[R] Density estimation when an end may not go to zero?

[Prof Brian Ripley]

On Mon, 7 Mar 2005, Spencer Graves wrote:

>     All the density estimators I've found in R seem to force the ends to go 
> to zero.

Which ones are those?

> What can we do if we don't believe that, e.g., with something that 
> might be a uniform distribution or a truncated normal with only observations 
> above mu+sigma observed? 
>     The closest I could come to this was to artificially extend the numbers 
> beyond the range, thereby forcing the density estimator to continue outside 
> the range of the numbers, then plot only the part that I wanted.  The 
> following example supposes simulates observations from a truncated normal 
> with mean 0, standard deviation 1, and only observations above 1.5 are 
> observed and we faked numbers between 1 and 1.5: 
> set.seed(1)
> tst <- rnorm(1000)
> tst1 <- tst[tst>1]
> knl <- density(tst1)
> sel <- knl$x>1.5
> plot(knl$x[sel], knl$y[sel], type="l")

>     Are there any convenient methods for handling this kind of thing 
> currently available in R?

This is covered in MASS, for example.  logspline() would be a good choice 
here: it allows a finite support.

-- 
Brian D. Ripley,                  ripley at stats.ox.ac.uk
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
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