JSDNormal: Calculate the Jensen-Shannon divergence (JSD) between a...

In LDLcalc: Calculate and Predict the Low Density Lipoprotein Values

Calculate the Jensen-Shannon divergence (JSD) between a discrete empirical distribution and the normal distribution.

Description

Calculates the Jensen-Shannon divergence between a discrete distribution and the corresponding normal distribution with mean and standard deviation the same as these of the discrete one.

Usage

JSDNormal(dfSmpl, param)

Arguments

dfSmpl	A data frame containing the values of the discrete distribution. The data frame may contain more that one column with discrete distribution values. The argument "param" specified next will determine which column will be used
param	The name of the column to be used.

Value

The function returns the Jensen-Shannon divergence between the discrete and corresponding normal distribution. It also returns a data frame with the empirical probability of the values supplied in the column as well as the empirical probabilies one of the normal discrete distribution. distribution.

References

D.M. Endres, J.E. Schindelin, A new metric for probability distributions, IEEE Trans. Inf. Theory (2003), https://doi.org/10.1109/TIT.2003.813506.

F. Oesterreicher, I. Vajda, A new class of metric divergences on probability spaces and its applicability in statistics, Ann. Inst. Stat. Math. (2003), https://doi.org/ 10.1007/BF02517812.

Examples

## Not run: 
JSD.between.empirical.Normal =JSDNormal(sampleA,"LDL")

## End(Not run)

LDLcalc documentation built on May 31, 2022, 5:07 p.m.