Message-ID: <512BE389.7080102@ase-research.org>
Date: 2013-02-25T22:19:53Z
From: Mathieu Basille
Subject: MADIFA results
In-Reply-To: <511373FA.8070503@isprambiente.it>
Dear Michela,
I apologize for the late answer. From the help of mahasuhab, section "Details":
?mahasuhab
The function ?mahasuhab? first computes this mean vector as well
as the variance-covariance matrix of the niche density function,
based on the value of habitat variables in the sample of
locations. Then, the *squared* Mahalanobis distance from this
optimum is computed for each pixel of the map. Thus, the smaller
this squared distance is for a given pixel, and the better is the
habitat in this pixel.
Assuming multivariate normality, squared Mahalanobis distances are
approximately distributed as Chi-square with n-1 degrees of
freedom, where n equals the number of habitat characteristics. If
the argument ?type = "probability"?, maps of these p-values are
returned by the function. As such these are the probabilities of a
larger squared Mahalanobis distance than that observed when x is
sampled from the niche.
Hope this helps!
Mathieu Basille.
Le 02/07/2013 04:29 AM, Michela Giusti a ?crit :
> Dear list,
>
> I did an ENFA analysis and then I used MADIFA to produce an habitat
> suitability map.
>
> I attached the legends results as jpg (sorry for low resolution).
>
> Can you help me in explaining the unit of mesaure obtained from the
> analysis? percentage of what? and distance (m, Km,...)?
>
> Any help would be appreciated....
>
> Thank you very much.
>
> michela
>
>
>
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--
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Mathieu Basille, PhD
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University of Florida \\
Fort Lauderdale Research and Education Center
(+1) 954-577-6314
http://ase-research.org/basille
~$ fortune
? Le tout est de tout dire, et je manque de mots
Et je manque de temps, et je manque d'audace. ?
-- Paul ?luard