Re: [R] Histogram to KDE

2012-09-06 Thread Tim Hesterberg
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 boo

Re: [R] Histogram to KDE

2012-09-05 Thread firdaus.janoos
On Fri, Aug 31, 2012 at 12:15 PM, David L Carlson 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("bi

Re: [R] Histogram to KDE

2012-08-31 Thread David L Carlson
L Carlson Associate Professor of Anthropology Texas A&M University College Station, TX 77843-4352 > -Original Message- > From: r-help-boun...@r-project.org [mailto:r-help-bounces@r- > project.org] On Behalf Of firdaus.janoos > Sent: Friday, August 31, 2012 9:52 AM > To

[R] Histogram to KDE

2012-08-31 Thread firdaus.janoos
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 kd