[R] using kernel density estimates to infer mode of distribution

[Adelchi Azzalini]

On Fri, Feb 17, 2006 at 10:45:44AM -0500, Frank Samuelson wrote:
> A  density() fit calls the eval x and estimate y:
> fit<-density(data)
> plot(fit$x,fit$y)
> 
> 

in my earlier message, I explained that I was referring to the
ingredients names produced ny sm.density (of package sm);
in case some other function is used, eg density(), then
a little adjustment of names is required

AA

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