----- Forwarded message from [email protected] -----

     Date: Wed, 05 Feb 2014 22:32:35 -0800
      From: [email protected]
      Reply-To: [email protected]
      Subject: Re: Combining geometric and traditional morphometric datasets
      To: [email protected]

----- Forwarded message from Carmelo Fruciano <[email protected]> -----

Date: Sat, 1 Feb 2014 02:35:23 -0500
From: Carmelo Fruciano <[email protected]>
Reply-To: Carmelo Fruciano <[email protected]>
Subject: Re: Combining geometric and traditional morphometric datasets
To: [email protected]

[email protected] ha scritto:

>
> ----- Forwarded message from Kara Feilich  -----
>
>      Date: Fri, 17 Jan 2014 16:22:49 -0500
>       From: Kara Feilich
>       Reply-To: Kara Feilich
>       Subject: Combining geometric and traditional morphometric datasets
>       To: [email protected]
>
> Hi all,
>
> I'm fairly new at this, so I hope this question makes sense: 
> I'm trying to look for covariation and/or modularity among four  
> datasets (all taken from the same individuals, with a phylogeny),  
> where one dataset has Procrustes coordinates for body landmarks, and  
> the other datasets use linear measures. Is there a way to look for  
> (even just two-way) covariation among the datasets? I would like to  
> use a partial least squares approach, but I'm not sure if the single  
> dimension linear measures will play with the two dimensional  
> landmarks. 
>
> Though, if the landmark coordinates are broken down so that the x  
> and y components of the coordinates are considered independent (i.e.  
> if you have 10 landmarks, it's considered 20 variables), I should be  
> able to just append linear measures as long as I consider them a  
> separate partition, maybe? I hope?
>
> Any ideas on how to work with geometric and traditional measures in  
> tandem would be greatly appreciated. 

Hi Kara,
I'm not quite sure about what you mean in the "appending" part. 
In general, however, you can use partial least squares and the  
Escoufier RV coefficient to see how and how "strongly" your blocks of  
variables (datasets) covary. 
Best,
Carmelo

-- 
Carmelo Fruciano
Marie Curie Fellow - University of Konstanz - Konstanz, Germany
Honorary Fellow - University of Catania - Catania, Italy
e-mail [email protected]
http://www.fruciano.it/research/

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

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