On Fri, Aug 31, 2012 at 12:15 PM, David L Carlson <dcarlson@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
> >
>

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