----- Forwarded message from [email protected] ----- Date: Wed, 01 Aug 2012 16:57:47 -0700 From: [email protected] Reply-To: [email protected] Subject: PLS and hierarchical partition To: [email protected]
----- 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: "[email protected]" 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 -----
