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Re: st: PCA with unbalanced data

From   Nick Cox <>
Subject   Re: st: PCA with unbalanced data
Date   Wed, 6 Apr 2011 09:11:30 +0100

Well, perhaps it appears that  PCA "kind of assumes" that, but using
this procedure certainly does. Need to check either way.

On Wed, Apr 6, 2011 at 3:01 AM, Stas Kolenikov <> wrote:
> On Tue, Apr 5, 2011 at 3:05 PM, PINAR ERDEM <> wrote:
>> I want to use PCA (principal componets analysis) with a dataset of 49 variables. However my data is unbalanced (unequal number of observations). My questions are if it is possible to run PCA with unbalanced data and how to get longest possible components/factors? Any suggestions would be very much appreciated.
> I think this is a perfect case for -mi impute mvn-. PCA kind of
> assumes normal data, so -mi- will come up with an appropriate
> estimator of the covariance matrix of the data; with some luck, you
> might even be able to pull it out of the guts of -mi- and analyze as
> is. If not, you can impute a few dozen times... and then get stuck, as
> -mi estimate- does not support -pca-.

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