You can try `locfit', though it does local likelihood, rather than garden-variety kernel density estimation. Here's an example: library(locfit) data(cldem) den.fit <- locfit(~ x1 + x2, data=cltrain) predict(den.fit, newdata=cltrain) Andy From: Strickland, Matthew
Hi, My data consists of a set of point locations (x,y). I would like to know if there is a procedure for bivariate kernel density estimation in R that returns the density estimates at the observed point locations rather than at grid locations. I have looked at a number of different routines and they all seem to return estimates at grid locations. Any type of kernel is fine (i.e., Gaussian, Quartic, etc). Thank you for your help! Matt Strickland U.S. Centers for Disease Control and Prevention
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