John - My recollection is that Adrian Raftery's contributed package 'mclust' does kernel density estimation as well. Not sure whether it does what you need. Take a look at it on CRAN. Ah..I see that the description which shows up on Jon Baron's search page is not encouraging. Give it a try, anyway. That description does not do it justice.
- tom blackwell - u michigan medical school - ann arbor - On Wed, 22 Oct 2003, John Fieberg wrote: > 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 > ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help
