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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