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
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