[R] Density Estimation

Greg Snow Greg.Snow at intermountainmail.org
Wed Jun 7 19:21:51 CEST 2006

Not a direct answer to your question, but if you use a logspline density
estimate rather than a kernal density estimate then the logspline
package will help you and it has built in functions for dlogspline,
qlogspline, and plogspline that do the integrals for you.

If you want to stick with the KDE, then you could find the area under
each of the kernals for the range you are interested in (need to work
out the standard deviation used from the bandwidth, then use pnorm for
the default gaussian kernal), then just sum the individual areas. 

Hope this helps,

Gregory (Greg) L. Snow Ph.D.
Statistical Data Center
Intermountain Healthcare
greg.snow at intermountainmail.org
(801) 408-8111

-----Original Message-----
From: r-help-bounces at stat.math.ethz.ch
[mailto:r-help-bounces at stat.math.ethz.ch] On Behalf Of Pedro Ramirez
Sent: Wednesday, June 07, 2006 11:00 AM
To: r-help at stat.math.ethz.ch
Subject: [R] Density Estimation

Dear R-list,

I have made a simple kernel density estimation by

x <- c(2,1,3,2,3,0,4,5,10,11,12,11,10)
kde <- density(x,n=100)

Now I would like to know the estimated probability that a new
observation falls into the interval 0<x<3.

How can I integrate over the corresponding interval?
In several R-packages for kernel density estimation I did not found a
corresponding function. I could apply Simpson's Rule for integrating,
but perhaps somebody knows a better solution.

Thanks a lot for help!



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