Message-ID: <x2wux6hkat.fsf@blueberry.kubism.ku.dk>
Date: 2002-02-21T16:27:38Z
From: Peter Dalgaard
Subject: Factor analysis of categorical or mixed categorical/continuousdata in [R]
In-Reply-To: <5.1.0.14.2.20020221110115.01da4670@mcmail.cis.mcmaster.ca>
John Fox <jfox at mcmaster.ca> writes:
> At 11:18 AM 2/21/2002 +0000, Dr Stuart Leask wrote:
> >I am looking to fit one or more latent categorical variables to data that is
> >a mixture of categorical and continuous variables. Factor analysis would
> >work for continuous data, latent class analysis for categorical data. I
> >understand that in a package such as MPlus I could perform a single analysis
> >of both data types. Are there similar routines available in R?
>
> Dear Stuart,
>
> If memory serves me, a common approach is to use tetrachoric
> correlations (for dichotomous data), polychoric correlations (for
> ordered-category data), and point-biserial and polyserial correlations
> (for mixed data). If you want to do inference, then this approach gets
> complicated (requiring asymptotic sampling covariances for the
> correlations), but for a descriptive factor analysis, it should be
> reasonably straightforward.
>
> I'm not aware of any facility for calculating these kinds of
> correlations in R, but programming them shouldn't be too hard. I may
> add this at some point to the sem package.
On the face of it (which is as far as I am able to see), it would seem
fairly easy to set up an MLE procedure if you treat all discrete
variables as obtained by setting cutpoints on continuous latent
variables. I suspect this is what MPlus is doing. The requisite normal
integrals should be available through library(mvtnorm).
--
O__ ---- Peter Dalgaard Blegdamsvej 3
c/ /'_ --- Dept. of Biostatistics 2200 Cph. N
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907
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