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Re: st: Conditional expectations for each latent class with gllapred


From   Kristian Karlson <kristian.karlson@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Conditional expectations for each latent class with gllapred
Date   Thu, 30 Dec 2010 17:47:01 +0100

Stas,

Thanks. My bad with the datafile. It is

webuse union, clear
sample 25
gllamm union age grade year, i(idcode) ip(f) nip(2) l(logit) f(binom)

After quite some googling, I learned that -gllapred- option -us()- may be the right thing for me. I came to that conclusion by reading http://www.stata.com/meeting/2nasug/lclass.pdf, not the helpfile (which I find a bit difficult to understand, at least the -us()- option). I will also look into your suggestion about manipulating -gllapred- using option -from()-.

Thanks.

Kristian



Den 30-12-2010 16:37, Stas Kolenikov skrev:
On Thu, Dec 30, 2010 at 3:06 AM, Kristian Karlson
<kristian.karlson@gmail.com>  wrote:
I have run the following -gllamm- model in Stata. It is a finite mixture
binary logit model with two latent classes:

use http://www.ats.ucla.edu/stat/paperexamples/singer/hsb12.dta, clear
sample 25
gllamm union age grade year, i(idcode) ip(f) nip(2) l(logit) f(binom)

I am interested in the conditional expectation for latent class: Pr(Union =
1 | u_1) and Pr(Union = 1 | u_2), where u_1 is latent class 1 and u_2 is
latent class 2.

I have looked at -gllapred-, but haven't been able to compute these. My idea
was to use options mu and marg, but these probabilities are the mixed
probabilities, not the ones from each component.

Your example does not run:

. gllamm union age grade year, i(idcode) ip(f) nip(2) l(logit) f(binom)
variable union not found
r(111);

You probably meant a different data set.

Note that this is a pretty restrictive model in which the effects of
the predictors are kept constant across classes, and the difference is
only via a shift.

You might be able to manipulate -gllapred- using -from()- option. For
that, you can create two matrices with the estimated point masses and
their weights fixed to one and the other class. Just a suggestion, I
never worked with mixture models using -gllamm-.

Your other option is to use -fmm- package that might provide
additional flexibility if it supports -logit- link (or an analogue
of).

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