-------- Original Message --------
Subject: Re: controlling for tooth wear
Date: Fri, 9 Dec 2011 19:34:37 -0500
From: [email protected]
To: [email protected]
Hi Rodrigo,
I think that the problems arise on the discretization of tooth
wear. Although took some more time you could quantify the tooth
wear as Vizcaino et al 2006 and 2011 do in some mammals
including caviomorph rodents.
Although they did in 2006 with windig you can use now the imageJ or even
more a combination of graphics programs (as corel or adobe illustrator) to
covert to a Black and with images of the dental wear.
http://www.scielo.org.ar/scielo.php?pid=S0002-70142006000100002&script=sci_arttext
I think than then you can use OSA as a Covariable in your GLM model.
All the best
Guillermo
-------- Original Message --------
Subject: controlling for tooth wear
Date: Wed, 7 Dec 2011 16:16:27 -0500
From: Rodrigo Lima <[email protected]>
To: [email protected] <[email protected]>
Hello morphometricians,
I would be really thankful if you could help me with the following
problem:
I'm studying differences in shape and size of teeth between different
populations of voles, and trying to explain this variation through
environmental and geographical variables. Tooth wear is a great source
of variation, so I'm trying to get rid of the effect of wear. I tried
two very similar approaches:
1 - First I classified all teeth into 5 stages of tooth wear (0 being
the "brand new" teeth, 4 being the "very worn-out" teeth). Then I
performed a linear regression using scores on principal components as
the dependent variables and tooth wear stage as the independent
variable. I took the residuals and used them as the new variables
against which I regressed my environmental and geographical variables.
2 - I did the same thing as in 1, but instead of residuals from a linear
regression I used the residuals from a GLM.
3 - I did the same thing as in 1 and 2 with the size variable (occlusal
surface area).
The problem: the results of the regressions of residuals on
environmental and geographical variables have very low r2 (adjusted r2
always around 0.04). If I ignore tooth wear I get a higher r2 (around
0.25). So: does this mean that practically all the variation in my
sample is due to tooth wear? Am I doing something wrong here?
I thank in advance all of those who kindly use their time to read this
post.
Best wishes,
Rodrigo
MSc student
McGill University
([email protected])
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Lic. Guillermo CASSINI
Dto. Ciencias Básicas
Universidad Nacional de Luján
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