Re: st: GLLAMM: Predict Class Membership

[m.spitzenpfeil@gmx.net]

I've already assigned the individuals according to the conditional 
(conditional on the observed variables)
probabilities of belonging to the class (class 1 instead of 0).

Since there are only two classes i expected the mean of the class variable
to equal the prior probability of belonging to class 1.
That's not the case, though.

Thanks for the references!

Martin

Am 19.09.2012 14:05, schrieb Cameron McIntosh:
> 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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