In my understanding to PCA, its main goal is to reduce the dimensionality of
a problem without the loss of too much information. In other words,
according to Prof. Rohlf, the purpose of PCA is to give you a low
dimensional space that accounts for as much variation as possible. However,
I agree
Don't know what happened to cause the earlier message largely void of content, but I
think the original communication was to correct the Red Book reference.
The date is 1985, not 1982. -ds
On Tue, 2004-05-18 at 14:12, [EMAIL PROTECTED] wrote:
--
Dennis E. Slice, Ph.D.
Department of
Dear collegues,
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About the above discussion on the linear measurements data for multivariate
analysis, I should state that most times my problem (and I expect the problem
of many people that wrks with it) is not of
I applaud your courage, Dr. Hammer. I hope everyone appreciates how intimidating this
list of experts can be.
I also agree with your point that PCA can be used when the data are not multivariate
normal if you are just using it to visualize information, or if you just know what it
is doing
Dr. Hammer, Please consider your courage credited. -ds
A couple of points about PCA in general:
1) PCA makes no assumptions about the distribution (multivariate normal
or otherwise) of your data. It is a procedure that simply produces the
linear combinations of variables with maximum variance
Dear Colleagues,
Nicholas Jones and I are pleased to announce that morphologika, which
is a Windows based program for 3d geometric morphometrics is available
at:
http://www.york.ac.uk/res/fme/index.htm
It can be downloaded from the resources page.
We ask that you complete details of your
