Majnu John wrote: > > When I use bw.SJ (based on Sheather & Jones, 1991) in R to estimate > the bandwidth for a highly skewed data, I get the following message: > "sample is too sparse to find TD". I played around with the parameters > such as no. of bins (nb), lower, upper (range over which to minimize) to > no avail. My sample size is 50,000. > > Can anyone tell me what this means and of some way to get around > this. Any help is very much appreciated. >
I also get the same error message. It seems to be a function of sample size. The following example runs fine with fewer random numbers. rdat <- rnorm(111111) hist(rdat,breaks=100) rdpi <- density(rdat, width="SJ-dpi", n=ceiling(diff(range(rdat))*100)) Erreur dans bw.SJ(x, method = "dpi") : sample is too sparse to find TD Clearly density() doesn't like being turned up to 11. I didn't find a solution in R, but the follow package for python seems to handle sample sizes at least two orders of magnitude greater than density() in R :-p http://bonsai.ims.u-tokyo.ac.jp/~mdehoon/software/python/Statistics/manual/index.xhtml Good luck Dave -- View this message in context: http://www.nabble.com/bandwidth-estimation-using-bw.SJ-tp16284293p16470097.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
