# 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

 `1` ```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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15``` ```##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) ```

modes documentation built on May 30, 2017, 4:35 a.m.