I would like to fit some models on a dataset where I have a lot of missing values. I am especially interested in comparing models fit with and without imputed values, because the dataset has so many missing values (>50%) that it seems unlikely to me that any imputation procedure is going to yield sensible results.
I’m looking for a software package, ideally in R/Python, that could do all three of the following:
- penalized regression (l1/l2/ElasticNet loss);
- multinomial, linear, and logistic models;
- handles missing values
In base R I can fit simple models using
na.exclude. But neither
glmnet can handle missing values. I know some methods, especially nearest-neighbor or decision tree-based methods, can naturally handle missing values, e.g. by adding another type of split or by omitting missing values in pairwise comparisons. Is there a package that can do all three of the above or is it just not possible?