[R] Computing the mode

[Ravi Varadhan]

Forgive me for not following the "posting guidelines" and posting 
before doing my homework!  I checked CRAN website and found that there 
is a package developed by Davies and Kovac, called "ftnonpar" that 
implements the "taut spring" approach that I mentioned in my previous 
posting. 

# For example:
library(ftnonpar)
plot(dclaw(seq(-3,3,len=1000)),type="l")
xx <- rclaw(500)
pmden(xx,verbose=T)

Best,
Ravi.

----- Original Message -----
From: Ravi Varadhan <rvaradha at jhsph.edu>
Date: Tuesday, February 24, 2004 4:23 pm
Subject: Re: [R] 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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