[R-sig-eco] Subject: Re: missing data in PCA

[Nicholas Lewin-Koh]

Hi,
It's not quite so simple, at what stage do you recombine your samples.
Recombining the covariances into an "average" covariance from the
imputed samples has different implications than recombining the
projected data. I think that in terms of pca uncertainty the projection
directions is the issue. 

Nicholas


> Message: 1
> Date: Mon, 6 Apr 2009 12:21:51 -0400
> From: Farrar.David at epamail.epa.gov
> Subject: Re: [R-sig-eco] missing data in PCA
> To: St?phane Dray <dray at biomserv.univ-lyon1.fr>
> Cc: r-sig-ecology-bounces at r-project.org, Jason S <jas2339 at yahoo.com>,
> 	r-sig-ecology at r-project.org
> Message-ID:
> 	<OF3DFCFEFF.E4C894BF-ON85257590.00568507-85257590.0059E106 at epamail.epa.gov>
> 	
> Content-Type: text/plain
> 
> For combining imputation with PCA, transcan{Design} could be interest. 
> 
> For imputation, I have been hearing about the mice program. 
> 
> The documentation for princomp() suggests that a covariance matrix can be 
> entered via the covmat argument. 
> 
> David F
> 
> 
> r-sig-ecology-bounces at r-project.org wrote on 04/03/2009 10:34:51 AM:
> 
> > [image removed] 
> > 
> > Re: [R-sig-eco] missing data in PCA
> > 
> > Stéphane Dray 
> > 
> > to:
> > 
> > Jason S
> > 
> > 04/03/2009 10:48 AM
> > 
> > Sent by:
> > 
> > r-sig-ecology-bounces at r-project.org
> > 
> > Cc:
> > 
> > r-sig-ecology
> > 
> > ade4 has nipals() which deals with row data.
> > 
> > Cheers.
> > 
> > Jason S wrote:
> > > Dear Jari,
> > >
> > > Thanks for the hints. A package such as mvnmle would be ideal, but
> > it provides only the estimate of the covariance matrix, not the raw 
> > data. However, comments like princomp and pca require the raw data.
> > >
> > > Do you (or anyone else) know a command to do PCA straight from a 
> > covariance matrix?
> > >
> > > best,
> > >
> > > Jason
> > >
> > >
> > >
> > >
> > > ________________________________
> > > From: Jari Oksanen <jari.oksanen at oulu.fi>
> > >
> > > Cc: Jari Oksanen <jari.oksanen at oulu.fi>; r-sig-ecology at r-project.org
> > > Sent: Friday, April 3, 2009 2:55:20 AM
> > > Subject: Re: [R-sig-eco] missing data in PCA
> > >
> > >
> > > On 03/04/2009, at 00:32 AM, Jason S wrote:
> > >
> > > 
> > >> Dear all,
> > >>
> > >> I was wondering if you have good options to deal with missing 
> > data on a PCA in R. I guess I could simply delete those cases, but I
> > think there should be better options in terms of predicting them. Any 
> hints?
> > >>
> > >> 
> > > Howdy,
> > >
> > > There may be better ways, but they are not easy... Check paragraph
> > on missing data in multivariate task view. This lists several 
> > alternatives of multiple imputation data. About a year ago I tried 
> > some of them for multivariate analysis, but that was not quite 
> > straightforward. The problem was summarizing multivariate results. 
> > Things may have been changed since then, and there may be some 
> > canned routines.
> > >
> > > Here is one link to multivariate task view (but you can use a 
> > mirror close to you):
> > >
> > > http://cran.r-project.org/web/views/Multivariate.html
> > >
> > > One thing you should remember: do not replace missing values with 
> means.
> > >
> > > Cheers, Jari Oksanen
> > >
> > >
> > >
> > > 
> > >    [[alternative HTML version deleted]]
> > >
> > > _______________________________________________
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> > >
> > >
> > > 
> > 
> > -- 
> > Stéphane DRAY (dray at biomserv.univ-lyon1.fr )
> > Laboratoire BBE-CNRS-UMR-5558, Univ. C. Bernard - Lyon I
> > 43, Bd du 11 Novembre 1918, 69622 Villeurbanne Cedex, France
> > Tel: 33 4 72 43 27 57       Fax: 33 4 72 43 13 88
> > http://pbil.univ-lyon1.fr/members/dray/
> > 
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> 
> 	[[alternative HTML version deleted]]
> 
> 
> 
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