# bimodality_amplitude: Bimodality Amplitude 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 Ampltiude of a data vector. This is a measure of the proportion of bimodality and the existence of bimodality. The value lies between zero and one (that is: [0,1]) where the value of zero implies that the data is unimodal and the value of one implies the data is two point masses. The proportion of bimodality here is referring to the mixture proportions of two, say, Gaussian (normal) components that can have different frequencies. For instance, a 50 separation will be different from a 25 results of "Example 2" and "Example 3" to get a better understanding.

## Usage

 `1` ```bimodality_amplitude(x, fig, ...) ```

## Arguments

 `x` Data vector. `fig` Should a figure with the antimodes and peaks be plotted? Defaults to TRUE. `...` 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 16 17 18``` ```#Example 1 data<-c(rnorm(21,0,1),rnorm(21,5,1)) hist(data) bimodality_amplitude(data,TRUE) #Example 2 dist1<-rnorm(21,5,2) dist2<-dist1+11 data<-c(dist1,dist2) hist(data) bimodality_amplitude(data,TRUE) #Example 3 dist1<-rnorm(21,-15,1) dist2<-rep(dist1,3)+30 data<-c(dist1,dist2) hist(data) bimodality_amplitude(data,TRUE) ```

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