I have spatial data in 2 dimensions - say (x,y). The correlation between x and y is fairly substantial. My goal is to use a non-parametric approach to estimate the multivariate density describing the spatial locations. Ultimately, I would like to use this estimated density to determine the area associated with a 95% probability contour for the data.
Given the strong correlation between x and y, I have not been real happy w/ the results obtained using kernel density estimators with separate smoothing parameters for the x and y directions - e.g., bkde2D (KernSmooth library), sm (sm library), kde2d (MASS library). It seems to me that a better alternative would be to transform the data to have ~0 correlation, estimate the density, then transform back to the original scale. Does this seem reasonable for this sort of problem? Has anyone written code in R to do this sort of thing? I also attempted to explore local likelihood fitting (using locfit library). I liked the look of the estimated densities, but found it difficult to obtain predictions at an arbitrary set of grid points (as needed to determine a 95% probability contour). Does anyone have examples using locfit w/ the "ev" option or predict.locfit in order to obtain local likelihood density estimates at an arbitrary set of grid points? Any suggestions would be greatly appreciated! John John Fieberg, Ph.D. Wildlife Biometrician, MN DNR 5463-C W. Broadway Forest Lake, MN 55434 Phone: (651) 296-2704 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
