----- Forwarded message from morphmet_modera...@morphometrics.org -----
Date: Wed, 01 Aug 2012 16:57:47 -0700
From: morphmet_modera...@morphometrics.org
Reply-To: morphmet_modera...@morphometrics.org
Subject: PLS and hierarchical partition
To: morphmet@morphometrics.org
----- Forwarded message from Rodrigo Lima -----
Date: Tue, 24 Jul 2012 13:41:22 -0400
From: Rodrigo Lima
Reply-To: Rodrigo Lima
Subject: PLS and hierarchical partition
To: "morphmet@morphometrics.org"
Hello morphometricians,
I'm trying to understand my stats, and any input would be mostly appreciated.
The situation: I did a two-block PLS and a hierarchical partition analysis
using my shape variables and 12 environmental variables. The first latent
variable of PLS explains 96% of the covariation between blocks.
The problem: the most important environmental variables on PLS (loadings on
LV1) are different from the most important variables on the hierarchical
partition analysis. Although the calculation is different (hierarchical
partition uses all regressions possible between shape and environmental
variables while PLS extracts eigenvalues that explain most of the covariation
between shape and the environmental variables), since LV1 represents 96% of the
variation the most important variables should be the same, right? Am I missing
something here?
Thank you very much,
Rodrigo
----- End forwarded message -----
----- End forwarded message -----
