estimating mixed logit model using maximum simulated likelihood: Matrix for starting values

[Francesco Chirico]

I am using the code from Hole's paper enclosed (estimating mixed logit model using maximum simulated likelihood) to adopt the method from Haan and Uhlendorff (2006) about mlogit with unobserved hetereogeneity. However, unfortunately and sorry for my ignorance I am not able to understand how Hole derives the following formula for the matrix (page 11 and 12) following from the coefficients got after estimating a model with random but uncorrelated intercepts (page 11 and 12):
Matrix b=e(b)
Matrix b= b[1,1..5], 0, b[1,6]

How does she derive these values for the matrix? Sorry but this is very unclear to me, and I know this derives from my ignorance. Im using a different dataset, and I guess that if I understand how she derives these values in the example in her article, I will be able to understand how to adapt it in my article. In my article I get for instance this but I dont understand the meaning:
initial vector: matrix must be dimension 12
r(503);

Many many many thanks
Francesco

[Francesco Chirico]

I solved the problem: it is just needed to not indicate the command "from" and the matrix is generated automatically, thanks

[jamal mamkhezri]

Those are not values, but the coefficient matrix of his previous model. Your model has more than two random variables, thus "Matrix b= b[1,1..5], 0, b[1,6]" will not work. I you have 3 random variables try: Matrix b= b[1,1..5],0,0, b[1,6], 0, b[1,7].

[Julia Jones]

Hi Statalisters,

I am curious how to code the b matrix Francesco refers to, which is published in Hole's manual entry (Francesco links above). Jamal, how did you know how to use Matrix b= b[1,1..5],0,0, b[1,6], 0, b[1,7] if you have three random variables? What is the syntax here?

Best,

Julia