BeEFdiscretization.numnum: BeEF: Best Equal-Frequency discretization (for a couple of...

In linkspotter: Bivariate Correlations Calculation and Visualization

Description

Discretize two quantitative variables by optimizing the obtained the Normalized Mutual Information

Usage

BeEFdiscretization.numnum(
  continuousX,
  continuousY,
  maxNbBins = 100,
  includeNA = T,
  showProgress = F
)

Arguments

continuousX	a vector of numeric.
continuousY	a vector of numeric.
maxNbBins	an integer corresponding to the number of bins limitation (for computation time limitation), maxNbBins=100 by default.
includeNA	a boolean. TRUE to include NA value as a factor level.
showProgress	a boolean to decide whether to show the progress bar.

Value

a list of two factors.

Examples

# calculate a correlation dataframe
data(iris)
disc=BeEFdiscretization.numnum(iris$Sepal.Length,iris$Sepal.Width)
summary(disc$x)
summary(disc$y)

linkspotter documentation built on July 23, 2020, 5:08 p.m.