Re: bivariate kernel density estimates at point locations ( r ather than at grid locations)

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Re: bivariate kernel density estimates at point locations ( r ather than at grid locations)

Liaw, Andy
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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