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Mon, May 14, 2018 4:42 AM

Dear Soufianou,
this is just a framework. Let's say that you have a vector ('variables') 
containing the name of the environmental variables.

library(raster)
library(dismo)
library(corrplot)

for (variable in variables) {
 ??? assign(variable, raster(paste0(variable, ".asc"))
}
environment <- brick(variables)
environment_standardized <- data.frame(scale(x = 
as.data.frame(environment), center = TRUE, scale = TRUE))

correlation_matrix <- cor(environment_standardized, use = "na.or.complete")
corrplot(corr = correlation_matrix)
VIF <- vif(environment_standardized)
CN <- kappa(na.omit(environment_standardized), exact = TRUE)
# You can select variables that fulfill your criteria about correlation 
structure
selected_variables <- variables[c()] # subsetting

maxent(x = environment[[selected_variables]] , p = presence_points)

HTH,
?kos Bede-Fazekas
Hungarian Academy of Sciences

2018.05.14. 12:28 keltez?ssel, Soufianou Abou via R-sig-Geo ?rta:

Dear Rolf Turner,

  I have points of presence of cowpea in Niger in CSV format; in addition to other variables (soil texture, soil pH, altitude, I downloaded from worldclim archives, the 19 environmental variables, I cut them all at the Niger scale and I converted them under ASCUI format. The idea for me is to choose the best variables to include in the model.
 ?NB. I'm using Maxent model, but I'm not good in R?software.

Merci














SADDA Abou-Soufianou

--------------------------------------

Doctorant

Universit? Dan Dicko Dankoulodo deMaradi-Niger

BP 465?120, avenue MamanKoraou-?ADS

 ???????????????????&

Institut?d?Ecologie et des Sciencesde l?Environnement de Paris (iEES-Paris)

Centre IRD France Nord-(iEES Paris)-32,av.Henri Varangnat 93143 BONDY cedex.

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Lien:?https://ieesparis.ufr918.upmc.fr/index.php?page=fiche&id=378&droit=1


 ?abousoufianou at gmail.com

GSM?: Niger?: (+227) 96-26-99-87/91-56-35-19?; France?(+?33)? 07-55-79-39-93

  
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     Le lundi 14 mai 2018 ? 12:05:40 UTC+2, Rolf Turner <r.turner at auckland.ac.nz> a ?crit :



Please keep your posts "on-list".? You are much more likely to get a
useful answer that way.? There are many others on the list whose
knowledge and insight are far greater than mine.

I have therefore cc-ed the list in this reply.

On 14/05/18 21:48, Soufianou Abou wrote:

Thank you for advice, Rolf Turner

My question is as follows:

I'd use maxent to model the potential distribution of cowpea on the
basis of the only presence data. Indeed, I have acquired a number of
environmental variables and bioclimatic regarding my area of study. But
to choose the most contributive variables in the model; I would like to
make a correlation analysis of these. On this, could you explain to me
the step by step procedures to follow in R? I would like to say scripts
for:- compile and call all environmental variables;- run the correlation
test to select the least correlated ones.

As I said before, I don't think this is the right approach, but I can't
be sure without knowing more about your data.? I find your description
to be vague.

How are your data stored?? What information do you have about the
"distribution of cowpea".? Do you have *points* where cowpea is present
or more extensive *regions* where it is present?? (And could these
regions be "considered to be points" on the scale of interest?) How are
your predictors stored?? Are the values of these predictors known at
every point of your study area?? Can you show us a bit of your data (use
the function dput() to include *a small sample* of your data in the body
of your email).

If you insist on mucking about with correlation and testing, perhaps the
function cor.test() will give you what you want.? I reiterate however
that this seems to me to be a wrong approach.

cheers,

Rolf Turner

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