Posted by
Art Kendall on
Jun 23, 2011; 11:34am
URL: http://spssx-discussion.165.s1.nabble.com/PCA-R-Matrix-Determinant-0-and-not-positive-Definite-tp4512844p4517146.html
When you use factor analysis (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.
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.
WRT the significance level, it is not important.
Why is your data missing? Is there anything meaningful about what is
missing?
Does your version of SPSS have the RMV -replace missing values-
procedure?
I do not understand your question about accuracy.
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.
What constitutes a case? What do the variables mean?
Art Kendall
Social research Consultants
On 6/23/2011 5:46 AM, mzalikhan wrote:
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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Art Kendall
Social Research Consultants