Re: [R] factor analysis

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From: Pierre BADY (Pierre.Bady@univ-lyon1.fr)
Date: Fri 14 May 2004 - 04:50:12 EST


Message-id: <1084474212.40a3c36483c65@webmail.univ-lyon1.fr>

hi all,

In the library ‘ade4’, there are two eigenanalysis which enable the ordination
of the categorical variables.

1- Multiple Correspondence Analysis (MCA, Tenenhaus & Young 1985) performs the
multiple correspondence analysis of a factor table (see the
function ‘dudi.acm’).

2- the “mixed factorial analysis” (Hill & Smith 1976) enables the ordination
of tables mixing quantitative variables and factors (functions ‘dudi.mix’
or ‘dudi.hillsmith’).

I hope this helps,

P.BADY

En réponse à John Fox <jfox@mcmaster.ca>:
> Dear Allan,
>
> I assume that the categorical data are ordinal. There are methods for
> factor analyzing ordinal data (e.g., using polychoric correlations)
> and
> mixed ordinal and interval data, but as far as I know, these aren't
> implemented in R.
>
> John
>
> On Thu, 13 May 2004 18:32:11 +0200
> allan clark <allan@stats.uct.ac.za> wrote:
> > hi all
> >
> > this is a stats question.
> >
> > when undertaking factor analysis should the variables included
> always
> > be
> > continiuos random variables? could one include categorical data? e.g
> > some survey type data
> >
> > my gut feeling is that one should not include categorical type data
> > since factor analysis is based on the eigenanalysis of the
> > correlation
> > matrix of a group of variables. The introduction of categorical data
> > into a data set might "mess up" the corelation matrix.
> >
> > ______________________________________________
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>
>
> --------------------------------
> John Fox
> Department of Sociology
> McMaster University
> Hamilton, Ontario, Canada
> http://socserv.mcmaster.ca/jfox/
>
> ______________________________________________
> R-help@stat.math.ethz.ch mailing list
> https://www.stat.math.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
> http://www.R-project.org/posting-guide.html
>
>

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Pierre BADY
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