R help - Feb 2006 - using kernel density estimates to infer mode of distribution

Hello...

Is it possible to use "density" or another kernel density estimator to
identify the mode of a distribution?  When I use 'density', the
resulting
density plot of my data is much cleaner than the original noisy histogram, 
and I can clearly see the signal that I am interested in.  E.g., suppose my 
data is actually drawn from two or more normal (or other) 
distributions.  Looking at the kernel density plots, it seems that the 
estimator gives a good approximation of the modal values of each 
distribution, but I can't figure out how to obtain these values short of 
visually estimating the location of the mode using the plot(density).

Is there a relatively easy way to do this?

Thanks in advance for your help!
Dan Rabosky



Dan Rabosky
Department of Ecology and Evolutionary Biology
Cornell University
Ithaca, NY14853-2701 USA
web: http://www.birds.cornell.edu/evb/Graduates_Dan.htm

On Wed, 15 Feb 2006 18:28:25 -0500, Dan Rabosky wrote:

DR> 
DR>  Is it possible to use "density" or another kernel density
DR>  estimator to  identify the mode of a distribution?  When I use
DR>  'density', the resulting 

a simple option is of the form
   fit$eval[fit$estimate==max(fit$estimate)]
assuming that fit$eval is the vector of evaluation points,
and fit$estimate the corrisponding density estimates (this is
the sort of output produced by sm.density)

Here I have assumed there is single mode and we are in the scalar
case, for simplicity. Some variant required in the more general case.


best regards,

Adelchi Azzalini
-- 
Adelchi Azzalini  <azzalini at stat.unipd.it>
Dipart.Scienze Statistiche, Universit?? di Padova, Italia
tel. +39 049 8274147,  http://azzalini.stat.unipd.it/