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re: RE: st: GLLAMM: Predict Class Membership

From   "Ariel Linden, DrPH" <>
To   <>
Subject   re: RE: st: GLLAMM: Predict Class Membership
Date   Thu, 20 Sep 2012 11:21:14 -0400


Have you considered using a finite mixture modeling approach? This creates a
latent variable and provides the posterior probabilities you require.

See the user written program -fmm- by  Partha Deb (ssc install fmm), and its
post estimation commands that provide the posterior probabilities...


Date: Wed, 19 Sep 2012 13:09:27 +0200
From: Maarten Buis <>
Subject: Re: st: GLLAMM: Predict Class Membership

On Wed, Sep 19, 2012 at 12:34 PM,  <> wrote:
> is there any way to predict individual latent class membership
> as a dummy variable - and not in terms of probability - using

You can always assign each individual to class on which it has the
highest probability. However, that does not fit well with the logic
behind these models: these models accept that we do not know which
person belongs to which class and are all about modeling the
probabilities instead. What might make more sense is to make multiple
datasets and in each of these datasets assign each individual member
at random to a class based on the predicted probabilities.

Hope this helps,

- ---------------------------------
Maarten L. Buis
Reichpietschufer 50
10785 Berlin
- ---------------------------------
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Date: Wed, 19 Sep 2012 08:05:10 -0400
From: Cameron McIntosh <>
Subject: RE: st: GLLAMM: Predict Class Membership

You will often see people assigning cases to classes based on posterior
probabilities (usually to run separate logistic regressions of classes on
covariates), but this removes uncertainty in class membership and thus
biases standard errors of coefficients downward.  So optimally you would use
a concomitant-variable LCA, or improved multi-step approaches:

Vermunt, J.K. (2010). Latent Class Modeling with Covariates: Two Improved
Three-Step Approaches. Political Analysis, 18(4), 450-469.

Clark, S. & Muthén, B. (submitted). Relating latent class analysis results
to variables not included in the analysis.


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