# bimodality_ratio: Bimodality Ratio 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 Ratio which is a measure of the proportion of bimodality. 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", "Example 3", and "Example 4" to get a better understanding.

## Usage

 `1` ```bimodality_ratio(x, list = FALSE, ...) ```

## Arguments

 `x` Data vector. `list` Calculate the Bimodality Ratio for a list of data vectors. This technique is faster than parallelizing for typical big datasets (i.e. when the length of a data vector <=1E9), though benchmarks weren't assessed beyond dimensions of 1E10 x 1E10. When selected, this outputs a list of Bimodality Ratios. Defaults to FALSE. `...` 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 19 20 21 22 23 24 25 26 27``` ```#Example 1 data<-c(rnorm(15,0,1),rnorm(21,5,1)) bimodality_ratio(data,FALSE) values<-as.list(rep(list(rnorm(15,-4,2),rnorm(21,7,2),data),2)) bimodality_ratio(values,TRUE) #Example 2 dist1<-rnorm(21,5,2) dist2<-dist1+11 data<-c(dist1,dist2) hist(data) bimodality_ratio(data,FALSE) #Example 3 dist1<-rnorm(21,-15,1) dist2<-rep(dist1,3)+30 data<-c(dist1,dist2) hist(data) bimodality_ratio(data,FALSE) #Example 4 dist1<-rep(7,70) dist2<-rep(-7,70) data<-c(dist1,dist2) hist(data) bimodality_ratio(data,FALSE) ```

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