bimodality_coefficient: Bimodality Coefficient

In modes: Find the Modes and Assess the Modality of Complex and Mixture Distributions, Especially with Big Datasets

Description

This function calculates the Bimodality Coefficient of a data vector with the option for a finite sample (bias) correction. This bias correction is important to correct for the (well-documented) finite sample bias. The bimodality coefficient has a range of zero to one (that is: [0,1]) where a value greater than "5/9" suggests bimodality. The maximum value of one ("1") can only be reached when the distribution is composed of two point masses.

Usage

bimodality_coefficient(x, finite = TRUE, ...)

Arguments

x	Data vector.
finite	Should the finite sample size correction be applied to the skewness and kurtosis measures? Defaults to TRUE.
...	Pass through arguments.

References

Ellison, A. (1987). Effect of Seed Dimorphism on the Density-Dependent Dynamics of Experimental Populations of Atriplex triangularis (Chenopodiaceae). American Journal of Botany, 74(8), 1280-1288.

Examples

 data<-c(rnorm(15,0,1),rnorm(21,5,1))
 hist(data)
 bimodality_coefficient(data,TRUE)

Example output

[1] 0.7857285

modes documentation built on May 2, 2019, 1:28 p.m.