Histogram to KDE

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