I gave you the wrong answer. I thought you were trying to predict votes
with some independent variables and you were using PCA
scores of these independent variables as the predictors.
It sounds like you have classified the subject of the vote, somehow. I
don't have an answer for this.
Sorry for the confusion.
David J Svendsgaard, PhD
Biostatistician
EPA/ORD/NCEA/RTP, Mail Drop B-243-01
Research Triangle Park, NC 27711
Phone (919) 541-4186
Fax (919) 541-1818
From: Nick Eubank <nickeubank@gmail.com>
To: statalist@hsphsun2.harvard.edu
Date: 07/03/2011 12:17 AM
Subject: st: PCA with Missing Values (or other factor analysis)
Sent by: owner-statalist@hsphsun2.harvard.edu
Hello All,
I'm trying to do a PCA on legislator votes (1 or 0), where different
legislators have overlapping but distinct terms. In other words,
legislator A is in office periods 1 and 2, legislator B is in office 2
and 3, so A has no votes in period 3 and B has no votes in period A. I
want to map their "ideal points" (i.e. pca predicted value from votes)
into the same space. But PCA obviously dislikes missing values.
Suggestion? I'm really open to any factor analysis algorithm.
Thanks!
Nick
--
PhD Student, Political Economics
Stanford University GSB
nickeubank@gmail.com
Cell: +1 (303) 918.6528
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