I’m going to link you to this explanation by @himel.mallick (one of the original authors).
In essence during model fitting the models will not use any data that is missing values in the covariates. If you want to avoid this you could try imputing them in sensible manner or modeling the lack of data directly (although this could be messy depending on the data etc.).