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Re: st: Class membership probabiliy and mlogit
Dear Jon, Jonathan and others,
Bolck et al. 2004. describe one way to deal with this situation [in
it's known as a variant of latent structure model, MIMIC model etc.]
What you plan to do is what they call a three-step approach as opposed
to a one
step approach of setting up the full likelihood of the measurement part
class] and the structural part [multinomial logit].
Here's the reference:
Bolck A, Croon M, Hagenaars J.
Estimating Latent Structure Models with Categorical Variables: One-Step Versus
Three-Step Estimators, POLITICAL ANALYSIS, 12 (1): 3-27, Winter 2004
I tend to use the full maximum likelihood in another software and so
any experience in constructing the matrix they suggest to correct the bias
inthe three step approach.
Date: Mon, 14 May 2007 09:13:56 +0100
From: "Jon Heron (ALSPAC)" <Jon.Heron@bristol.ac.uk>
Subject: Re: st: Class membership probabiliy and mlogit
unfortunately, our data don't appear suitable for this model
- all but 3% of the cases have at least one probability which is
equal to zero
In particular, for the commonest response 'patterns' YYYYYYY and
NNNNNNN, we find ourselves with one probability practically equal
to one and very little else. Only when we have a great deal of
uncertainly, e.g. for NYNNYNY will we get six non-zero class assignment
Dr Jon Heron
Statistics Team Leader
ALSPAC, Dept of Social Medicine
24 Tyndall Avenue
Bristol BS8 1TQ
Tel: 0117 3311616
Fax: 0117 3311704
- --On 11 May 2007 20:24 +0100 Maarten buis <email@example.com> wrote:
I have another suggestion. You could use the probabilities as the
dependent variable by estimating a -dirifit- model. See:
Hope this helps,
--- Jonathan Sterne <Jonathan.Sterne@bristol.ac.uk> wrote:
We have been fitting latent class models, the output of which is a
posterior probabilities that each subject falls into one of six
classes. We now want to use multinomial logistic regression (mlogit)
examine predictors of class membership.
One option is to assign each subject to her/his modal class (the
which there is the highest probability of membership. However loses
information (some subjects will have a high probability that they
a particular class, others will have relatively similar probabilities
membership of two or more classes.
As an alternative, we wish to fit multinomial logistic regression
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
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
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
(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?
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