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 Université 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. | Lien: https://ieesparis.ufr918.upmc.fr/index.php?page=fiche&id=378&droit=1 [email protected] GSM : Niger : (+227) 96-26-99-87/91-56-35-19 ; France (+ 33) 07-55-79-39-93 | | Le lundi 14 mai 2018 à 12:05:40 UTC+2, Rolf Turner <[email protected]> a écrit : 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 -- Technical Editor ANZJS Department of Statistics University of Auckland Phone: +64-9-373-7599 ext. 88276 [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list [email protected] https://stat.ethz.ch/mailman/listinfo/r-sig-geo
