# Re: st: Class membership probabiliy and mlogit

 From Maarten buis To statalist@hsphsun2.harvard.edu Subject Re: st: Class membership probabiliy and mlogit Date Fri, 11 May 2007 20:24:06 +0100 (BST)

```I have another suggestion. You could use the probabilities as the
dependent variable by estimating a -dirifit- model. See:
http://home.fsw.vu.nl/m.buis/software/dirifit.html

Hope this helps,
Maarten

--- Jonathan Sterne <Jonathan.Sterne@bristol.ac.uk> wrote:

> Dear statalisters
>
> We have been fitting latent class models, the output of which is a
> set of
> posterior probabilities that each subject falls into one of six
> latent
> classes. We now want to use multinomial logistic regression (mlogit)
> to
> examine predictors of class membership.
>
> One option is to assign each subject to her/his modal class (the
> class for
> which there is the highest probability of membership. However loses
> information (some subjects will have a high probability that they
> belong to
> a particular class, others will have relatively similar probabilities
> of
> membership of two or more classes.
>
> As an alternative, we wish to fit multinomial logistic regression
> models
> using the class variable as the multinomial outcome and weighting the
>
> analysis using class membership probabilities.
>
> We have stacked the data so we have multiple rows for each subject in
> the
> following form
>
> 	ID     Exposure     Class     Prob
>         1      1            1         0.1
>         1      1            2         0.1
>         1      1            3         0.4
>         1      1            4         0.3
>         1      1            5         0.05
>         1      1            6         0.05
>
> 'Prob' sums to one within subject and class repeats 1,2,3,4,5,6
> through the
> whole dataset.
>
> We weight using pweights [pw = prob]
>
> Consequently, our model of choice has been:
>
> xi: mlogit class xvars [pw = prob], rrr
> (identical to xi: mlogit class xvars [iw = prob], rrr robust)
>
> and we have also experimented with
>
> xi: mlogit class xvars [pw = prob], rrr robust cluster(id)
>
> which gives lower SE's, and
>
> xi: mlogit class exposure [iweight = prob], rrr
>
> which gives *higher* SE's than the pweight model without 'robust'
>
> We would be grateful for advice on the following questions:
>
> 1. Is it appropriate to weight according to class membership
> probability
> (we are pretty convinced that it is)?
>
> 2. Does anyone have a recommendation as to which of the above model
> formulations gives theoretically appropriate standard errors?
>
> Many thanks
>
> Jonathan Sterne
>
>
>
>
> *
> *   For searches and help try:
> *   http://www.stata.com/support/faqs/res/findit.html
> *   http://www.stata.com/support/statalist/faq
> *   http://www.ats.ucla.edu/stat/stata/
>

-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands

Buitenveldertselaan 3 (Metropolitan), room Z434

+31 20 5986715

http://home.fsw.vu.nl/m.buis/
-----------------------------------------

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```