Apparently the car package vif function and performance package check_collinearity function both calculate generalized variance inflation factor (GVIF) automatically if a categorical variable is entered into a regression. As an example, this can be done with the iris dataset with the following code:
#### Categorical Fit ####
fit.cat <- lm(
Petal.Length ~ Species + Petal.Width + Sepal.Width,
iris
)
check_collinearity(fit.cat)
Which gives an an expected value of 26.10 that I have already hand calculated. However, Jamovi doesn't allow one to automatically add factors to a regression, so I dummy coded the same regression factor and entered the regression like so:
You can see in the arrow that the value doesn't match that obtained from the R function. I also double checked in R to see if it is just calculating VIF instead:
1/(1-summary(lm(as.numeric(Species) ~ 1 + Petal.Width + Sepal.Width,
iris))$r.squared)
But the values don't match, as this gives me a VIF of 12.78. Why is it doing this and is there a solution in Jamovi for hacking this?
