Hello, 

I am pretty new to mlogit, and still trying to figure out what models to use.I have a data set of N individuals, each of which faces I alternatives. The utility function of individual n, for choice i is: 

u(i,n) = alpha(i) * x1(i,n) + beta * x2(i,n) 

where alpha(i) is the individual specific parameter, and beta is shared among all individuals. I don't really know how to set this up in mlogit. 

If I assumed that beta is individual-specific (beta(i)), then I can divide the data set to many subsets, each of which corresponds to a particular individual i, and run this model for each subset to estimate alpha(i) and beta(i). 
y ~ x1 + x2 
This can be done just fine. 

I have gone over tutorials by Train and by Heshner but I haven't found out how to solve this problem yet. Any suggestions are welcome. Thank you so much for your time!

	[[alternative HTML version deleted]]

