R help - Mar 2012 - nonparametric densities for bounded distributions

Can anyone recommend a good nonparametric density approach for data bounded
(say between 0 and 1)?

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))
(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). 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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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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>
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David Winsemius, MD
West Hartford, CT