It certainly helps, thank you very much Clément!!

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
   (   (     )
  _)    )  /
        (_/

Stop Gaza Massacre


2010/5/31 Clément Calenge <clement.calenge@gmail.com>

> On 05/31/2010 02:28 AM, Consuelo Hermosilla wrote:
>
>> I have a doubt. I'd like to implement the FANTER analysis, described in
>> Calenge&  Basille (2008), which should be a type of Gnesfa analysis,
>> right?
>> But I don't know how to implement it (in adehabitat)... the gnesfa default
>> option is equivalent to FANTER?
>>
>
> No. Actually, depending on the distribution chosen, the GNESFA will
> correspond to the MADIFA or the FANTER.  Consider the examples of the help
> page of this function:
>
> ## Loads the data
> data(bauges)
> kasc <- bauges$kasc
> locs <- bauges$locs
>
> ## Prepares the data for the GNESFA:
> litab <- kasc2df(kasc)
> pc <- dudi.pca(litab$tab, scannf = FALSE)
> Dp <- count.points(locs, kasc)[litab$index]
>
>
> In this case, pc stores the environmental information. Conceptually, it can
> be considered as a table storing the value of the environmental variables
> (columns) in each pixel of the map (rows). Dp is a vector containing the
> utilization weights, i.e. the number of animals in each pixel of the map.
> The MADIFA corresponds to a GNESFA with the reference distribution
> corresponding to the utilization weights, that is, to perform the MADIFA,
> type:
>
> gn <- gnesfa(pc, Reference = Dp)
>
> If you want to perform a FANTER, you have to set the utilization weights as
> the Focus distribution, that is:
>
> gn <- gnesfa(pc, Focus = Dp)
>
>
>
>
>   I understand the modifications leading to
>> ENFA and MADIFA (using gnesfa fuction), but I'm kind of lost in how to
>> implemet FANTER...
>> I know (following the paper) that I should keep the first and last
>> eigenvalue, but what about the other options of the function?
>>
>>
>
> You can choose the number of first and last axes that you keep in your
> analysis, not necessarily only the first and last one.
> The options nfFirst and nfLast are easier to understand if you do not set
> scannf=FALSE, so that the eigenvalue barplot is displayer. For example, if
> you can identify visually a clear "break" in the decrease of the eigenvalues
> after the second eigenvalue, then, it would be a good idea to keep the first
> two axes. Similarly, if you can identify a strong "break" in the increase of
> 1/eigenvalues just before the eigenvalue P-3 (where P is the total number of
> eigenvalues), then it would be a good idea to keep the last three axes. Then
> factorial maps and other tools described on the help page and in the paper
> would help to interpret the results.
> Hope this helps,
>
>
> Clément Calenge
>
>

	[[alternative HTML version deleted]]

