[R] Sampling from multi-dimensional kernel density estimation

[Greg Snow]

Generating new data from a kernel density estimate is equivalent to choosing a point from your data at random, then generating a point from your kernel centered at the chosen point.

-- 
Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at imail.org
801.408.8111


> -----Original Message-----
> From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-
> project.org] On Behalf Of Christoph Goebel
> Sent: Friday, November 19, 2010 1:56 PM
> To: r-help at r-project.org
> Subject: [R] Sampling from multi-dimensional kernel density estimation
> 
> Hi,
> 
> 
> 
> I'd like to use a three-dimensional dataset to build a kernel density
> and
> then sample from the distribution.
> 
> 
> 
> I already used the npudens function in the np package to estimate the
> density and plot it:
> 
> 
> 
> fit<-npudens(~x+y+z)
> 
> plot(fit)
> 
> 
> 
> It takes some time but appears to work well.
> 
> 
> 
> How can I use this to evaluate the fitted function at a certain point,
> e.g.
> (x=1, y=1, z=1)? Does R provide methods for sampling from the fitted
> function?
> 
> 
> 
> Thanks,
> 
> 
> 
> Christoph
> 
> 
> 
> 
> 	[[alternative HTML version deleted]]
> 
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