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 [EMAIL PROTECTED] (801) 408-8111 -----Original Message----- From: [EMAIL PROTECTED] [mailto:[EMAIL PROTECTED] On Behalf Of Pedro Ramirez Sent: Wednesday, June 07, 2006 11:00 AM To: r-help@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! Pedro _________ ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
