In testing the parallel regression assumption in ordinal logistic regression I find there are several approaches. I’ve used both the graphical approach (as detailed in Harrell´s book) and the approach detailed using the ordinal package in R.
However I would also like to run the Brant test (from Stata) for both the individual variables and also for the total model. I’ve looked around but cannot find it implemented in R.
Is there an implementation of the Brant test in R?
I implemented the brant test in R. The package and function is called brant and it’s now available on CRAN.
The brant test was defined by Rollin Brant to test the parallel regression assumption (Brant, R. (1990) Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics, 46, 1171–1178).
Here is a code example:
data = MASS::survey data$Smoke = ordered(MASS::survey$Smoke, levels=c("Never","Occas","Regul","Heavy")) model1 = MASS::polr(Smoke ~ Sex + Height, data=data, Hess=TRUE) brant(model1)
In the example, the parallel regression assumption holds, because all p-values are above 0.05. The Omnibus is for the whole model, the rest for the indvidual coefficents.