Posted by
Art Kendall on
Jun 22, 2011; 11:48am
URL: http://spssx-discussion.165.s1.nabble.com/PCA-R-Matrix-Determinant-0-and-not-positive-Definite-tp4512844p4513768.html
Questions to get at extreme examples of one or more variables being
perfect linear predictors of (an)other variable(s).
do you have more variables than cases? Either before
you start or after removing cases with missing data?
Do you have a variable in the specification more than once?
something like this?
variables = a b c a d e
Do you have sets of dummy variables that represent all categories
of a nominal level variable?
Do you have variables that add up to another variable, such as
daily and weekly rainfall?
Is your correlation matrix small enough to check by eye? if so,
do you have correlation near +/- 1?
Please describe your data in more detail.
What constitutes a case (row)? How many to you have before
accounting for missing data? How many have complete data?
What are your variables? How many are there? Are they standardized?
If you have a large correlation matrix, the RELIABILITY procedure
produces statistics on the distribution of correlation coefficients.
Pretend that you variables that are items in a scale. In this
context item means variable. Look at the item-total correlations
for high absolute values. Look at the alpha-if-item-deleted to see
if some item(s) is/are redundant.
Art Kendall
Social Research Consultants
On 6/22/2011 12:00 AM, mzalikhan wrote:
Hi Everybody
I am new to stats and doing PCA using SPSS 16.0, dealing with some
meteorological variables to do synoptic met patterns. The problem is that
the correlation matrix is giving 0 determinant with a warning of "not
positive definite matrix".
My question is "is that going to affect the results of the PCA?" as the
analysis does not quit after this warning, rather it goes on. If yes, what
can be done in this case?
Peace.
Muhammad Zeeshan
--
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Art Kendall
Social Research Consultants