[R] Computing the mode

[Ravi Varadhan]

I remember Prof. Ripley suggesting the "taut springs" approach to 
estimating the modes, sometime ago in a posting to this group. I would 
be interested in knowing whether there is any R implementation of this 
approach (developed by Davies (1995)), for both non-parametric 
regression and density estimation.

Ravi.

----- Original Message -----
From: Spencer Graves <spencer.graves at pdf.com>
Date: Tuesday, February 24, 2004 7:12 am
Subject: Re: [R] Computing the mode

>      The problem is that 'the statistic "mode" of a sample' has 
> no 
> clear definition.  If the distribution is highly discrete, then 
> the 
> following will do the job: 
> 
> > set.seed(1)
> > X <- rpois(11,1)
> > (nX <- table(X))
> X
> 0 1 2 3
> 4 4 2 1
> > names(nX)[nX==max(nX)]
> [1] "0" "1"
> 
>      However, if the data are continuous with no 2 numbers 
> exactly 
> equal, then the "mode" depends on the procedure, e.g., the 
> specific 
> selection of breakpoints for a histogram.  If you insist on 
> finding 
> something, you can try "www.r-project.org" -> search -> "R site 
> search" 
> for something like ""nonparametric density estimation" and / or 
> "kernel 
> density estimator". 
> 
>      hope this helps. 
>      spencer graves
>      p.s.  This has been discussed recently on this list, but I 
> could 
> not easily find it in the archives. 
> 
> Aurora Torrente wrote:
> 
> > Hi all,
> > I think this question could be quite trivial, but I can´t find 
> out the 
> > solution... How can you compute the statistic "mode" of a 
> sample, in 
> > case it exists (as mode() returns the mode of an object)? I 
> tried 
> > help.search("mode") but I couldn't find a clue...
> > Any help would be much appreciated. Regards,
> >
> >        Aurora
> >
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