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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