----- Forwarded message from Michael Kenyhercz
<[email protected]> -----
Date: Wed, 5 Mar 2014
11:22:10 -0500
From: Michael Kenyhercz
<[email protected]>
Reply-To: Michael Kenyhercz
<[email protected]>
Subject: Re: Shape analysis without
removing size as a factor?
To:
[email protected]
CT,
You can export centroid size (both original and
log) from the Project Tree tab. Click on the file you want to export, which I am
assuming is PC scores, and then go to File and select "Export Dataset" Here you
can select the Datatypes you want, which should be PC Scores, and any
classifiers you might have. Also in the Projects Tree tab you will have to
select the file you imported that should be at the top of the tree - this file
will contain the Raw Coordinates, Procrustes Coordinates, and Centroid Sizes.
Click on that file and, again, go up to File and then "Export Dataset" and
select the variables and identifiers you want exported. You can then merge the
two files to get a dataset that has both the PC scores and centroid size in R
using the merge() function, or in Excel if you sort by IDs, you can just copy
and paste.
Depending on your particular research question, DFA might
not be appropriate, but that is something you want to hash out with your
advisor. However, this way at least you can include centroid size in your final
analyses.
Cheers,
Mike
Michael Kenyhercz
PhD
Candidate
University of Alaska Fairbanks
Department of Anthropology
On Wed, Mar 5, 2014 at 1:05
AM, <[email protected]>
wrote:
----- Forwarded message from [email protected] -----
Date: Fri, 31 Jan 2014 20:33:52 -0800
From: [email protected]
Reply-To: [email protected]
Subject: Shape analysis without removing size as a factor?
To: [email protected]
----- Forwarded message from Celena Toon <[email protected]> -----
Date: Mon, 20 Jan 2014 15:36:52 -0500
From: Celena Toon <[email protected]>
Reply-To: Celena Toon <[email protected]>
Subject: Shape analysis without removing size as a factor?
To: [email protected]
Hello,
I've been working on my master's thesis that uses a geometric
morphometric approach to analyzing the human tibia and the _expression_
of sexual dimorphism. I've previously consulted this forum about
formatting my text files and it has been a wonderful help! After
conducting my analyses, I did not get the results expected and my
advisor wants me to seek other ways I could potentially analyze my
data to cover all my bases and make sure I'm not doing something
wrong. Using MorphoJ, I conducted a Procrustes fit, a principal
components analysis, and a discriminant function analysis. I know
that the Procrustes fit removes size as a factor, but is there a way I
could analyze my data in terms of both size and shape? Or should I be
approaching this differently?
Thank you,
CT
----- End forwarded message -----
----- End forwarded message -----
----- End forwarded message
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