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 <[EMAIL PROTECTED]> 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 <[EMAIL PROTECTED]> > 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 > > > > > > ______________________________________________ > > > [EMAIL PROTECTED] mailing list > > > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > > > PLEASE do read the posting guide! > > > http://www.R-project.org/posting-guide.html > > > > ______________________________________________ > > [EMAIL PROTECTED] mailing list > > https://www.stat.math.ethz.ch/mailman/listinfo/r-help > > PLEASE do read the posting guide! http://www.R- > project.org/posting- > > guide.html > ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html