# [R] Computing the mode

Tue Feb 24 22:23:20 CET 2004

```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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