-------- Original Message --------
Subject: RE: Simulating naturally occurring human craniofacial variation
Date: Thu, 3 Feb 2011 13:09:56 -0500
From: Murat Maga <[email protected]>
Organization: University of Washington
To: <[email protected]>
Hi Hans,
For your first aim why don't use the bootstrapping your sample? Then I guess
you can try to compare the procrustes distances for each mean configuration
from your bootstrap samples to assess the extent of variation.
For your second question, are you aware of the shapes package in R? It does
PCA, plus it will let you visualize the eigenvectors.
Hope these help.
M
-----Original Message-----
From: morphmet [mailto:[email protected]]
Sent: Thursday, February 03, 2011 9:25 AM
To: morphmet
Subject: Simulating naturally occurring human craniofacial variation
-------- Original Message --------
Subject: Simulating naturally occurring human craniofacial variation
Date: Mon, 31 Jan 2011 16:49:44 -0500
From: Hans Wellens <[email protected]>
To: <[email protected]>
Dear all,
For an ongoing research project, I would need to "simulate" (within
reasonable limits) naturally occurring human craniofacial variation.
More precisely, I was hoping to apply principal component analysis to a
relatively large patient sample, to then "randomly" vary/modify a
consensus configuration while respecting the underlying population's
covariation structure. Simply generating Gaussian scatter around the
consensus configuration's landmarks won't cut it, since it neglects this
underlying covariation structure. The goal would understandably be to
limit the random variation around the consensus configuration to what's
clinically (naturally) possible.
Although I understand in broad terms how principal component analysis
decomposes the variance-covariance matrix into eigenvectors and
eigenvalues, and I can program it in R, I keep struggling somewhat with
the required mathematical operations (for instance the ones to visualize
the information contained in the principal components). I know there are
programs out there that will do the latter for you (like MorphoJ), but I
cannot change these to suit my specific research purpose. Therefore I
thought it would probably be preferable to program these visualizations
in R, which also contains packages to perform PC analysis.
Does anyone know whether this would be possible and if so, could you
give me some clues as to how I should proceed?
Thanks!
With kind regards,
Hans
Hans Wellens, DDS
Orthodontist
Groene-Poortdreef 16
8200 Sint-Michiels
Tel: 050/39.68.36
[email protected]
