
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@oulu.fi>

Cc: Jari Oksanen <jari.oksanen@oulu.fi>; r-sig-ecology@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



      
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