mutual_inf_cd: Mutual information estimation

In knnmi: k-Nearest Neighbor Mutual Information Estimator

Mutual information estimation

Description

Estimate the mutual information MI(X;Y) of the target X and features Y where X is continuous or discrete and Y is discrete using k-nearest neighbor distances.

Usage

mutual_inf_cd(target, features, k = 3L)

Arguments

target	input vector.
features	input vector or matrix.
k	Integer number of nearest neighbors. The default value is 3.

Details

The features argument is a vector of the same size as the target vector, or a matrix whose column dimension matches the size of the target vector. Discrete values for the features or targets must be numeric or integer types.

Value

Returns the estimated mutual information. The return value is a vector of size 1 if the features argument is a vector. If the features argument is a matrix then the return value is a vector whose size matches the number of rows in the matrix.

References

Ross BC (2014) Mutual Information between Discrete and Continuous Data Sets. PLoS ONE 9(2): e87357. \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1371/journal.pone.0087357")}

Examples

data(mutual_info_df)
set.seed(654321)
mutual_inf_cd(mutual_info_df$Zc_XdYd, t(mutual_info_df$Xd))

M <- cbind(mutual_info_df$Xd, mutual_info_df$Yd)
mutual_inf_cd(mutual_info_df$Zc_XdYdWd, t(M))

knnmi documentation built on May 29, 2024, 10:58 a.m.