I'm using a tobit model for a left censored dataset in R, and including both continuous and categorical predictor variables. I've converted the factors to indicator coding for each level. I was initially getting errors when trying to run a summary of the model output, which I discovered was due to the fact that some levels of the categorical variables have a coefficient of 0, even though there are observations for those levels. I'm setting a reference level for the category using the level with the most observations.
I can't seem to find anything about why this would be happening. This isn't survival regression where 0 coefficient is a soft model selection, is it?
If it is a form of model selection, how do I handle that given the factor itself? I know in ridge regression I could use a form of grouping to test the variable overall. Could I use a similar approach here?