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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:enter image description here

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:

enter image description here

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.

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  • $\begingroup$ Is lot_dummy a categorical variable? If so, is that how it is defined in R? $\endgroup$
    – André.B
    Nov 21, 2019 at 21:58
  • $\begingroup$ It's a boolean and I define it as a logical using as.logical() $\endgroup$
    – ctd25
    Nov 21, 2019 at 22:02
  • $\begingroup$ I think this might hold some answers: stats.stackexchange.com/questions/76488/… $\endgroup$
    – André.B
    Nov 24, 2019 at 21:32

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