Hi Clément,
Thanks for the patience and kindness of replying my questions!! :)
I had already read the paper. I understood the idea of the scatter plot,
that the arrows were a projection of the marginality and specialization but
I didn't understand where the length of arrows came from. I was somehow lost
thinking how their length represent the marginality and specialization. Now
you tell me that they "correspond to the scores of the environmental
variables on the axes of the ENFA". Hmmm.... Are these scores given
somewhere in the output of enfa?
You see, the main issue for me is that if I put that graph in a manuscript,
I'd like to be able to tell, the arrow length represent XX times the
marginality and XX times the specialization of each variable or something
like that. In order to give an idea of the real value of marginality and
specialization. Does it make any sense? Maybe it doesn't make any sense and
it's not necessary, what do you think? I hope you understand me this time...
otherwise, I'll give up!!!
Regarding the global specialization issue. Not sure about something again.
You told me that there's not global specialization, but Hirzel et al (2002),
described it. Why you say "it cannot be measured globally over the
ecological space"? Is it wrong if I use the global specialization formula of
Hirzel et al (square root of the sum of the eigeinvalues divided by the
number of variables) to estimate this global specialization (sensu Hirzel et
al)? Can I do that?
And regarding the tolerance, just to be sure we're talking about the same,
is it the inverse of the specialization, right? if I calculate it, could I
use it to estimate the specialization? Actually, I have tried to calculate
the tolerance, but I think I have used the wrong "wei". Can you please
confirm I'm using the correct terms?
You told me to use this formula: sum(dudi.pca(tab, row.w=wei,
scan=FALSE)$eig); in which "tab" is the dataframe containing the values of
environmental variables and "wei" is vector describing the utilization
weight of each pixel. Using the manual example for enfa:
data(lynxjura)
map <- lynxjura$map
tmp <- lynxjura$locs[,4]!="D"
locs <- lynxjura$locs[tmp, c("X","Y")]
dataenfa1 <- data2enfa(map, locs)
enfa1 <- enfa(pc, dataenfa1$pr,+ scannf = FALSE)
For me, in this case, "tab" would be kasc2df(map) and "wei" would be
dataenfa1$pr
so, the tolerance would be: sum(dudi.pca(kasc2df(map), row.w=dataenfa1$pr,
scan=FALSE)$eig)
Is this correct? I got this huge value (2355). It's kind of high, isn't it?
And about the specialization axis you keep in enfa (or madifa or gnesfa or
any other one of these analysis), is there a way to get the % variation
explained by each eigenvalue? I mean, a referee can ask that, right? I have
tried to figure it out, but without any luck... I have chosen only one
eigenvalue of specialization, but I wasn't able to calculate how much
variation it accounted for.
I hope you don't mind all these question... I need to be sure of what I'm
doing if I want to publish these results...
Thanks!!!
Consuelo
-------------
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
oooO
( ) Oooo
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_) ) /
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Stop Gaza Massacre
2010/6/7 Clément Calenge <clement.cale...@gmail.com>
> On 06/04/2010 09:40 AM, Consuelo Hermosilla wrote:
>
> Hummm, true, I got confused! Sorry!! I meant scatter.enfa.
> What I don't understand is the length of the arrows. The grid is d=2,
> different from the grid set with the biplot of marginality and
> specialization (d=0.5). In that case, the length make senses to me, but I
> don't understand it in the scatter.enfa. They don't have the same lenght
> /value as they have in the biplot. Do you understand me?
>
>
> I do not understand you... The function scatter.enfa draws the biplot
> associated with the results of the ENFA. The following paper explains this
> graph in detail:
>
> Basille, M., Calenge, C., Marboutin, E., Andersen, R. and Gaillard, J.M.
> 2008. Assessing habitat selection using multivariate statistics: some
> refinements of the ecological niche factor analysis. Ecol. model. 211:
> 233--240.
>
>
>
>
> I am not sure what you mean by "biplot of marginality and specialization".
> There is only one way to draw a biplot with the ENFA: it is provided by
> scatter.enfa (see the paper cited above). So I do not understand how the
> result of scatter.enfa could be inconsistent with the biplot, since the
> result of scatter.enfa *is* the biplot.
> Best,
>
>
> 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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