----- Forwarded message from [email protected]
-----
Date: Wed, 5 Mar 2014 02:44:01 -0500
From:
[email protected]
Reply-To:
[email protected]
Subject: Re: Shape analysis without
removing size as afactor?
To:
[email protected]
The procrustes fit does remove size but doesn't take
in to effect allometric scaling so you might want to maybe correct for this,
(i.e. size correct) and see if it has an effect on your results. This will
remove shape variation associated with age or size.
Oliver Hooker
Its quite
straight forward to do.
Under the covariation tab
select regression
On the left hand side of the drop
down tab select your data set (under dependent variables)
Under data
matrices select procrustes coordinates
Under variables
select procrustes coordinates
On the right hand side
of the drop down tab select the same data set (under independent
variables)
Under data matrices select centroid size
Under
variable select centroid size (or log centroid
size)
Tick the permutation box
Tick the
pooled regression within subgroup box
High light the classifier for
your subgroups in the box below
this
Execute
No with your
regression dataset highlighted generate a new covariance matrix using your
regression residuals
Tick the pooled within-group covariance box and
highlight your classifier again in the box underneath (what groups your
comparing)
Execute
Now use the
new covariance matrix for your PCA
Or use the regression residuals for
your dicsriminant function / CVA
Hope this
helps
Oliver Hooker
PR~Statistics
On 5 March 2014, [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 anal ysis 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 file s 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
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