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Re: st: Class membership probabiliy and mlogit


From   Maarten buis <maartenbuis@yahoo.co.uk>
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
> 
> 
> 
> 
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> 


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

visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434

+31 20 5986715

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


		
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