----- Forwarded message from carlo.mel...@unina.it -----
Date: Fri, 26 Apr 2013 05:55:22 -0400
From: carlo.mel...@unina.it
Reply-To: carlo.mel...@unina.it
Subject: RE: Linear models for cranial variability
To: morphmet@morphometrics.org
Below the link for PLS:
http://www.nhm.ac.uk/hosted_sites/paleonet/IC-NHM_MSc/Rohlf%20&%20Corti%20(2000).pdf
Rohlf FJ, Corti M. 2000. Use of two-block partial least
squares to study covariation in shape. Systematic Biology
49: 740–753.
In Past:
copy and paste the block of linear measurements (variables = column,
cases = raws) and add the block of enviornment for each cases (yes
percentage of habitat adaptation is ok).
Then select all variables go on: multivariate - two block PLS and
include the number of variables for the first block (say you have 19
measurements vs 10 habitat variables...your number is 19).
Then look at the results. The RV in MorphoJ is the Escoufier index a
measure of strenght of correlation between blocks and it is useful
when testing hypotheses on morphological integration.
In PLS you interpret only R (the correlation coerfficient). If you see
that the correlation between vector 1 measurement and vector 1
environment is low (that is based on the R value) then you can try
transform your measurements or use PC or ratios. Make sure you look at
each pair of verctor per time.
All the best
Carlo
morphmet_modera...@morphometrics.org ha scritto:
>
> ----- Forwarded message from Milos Blagojevic -----
>
> Date: Wed, 24 Apr 2013 08:34:17 -0400
> From: Milos Blagojevic
> Reply-To: Milos Blagojevic
> Subject: RE: Linear models for cranial variability
> To: "morphmet@morphometrics.org"
>
> Thanks to Pere and Carlo,
>
> I haven`t thought about PLS or correlation/covariance matrix
> approach to this problem at all. Maybe you could provide some papers
> that utilize such methodology for correlating cranial dimensions
> with environmental variables?
>
> I have tried this approach and always there is a low value of the Rv
> coefficient.
>
> The only approach I had success with was Beta-regression (Dirichlet
> generalization) when using proportions of habitat types (forest,
> meadow, plow) as dependent and PC scores (mean per population) as
> independent variables. Still I don`t know if this is ok and have
> failed at finding some reference papers.
>
> Best regards,
> Milos
>
>> From: morphmet_modera...@morphometrics.org
>> To: morphmet@morphometrics.org
>> Subject: Re: Linear models for cranial variability
>> Date: Tue, 23 Apr 2013 20:53:14 -0700
>>
>>
>> ----- Forwarded message from carlo.mel...@unina.it -----
>>
>> Date: Tue, 23 Apr 2013 04:01:24 -0400
>> From: carlo.mel...@unina.it
>> Reply-To: carlo.mel...@unina.it
>> Subject: Re: Linear models for cranial variability
>> To: morphmet@morphometrics.org
>>
>> Dear Milos,
>>
>> you can try using Partial Least Square that allows to look at
>> correlation between one block of variables (cranial dimensions) and
>> the second block of variables (environmnetal variables). Make sure you
>> standardize the variables (e.g. for cranial dimension it would be good
>> using log transformation of measurements and for environmental data
>> try to standardize by subtracting mean so that data values are not too
>> disparate or large).
>>
>> Alternatively, if you want to make predictions you can perform a
>> multiple multivariate regression or a Generalised Least Square model.
>> However, they have more assumption dealing with multivariate data
>> normality while PLS has not.
>> You can do PLS using the current version of the free software PAST
>> that has a user friendly interface. For multiple multivariate
>> regression and Generalised Least Square NTSYS or SPSS or specific
>> scripts in R.
>>
>> All the best
>>
>> Carlo Meloro
>>
>> morphmet_modera...@morphometrics.org ha scritto:
>>
>> >
>> >
>> > ----- Forwarded message from Milos Blagojevic -----
>> >
>> > Date: Mon, 22 Apr 2013 15:12:47 -0400
>> > From: Milos Blagojevic
>> > Reply-To: Milos Blagojevic
>> > Subject: Linear models for cranial variability
>> > To: "morphmet@morphometrics.org"
>> >
>> > Dear Morphometricians,
>> > Drifting a little bit from the field of GM I have a question about
>> > the formulation of a linear (or possible any other) model that has
>> > to account for cranial variability in relation to certain
>> > ecological parameters.
>> > My dataset consists of 50 linear measurements taken on roe deer
>> > skulls from 12 populations. After PCA and optional discriminant
>> > analysis I have individual scores that should enter possible linear
>> > model as dependent variables. Ecological data consists of
>> > proportions of forest to meadow to plowland areas (expressed either
>> > as proportions that add up to 1 or as absolute areas in Ha) within
>> > every population and population density (individual/area or
>> > absolute numbers). Any ideas on what kind of a model could be
>> > suitable for this dataset and for testing the hypothesis that
>> > cranial dimensions are predicted by these independent variables
>> > (habitat structure and abundance or population density)?
>> > Best regards,Milos BlagojevicDepartment for Biology and
>> > Ecology,Faculty of Science,Kragujevac,Serbia
>> > Here is sample data (with absolute numbers but they can be expressed
>> > as proportions as well)
>> > PCx score population abundance forest plow meadow -0.6033788
>> > ADA_BEC 1500 61154 12000 32313 0.3250981 ADA_BEC 1500
>> > 61154 12000 32313 0.5577059 ADA_BEC 1500 61154
>> > 12000 32313 -0.1596194 PM 23980 89499 579870 8178
>> > -1.3089952 PM 23980 89499 579870 8178 -2.1693392 SP
>> > 2500 38000 47098 432432 -0.9669080 SP 2500
>> > 38000 47098 432432 -1.8857842 SP 2500 38000 47098
>> > 432432 0.7242678 DKN 65908 181133 12400 1233
>> > 1.6815373 DKN 65908 181133 12400 1233
>> >
>> > ----- End forwarded message -----
>> >
>> >
>> >
>> >
>>
>> ----- End forwarded message -----
>>
>>
>
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>
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