On Wed, 22 Jun 2011, Quang Anh Duong wrote:
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.?
I guess you mean that alpha(i) is the alternative-specific coefficient of
the individual-specific regressor x1(n)? And x2(i,n) is an
alternative-specific regressor with coefficient beta. If so, the model is
y ~ x2 | x1. (Possibly, you may want to exclude the alternative-specific
intercepts.)
But see the extensive package vignettes for more details:
vignette("mlogit", package = "mlogit")
vignette("Exercises", package = "mlogit")
hth,
Z
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!
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