Density estimation when an end may not go to zero?
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) Oxford OX1 3TG, UK Fax: +44 1865 272595