----- Forwarded message from Paolo Piras <paolo.pi...@uniroma3.it> -----
Date: Fri, 14 Jun 2013 19:56:37 -0400
From: Paolo Piras <paolo.pi...@uniroma3.it>
Reply-To: Paolo Piras <paolo.pi...@uniroma3.it>
Subject: RE: Pooled within-group covariance matrix
To: "morphmet@morphometrics.org" <morphmet@morphometrics.org>
Carlo,
how did you perform your analyses?
in R?
Maybe you want to take a look at groupPCA() function in Morpho package
best
paolo
________________________________________
Da: morphmet_modera...@morphometrics.org [morphmet_modera...@morphometrics.org]
Inviato: martedì 11 giugno 2013 11.36
A: morphmet@morphometrics.org
Oggetto: Pooled within-group covariance matrix
----- Forwarded message from carlo.mel...@unina.it -----
Date: Mon, 10 Jun 2013 10:59:47 -0400
From: carlo.mel...@unina.it
Reply-To: carlo.mel...@unina.it
Subject: Pooled within-group covariance matrix
To: morphmet@morphometrics.org
Dear all,
I am performing Partial Least Square analyses to check the association
between skull shape and climatic variables in a sample of 15 species
from two different genera of monkeys.
The association occurs and is significant.
I tried the same analysis but using the pooled within group covariance matrix.
The association does not occur and is not significant.
Can anyone explain me what the "pooled within group covariance matrix"
analysis really perform and if it generate a distortion to the data
(like CVA does)?
I tried comparing normal PCA and the one using "pooled within group
covariance" and what I obtained was a more squeezed distribution so
that my genera looked much closer in PC plots than they really
are...it seems to me that this create distortion of original data and
goes far away from biological interpretation.
All comments and replies about this are welcome,
Thank you in advance
Carlo
----- End forwarded message -----
----- End forwarded message -----
