bimodality_separation: Bimodality Separation Function

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

Description

This function calculates the Bimodality Separation of a data vector. Similar to Ashman, Bird, and Zepf's D statistic ("Ashman's D"), the Bimodality Separation statistic measures how differentiated two distributions (distribution components) are. However, this statistic uses the added assumption that both are Gaussian (normal) distributions (or that the distribution is a mixture of two Gaussian (normal) components). For instance, if the two distributions are identical, this statistic is zero.

Usage

bimodality_separation(mu1, mu2, sd1, sd2, ...)

Arguments

mu1	The mean of mode 1
mu2	The mean of mode 2
sd1	The standard deviation of mode 1
sd2	The standard deviation of mode 2
...	Pass through arguments.

References

Zhang, C., Mapes, B., & Soden, B. (2003). Bimodality in tropical water vapour. Quarterly Journal of the Royal Meteorological Society, 129(594), 2847-2866.

Examples

##Example 1
dist1<-rnorm(15,4,1)
dist2<-rnorm(21,5,1)
hist(c(dist1,dist2))

mu1<-mean(dist1)
mu2<-mean(dist2)
sd1<-sd(dist1)
sd2<-sd(dist2)
bimodality_separation(mu1,mu2,sd1,sd2)

#Example 2
data<-c(rnorm(15,0,1),rnorm(21,15,3))
hist(data)
bimodality_separation(0,15,1,3)

Example output

[1] -0.1984525
[1] -1.875

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