I have a multinomial logit model with 13 IVs and a dependent with three levels (`cash`, `stock`, `mix`). `cash` is the base dependent variable. As far as I know, we interpret results of a multinomial logit always as a comparison to the base dependent variable - so if I have as a base dependent `cash`, I interpret the coefficients in comparison to this base. This means that the result of a multinomial logit gives me two sets of coefficients - one for `stock` and one for `mix` - as shown below on the output of multinom function of `nnet` package in R. [![multinom output][1]][1] Here, we get two sets of coefficients - one for `stock`, one for `mix` - and we interpret the coefficients as changes in the probability of `stock` or `mix` occuring vs. `cash` occuring (loosely put). Nevertheless, the output of bayesian model averaging of multinomial logit, which is done with the `mlogitBMA` package in R, is following: [![BMA of multinomial logit][2]][2] In this output, there is only one set of coefficients - the column EV. So how do we interpret this output? Am I missing something? Thank you! I can provide you with my code and data if necessary. [1]: https://i.sstatic.net/B9f4m.png [2]: https://i.sstatic.net/8gCxt.png