Hi there: we have some multivariate datasets (some point measures, some grid or polygon-based) within which we wish to perform bivariate correlation tests (Spearman and Pearson) to determine which if any pairwise correlations are significant. One application would be variable selection within groups of, say, meteorological measurements from climate stations.  Within each dataset, the variables are spatially autocorrelated, so we need to control for non-independence. Really, we do.

 

Are any variogram-based corrections for effective sample size, such as in Eq. 8.5 of Haining (1990), now available in some R package. Or has anything similar been implemented? I coded Eq 8.5 and friends in C once long ago, would rather not do it again:-)

 

This question seems to come up every few years, but not lately. Please forgive if I have failed to check thoroughly enough.

 

Best regards

 

Steve Cumming

Canada Research Chair in Boreal Ecosystems Modelling

Sciences du bois et de la forêt

Université Laval

(418) 656-2131 poste 2593

stevec@sbf.ulaval.ca

 


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