To bootstrap from a histogram, use sample(bins, replace = TRUE, prob = counts)
Note that a kernel density estimate is biased, so some bootstrap confidence intervals have poor coverage properties. Furthermore, if the kernel bandwidth is data-driven then the estimate is not functional, so some bootstrap and jackknife methods won't work right. Tim Hesterberg http://www.timhesterberg.net New: Mathematical Statistics with Resampling and R, Chihara & Hesterberg >On Fri, Aug 31, 2012 at 12:15 PM, David L Carlson <dcarl...@tamu.edu> wrote: > >> Using a data.frame x with columns bins and counts: >> >> x <- structure(list(bins = c(3.5, 4.5, 5.5, 6.5, 7.5, 8.5, 9.5, 10.5, >> 11.5, 12.5, 13.5, 14.5, 15.5), counts = c(1, 1, 2, 3, 6, 18, >> 19, 23, 8, 10, 6, 2, 1)), .Names = c("bins", "counts"), row.names = >> 4:16, >> class = "data.frame") >> >> This will give you a plot of the kde estimate: >> > >Thanks. > >> >> xkde <- density(rep(bins, counts), bw="SJ") >> plot(xkde) >> >> As for the standard error or the confidence interval, you would probably >> need to use bootstrapping. >> >> >> > On a similar note - is there a way in R to directly resample / >cross-validate from a histogram of a data-set without recreating the >original data-set ? > > >> > -----Original Message----- >> > >> > Hello, >> > I wanted to know if there was way to convert a histogram of a data-set >> > to a >> > kernel density estimate directly in R ? >> > >> > Specifically, I have a histogram [bins, counts] of samples {X1 ... >> > XN} of a quantized variable X where there is one bin for each level of >> > X, >> > and I'ld like to directly get a kde estimate of the pdf of X from the >> > histogram. Therefore, there is no additional quantization of X in the >> > histogram. Most KDE methods in R seem to require the original sample >> > set - and I would like to avoid re-creating the samples from the >> > histogram. Is there some quick way of doing this using one of the >> > standard >> > kde methods in R ? >> > >> > Also, a general statistical question - is there some measure of the >> > standard error or confidence interval or similar of a KDE of a data-set >> > ? >> > >> > Thanks, >> > -fj >> > >> > > [[alternative HTML version deleted]] ______________________________________________ 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.