----- Forwarded message from "F. James Rohlf" -----

Date: Thu, 27 Sep 2012 17:59:01 -0400
From: "F. James Rohlf"
Reply-To: [email protected]
Subject: RE: PCA of landmark data on wings
To: [email protected]

That is not surprising as the sign to attach to a PCA or CVA axis is arbitrary. Often software reflects axes so that most of the loadings are positive but that is again arbitrary. If you are running separate analyses but want the directions of the axes to agree then you can simply reflect them so they match.

 

----------------------

F. James Rohlf, John S. Toll Professor, Stony Brook University

The much revised 4th editions of Biometry and Statistical Tables are now available:

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P Please consider the environment before printing this email

 

 

From: [email protected] [mailto:[email protected]]
Sent: Wednesday, September 26, 2012 4:49 PM
To: [email protected]
Subject: PCA of landmark data on wings

 


----- Forwarded message from Jason Mottern -----

Date: Wed, 26 Sep 2012 12:50:47 -0400
From: Jason Mottern
Reply-To: Jason Mottern
Subject: PCA of landmark data on wings
To: [email protected]

Hello,

I am a new list member, as well as a novice with respect to geometric morphometric analysis. I am doing a morphometric analysis of landmark data on wasp wings, and I'm doing both PCA and CVA. I am analyzing males and females separately, and there are three species. When I run all four analyses (CVA and PCA for each sex), the directions along the principal component axes are reversed for the female PCA analysis only. In other words, the signs are all reversed on the PC scores relative to the other three analyses, so its graph is "mirrored" compared to the other three. I am most intrigued as to why this reversal occurs between the female PCA and female CVA. These two analysis are based on the exact same set of partial warp scores, though, of course, subsequent calculations are different. I don't understand how the directions of the vectors are determined, and why they might differ between a PCA and CVA analysis of the same data. The programs I'm using for the analyses are PCAGen and CVAGen (Sheets, 2002). I apologize if I'm not articulating the phenomenon very well, but, like I said, I'm very new to this stuff. Any thoughts would be greatly appreciated.

Cheers,

Jason Mottern

 

 

 

 



----- End forwarded message -----

 

 



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