I am trying to use vif() from the car package to calculate VIF values after a regression based on this guide.
Without any categorical variables you get output that looks like this:
#code
model <- lm(mpg ~ disp + hp + wt + drat, data = mtcars)
vif_values <- vif(model)
vif_values
barplot(vif_values, main = "VIF Values", horiz = TRUE, col = "steelblue")
abline(v = 5, lwd = 3, lty = 2)
disp hp wt drat
8.209402 2.894373 5.096601 2.279547
However, the output changes if you add a categorical variable:
mtcars$cat <- sample(c("a", "b", "c"), size = nrow(mtcars), replace = TRUE)
model <- lm(mpg ~ disp + hp + wt + drat + cat, data = mtcars)
vif_values <- vif(model)
vif_values
GVIF Df GVIF^(1/(2*Df))
disp 8.462128 1 2.908974
hp 3.235798 1 1.798832
wt 5.462287 1 2.337154
drat 2.555776 1 1.598679
cat 1.321969 2 1.072273
Two questions: 1. How do I interpret this different output? Is the GVIF equivalent to the numbers output in the first version? 2. How do I make a nice bar chart with this the way the guide shows?