On Mar 9, 2012, at 4:36 PM, Max Kuhn wrote:
Can anyone recommend a good nonparametric density approach for data
bounded
(say between 0 and 1)?
I thought the "canonical" answer, at least the one that generally is
put forward whe people have difficulty with the stats::spline results
was to turn to function 'logspline' in package logspline.
For example, using the basic Gaussian density approach doesn't
generate a
very realistic shape (nor should it):
set.seed(1)
dat <- rbeta(100, 1, 2)
plot(density(dat))
require(logspline)
set.seed(1)
dat <- rbeta(100, 1, 2)
lsdat <- logspline(dat, lbound=0,ubound=1)
plot(lsdat)
# yield sharp edges to density.
(note the area outside of 0/1)
The data I have may be bimodal or have other odd properties (e.g.
point
mass at zero).
Ah, the Dirac function. (Just my physics background showing.)
HTH;
David.
I've tried transforming via the logit, estimating the
density then plotting the curve in the original units, but this
seems to do
poorly in the tails (and I have data are absolute zero and one).
Thanks,
Max
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