MADIFA results

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 :

MADIFA results

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