Ashmans_D


This function calculates Ashman's D, which is a measure of how
well differentiated two distributions (distribution components) are.
For instance, if the two distributions are identical, this 
statistic is zero. A good rule of thumb is that if the statistic 
is above ~2, there is good separation.
If you suspect that your data is bimodal this can be used 
by replicating the suspected mixture components and checking the 
statistic. Alternatively, if the components are known outright 
this is straightforward to implement.


Measure

Bimodality

Modality

Parametric

Designed with a dual purpose of
accurately estimating the mode (or modes) as well as characterizing
the modality of data. The specific application area includes complex
or mixture distributions particularly in a big data environment.
The heterogeneous nature of (big) data may require deep introspective
statistical and machine learning techniques, but these statistical tools
often fail when applied without first understanding the data. In small
datasets, this often isn't a big issue, but when dealing with large scale
data analysis or big data thoroughly inspecting each dimension
typically yields an O(n^n-1) problem. As such, dealing with big data
require an alternative toolkit. This package not only identifies the
mode or modes for various data types, it also provides a programmatic
way of understanding the modality (i.e. unimodal, bimodal, etc.) of
a dataset (whether it's big data or not). See
<http://www.sdeevi.com/modes_package> for examples and discussion.

Sathish Deevi

modes

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

Ashmans_D function

A function to calculate Ashman, Bird, and Zepf's D Statistic

Ashmans_D: A function to calculate Ashman, Bird, and Zepf's D Statistic

Description

Usage

Arguments

References

Examples