----- Forwarded message from Milos Blagojevic
<spearsata...@hotmail.com> -----
Date: Wed, 24 Apr 2013
08:34:17 -0400
From: Milos Blagojevic
<spearsata...@hotmail.com>
Reply-To: Milos Blagojevic
<spearsata...@hotmail.com>
Subject: RE: Linear models for cranial
variability
To: "morphmet@morphometrics.org"
<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
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 -----
>
>
> 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 -----
>
>
----- End forwarded message -----
