Computing the mode

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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