----- Forwarded message from Ruth Flatscher <ruflat...@gmail.com> -----

Date: Wed, 24 Apr 2013 02:38:56 -0400
From: Ruth Flatscher <ruflat...@gmail.com>
Reply-To: Ruth Flatscher <ruflat...@gmail.com>
Subject: Re: Linear models for cranial variability
To: morphmet@morphometrics.org

Hi Milos,

I fully agree with Carlo about his suggestions for analysis methods. As for user-friendly graphic interface programs, I just wanted to add that MorphoJ also offers the possibility to perform PLS with separate blocks of data (first import the environmental variables as covariates, the select in the menu "Covariation">"Partial Least Squares" > "Separate blocks" and choose the predictors/response variables ). MorphoJ also offers the possibility to perform simple linear regression; I'm afraid for multiple multivariate regression you would have to use one of the general statistics softwares.

Best wishes,
Ruth


On 24 April 2013 05:53, <morphmet_modera...@morphometrics.org> wrote:

----- 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 -----
>
>
>
>

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