Hi Clément, Mathiew,

I'm sorry I haven't replied earlier, I'm abroad and I have been a little
off...

Thank you very very much for your answers and tips. I agree with Mathiew,
the enfa it's a very elegant way of exploring the data. That's why I want to
use it in my paper.

Again, about the tolerance, using the code you told me:

pc3 <- dudi.pca(kasc2df(map)$tab, scan=FALSE)
sum(dudi.pca(pc3$tab, scale=FALSE, row.w = dataenfa1$pr/sum(dataenfa1$pr),
scan=FALSE)$eig)

I got* 3.129783*, is this correct?

But I don't understand, shouldn't tolerance have a value ranging from 0 to
1? I thougth that was the idea, since specialization varies from 1 to
infinite, right? [i.e. Reutter et al 2003***]

In the other hand, I have estimated the global specializations using Hirzel
et al (2002) formula,  like this:

S <- (sqrt(sum(enfa1$s)))/1.96

Does it seem correct?

I got this value S = 0.996322 (<1 though), and then, my tolerance (1/S) was
1.003692 (>1).

It bothers me that we don't have the same values! grrr

What do you think?

About my other question about the scatter plot of enfa, the scores and so
on... I think I haven't been able to explain myself... I have uploaded a
picture http://img808.imageshack.us/i/scatter.jpg/. I hope you can see what
I mean there.

Thanks again guys!!!

Consuelo


*** Reutter, B.A,  V. Helfer, A.H. Hirzel1 & P. Vogel. 2003. Modelling
habitat-suitability using museum collections: an example with three
sympatric Apodemus species from the Alps. Journal of Biogeography, 30,
581–590

-------------
Consuelo Hermosilla
PhD student
Departamento de Ecología y Biología Animal
Departamento de Bioquímica, Genética e Inmunología, Área de Genética
Facultad de Ciencias del Mar
Campus de As Lagoas-Marcosende
Universidad de Vigo
36310 Vigo
SPAIN
Mobile: +34 692 633 298

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Stop Gaza Massacre


2010/6/9 Clément Calenge <clement.calenge@gmail.com>

> On 06/09/2010 09:05 AM, Clément Calenge wrote:
>
>>
>>
>>> sum(dudi.pca(kasc2df(map), row.w = dataenfa1$pr/sum(dataenfa1$pr),
>>> scan=FALSE)$eig)
>>>
>>> which gives you 5 only. The weights should sum to 1 (i.e. they are
>>> proportions). But then, how would you interpret this? This is the same
>>> as for the "global specialization".
>>>
>>
>> Yes, but be careful: kasc2df returns a list with one component "tab" (the
>> component of interest) and one component "index" (the component allowing to
>> rebuild the original kasc), so that the correct code is:
>>
>> sum(dudi.pca(kasc2df(map)$tab, row.w = dataenfa1$pr/sum(dataenfa1$pr),
>> scan=FALSE)$eig)
>>
>
> I was a bit too hasty for this reply. Actually the code is incorrect.
> Consider the following code:
>
> pc <- dudi.pca(kasc2df(map)$tab, row.w = dataenfa1$pr/sum(dataenfa1$pr))
>
> This code performs a *centered and scaled* PCA of the environmental
> variables: this analysis first centers and scales the table containing the
> value of the environmental variables. Therefore, for each variable of
> pc$tab, the mean *weighted by the utilization weights* is equal to zero and
> the variance *weighted by the utilization weights* is equal to 1. Therefore,
> the sum of the eigenvalues of the PCA on this transformed table is equal to
> the number of environmental variables. This code is incorrect (thanks
> Mathieu for noting the inconsistency). First, to calculate the global
> tolerance, you would need to calculate:
>
> pc <- dudi.pca(kasc2df(map)$tab, scan=FALSE)
>
> This preliminary analysis is performed just to center/scale the variables
> with uniform weighting. This allows to compare the different variables in
> pc$tab (they all have the same average and the same scale). *Then*,
> calculate the weighted and *unscaled* PCA of the table pc$tab:
>
> sum(dudi.pca(pc$tab, scale=FALSE, row.w = dataenfa1$pr/sum(dataenfa1$pr),
> scan=FALSE)$eig)
>
> This gives the tolerance of the species on the area,
> Sorry for the confusion,
> HTH,
>
> Clément Calenge
>
> --
> Clément CALENGE
> Cellule d'appui à l'analyse de données
> Office national de la chasse et de la faune sauvage
> Saint Benoist - 78610 Auffargis
> tel. (33) 01.30.46.54.14
>
>

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