I'm looking to compare linear regression models from different data subsets in r. The models are not nested. I have a model from the complete dataset with sex as a factor and then 2 separate models from 2 subsets of this data (subset1 = all male data, subset2 = all female data). I am trying to see if it is better to have one model with sex as a factor or if there is benefit having a separate model for each gender. I am looking at the MAE (mean absolute error) and RSME (root mean square error) specifically.
Can I compare the raw values or the MAE and RSME (e.g. MAE from the complete model on the complete set vs female on the female set and male on the male set)?