value

a ggplot2 graph

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)

Rkabacoff/qacReg documentation built on Aug. 1, 2022, 11:11 p.m.