My question has to do with using the RSGHB package for predicting choice probabilities per alternative by applying mixed logit models (variation across respondents) with correlated coefficients.
I understand that the choice probabilities are simulated on an individual level and in order to get preference share an average of the individual shares would do. All the sources I have found treat each prediction as a separate simulation which makes the whole process cumbersome if many predictions are needed.
Since one can save the respondent specific coefficient draws wouldn't it be faster to simply apply the logit transform to each each (vector of) coefficient draw? Once this is done new or existing alternatives could be calculated faster than rerunning a whole simulation process for each required alternative. For the time being using a fitted() approach will not help me understand how prediction actually works.
