Thanks everybody.
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call output for a range a latent classes, and
evaluate solutions. The commands are very simple.
David Joubert
Date: Tue, 24 May 2011 12:30:01 +0100
From: chr...@stats.ucl.ac.uk
To: dan...@umd.edu
CC: r-help@r-project.org
Subject: Re: [R] Latent class analysis, selection of the number of class
. The commands are very simple.
David Joubert
> Date: Tue, 24 May 2011 12:30:01 +0100
> From: chr...@stats.ucl.ac.uk
> To: dan...@umd.edu
> CC: r-help@r-project.org
> Subject: Re: [R] Latent class analysis, selection of the number of classes
>
> Dear Daniel,
>
> the
Dear Daniel,
the BIC can be used to estimate the number of classes. This is actually
given out by lca, so you could run lca with several different k and pick
the solution that gives you the best BIC.
Unfortunately I can't tell you whether "large is good" or "small is good"
for the BIC implemen
function ?stepFlexmix in the flexmix package may be what you're looking for
hth, Ingmar
On Mon, May 23, 2011 at 11:21 PM, Daniel Malter wrote:
> Hi,
>
> I perform latent class analysis on a matrix of dichotomous variables to
> create an indicator of class/category membership for each observation
Hi,
I perform latent class analysis on a matrix of dichotomous variables to
create an indicator of class/category membership for each observation. I
would like to know whether there is a function that selects the best fit in
terms of number of classes/categories.
Currently, I am doing this with t
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