Testing for bimodality is rather testing for unimodality. Hartigan and Hartigan (1985) presented the Dip-Test which is implemented in the R package DipTest with a much better approximation of the test distribution. If the test statistic is too high unimodality is rejected. To estimate the dip point you could choose among several possibilities: (1) A very easy method is to use the kmeans function for a kmeans cluster and use the point in the middle of the connecting line between the kmeans cluster centers. (2) You could estimate a finite mixture distribution and take the middle of the connecting line of the modes.
Best Simon On 24 Nov 2013, at 20:41, Felix Breden <bre...@sfu.ca> wrote: > Hi > I have distributions that are typically bimodal (see attached .pdf), and I > would like to test for bimodality, and then estimate the point between the > two modes, the dip in the distributions. any help would be greatly > appreciated. > thanks > felix > <m66.junction.aln.pairwise.histogram.pdf>______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
