I'm running a choice model comparing how users choose between multiple options in an app.
I have the data formatted for use with MLogit:
Price and dist_rank_cat are features that vary by alternative and by choice_event. When I run a model using just these features, the model converges and I get coefficients. The code to run the model is:
choice_model <- mlogit(choice ~ price + dist_rank_cat,
data = choice_df,
chid.var = 'api_event_id',
alt.var = 'location_id',
choice = 'choice',
shape = "long",
reflevel = '3834',
print.level = 3)
Lot_dummy is a feature that varies across alternative, but does not change for each choice event (like a static product attribute).
When I add it to the model, it fails to converge and I get an error:
I don't completely understand how Mlogit wants you to specify different types of features and assume I am messing something up in the model definition that is causing this error.
