Date: Thu, 23 Jun 2011 07:34:46 -0400
Reply-To: Art@DrKendall.org
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: Art Kendall <Art@DrKendall.org>
Organization: Social Research Consultants
Subject: Re: PCA: R-Matrix Determinant =0 and "not positive
Definite"
In-Reply-To: <1308822373944-4516900.post@n5.nabble.com>
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<font size="+1">When you use factor analysis</font> (PCA is one kind
of factor analysis) you find a small number dimensions that
"account for" much of the variance in the original data. You can
think of each new dimension (factor) as pulling together a number of
imperfect variables into an internally consistent measure of a new
construct (idea). The original variables are redundant measures of
the new construct. If you use varimax rotation, you have a new set
of measure that cover pretty much the same hyperspace but that are
independent of each other which is what is desirable for clustering.<br>
<br>
The size of the determinant is not extremely important as long as it
is not zero. The small determinant means it will take more
iterations to come up with a solution but that should not be a
problem with today's computer.<br>
<br>
WRT the significance level, it is not important. <br>
<br>
Why is your data missing? Is there anything meaningful about what is
missing?<br>
<br>
Does your version of SPSS have the RMV -replace missing values-
procedure?<br>
<br>
I do not understand your question about accuracy.<br>
<br>
Members of this list will find it difficult to contribute to this
conversation without a more detailed understanding of what you are
trying to do and what your data are.<br>
What constitutes a case? What do the variables mean?<br>
<br>
Art Kendall<br>
Social research Consultants<br>
<br>
On 6/23/2011 5:46 AM, mzalikhan wrote:
<blockquote cite="mid:1308822373944-4516900.post@n5.nabble.com"
type="cite">
<pre wrap="">Thanks Art.
Yes i had some variables that were highly correlated (>0.9) with one
another. Excluding those variables solves the ""not positive Definite"
issue. However still the R-matrix determinant is very low (E-10). In the
literature i have read that it should not be less than 0.00001.
Is it necessary that the significance level (1-tailed) should be greater
than 0.001 for R matrix?
I am doing PCA followed by a 2 step clustering analysis on met data to find
out synoptic met patterns. My number of variables is around 30 (which
perhaps i have to reduce to around 18), number of observations is 1330 but
the results show VALID N= 890.
What would be the effect if i replace the missing values by means rather
than excluding cases list wise?
Any idea what should i be looking for in the results for accuracy?
I would also be thankful if you can guide me after i get PCA results and go
for clustering.
Peace.
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