R help - Mar 2005 - Density estimation when an end may not go to zero?

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

      Thanks,
      Spencer Graves

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)
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Oxford OX1 3TG, UK                Fax:  +44 1865 272595