Re: st: Principal Components Analysis with count data

[Evans Jadotte <evans.jadotte@uab.es>]

Cameron McIntosh wrote:
> Hi Jason, Adrian
>
> You should also check out chapters 8 and 9 of:
>
> Basilevsky, A. (1994). Statistical Factor Analysis and Related Methods: Theory and Applications. New York: Wiley. 
>
> Cam
>
>
> ----------------------------------------
>   
> > From: kokootchke@hotmail.com
> > To: statalist@hsphsun2.harvard.edu
> > Subject: RE: st: Principal Components Analysis with count data
> > Date: Mon, 10 Aug 2009 20:12:39 -0400
> >
> >
> >
> > Jason wrote:
> >     
> > > As PCA is appropriate for continuous data. I am wondering if it is
> > > appropriate for count data (i.e., highly skewed)? Can someone provide
> > > advice, guidance or a resource in using PCA with count data?
> > >       
> > Dear Jason,
> >
> > I don't know much about this but a while ago I was looking for something similar and I came across this paper which helped me:
> >
> > http://cosco.hiit.fi/search/MPCA/buntineDPCA.pdf
> >
> > If that's not useful to you, it has a bunch of references in the back. Maybe those can help.
> >
> > Best,
> > Adrian
> >
> >
> >
> >
> >     
> > > Cheers,
> > > Jason
> > >
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Hello Jason,

I think a straightforward way to deal with this issue is to apply a 
Multiple Correspondence Analysis (MCA) to your data. See Asselin (2002) 
for an application, and also reference therein.

Evans
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