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RE: st: GLLAMM: Predict Class Membership


From   Cameron McIntosh <cnm100@hotmail.com>
To   STATA LIST <statalist@hsphsun2.harvard.edu>
Subject   RE: st: GLLAMM: Predict Class Membership
Date   Wed, 19 Sep 2012 08:05:10 -0400

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. 
http://www.statmodel.com/download/relatinglca.pdf

Cam

> Date: Wed, 19 Sep 2012 12:34:42 +0200
> From: m.spitzenpfeil@gmx.net
> To: statalist@hsphsun2.harvard.edu
> Subject: st: GLLAMM: Predict Class Membership
> 
> Hi,
> 
> is there any way to predict individual latent class membership
> as a dummy variable - and not in terms of probability - using
> GLLAMM?
> 
> My modell is structurally equivalent to the Myocardial Infarction Example:
> http://www.gllamm.org/examples.html
> http://www.gllamm.org/data_dofile.zip
> 
> insheet using mi.dat, clear
> rename q y1
> rename h y2
> rename l y3
> rename c y4
> gen wt2 = count
> gen patt=_n
> 
> reshape long y, i(patt) j(var)
> tab var, gen(v)
> 
> eq v1: v1
> eq v2: v2
> eq v3: v3
> eq v4: v4
> 
> gllamm y, i(patt) ip(fn) nip(2) eqs(v1 v2 v3 v4) /*
> */ weight(wt) nrf(4) l(logit) f(binom) nocons
> 
> 
> 
> Thanks for your help!
> 
> Martin
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