[R] Permutation test with raster data

[Stefan Mühlbauer]

Hello!
 
I am having two variables x and y (whereas y is a set of raster images) and 
want to
quantify the correlation between x and y by calculating the Pearson
Correlation Coefficient. In order to ensure how signficant the correlation
results are, I am interested in getting the p-value (<0.1) for this two
tailed student-t distribution. The problem now is: I have a very small
number of observations and therefore would need to make a permutation test,
which enables to simulate a high number of observations.
 
So far I did  the Pearsons Correlation and Significance test, but without
applying permutaton test. I used following formula for obtaining the
p-values:
 
T = r*(sqrt(n-2))/sqrt(1-r²)
p-value = 2 P [ T(n-2) ≥ |t|]
r...Pearson correlation coefficient
n...degree of freedom
 
Now I have to redo everything using a permutation test. I thought of 
implementing 'lmp' function in 'calc' function of raster package.
The two variables I wanted to save within two lists. I am interested in getting 
the p-value for each pixel. Can this work?
 
I will very much appreaciate your help!
 
                                        
       

Dipl.-Ing. Stefan Mühlbauer, MSc

Kaiser Strasse 85/2/15
A - 1070 Wien

E-Mail:
stefan.mue...@yahoo.de
dattel_pa...@yahoo.de

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