Computing the mode

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,

There are several possibilities, e.g for *discrete* data:

  x <- floor(runif(100, min=10, max=20)) # some discrete data

Half a year ago it was proposed to use e.g.:

  x[rev(order(table(x)))[1]]

another possibility is:

  f <- table(x)
  as.numeric(names(f[max(f)==f])) # extracts mode(s) from vector names

For *continuous data* you can use class frequencies (from hist) together
with an interpolation formula. Another approximative solution uses
kernel density estimates (the density function):

  x <- rnorm(100, mean=5, sd=1)  # generate some data
  hist(x, prob=TRUE)
  dens <- density(x)
  lines(dens)
  dens$x[dens$y == max(dens$y)] # gives the mode


The precision depends on the parameters of the density() function. If
the distribution is multi-modal, I use a small function peaks() to
extract several maxima (a generalized version of the one in R-news 
2003/3 p. 9).

Thomas P.

Thomas Petzoldt
Dresden University of Technology
Institute of Hydrobiology          petzoldt at rcs.urz.tu-dresden.de
01062 Dresden                      http://www.tu-dresden.de/fghhihb/