discretize3D: Discretize 3-dimensional continuous data into bins

In Informeasure: R implementation of Information measures

Description

The function of discretize3D is used to assign the observations of three sets of continuous random variables to bins, and returns a corresponding three-dimensional count table. Two of the most common discretization methods are available: "uniform width" and "uniform frequency".

Usage

discretize3D(x, y, z, algorithm = c("uniform_width", "uniform_frequency"))

Arguments

x	a numeric vector of the random variable x.
y	a numeric vector of the random variable y.
z	a numeric vector of the random variable z.
algorithm	two discretization algorithms are available, "uniform_width" is the default.

Details

Uniform width-based method ("uniform_width") divides the continuous data into N bins with equal width, while Uniform frequency-based method ("uniform_frequency") divides the continuous data into N bins with (approximate) equal count number. By default in both methods, the number of bins N is initialized into a round-off value according to the square root of the data size.

Value

discretize3D returns a 3-dimensional count table.

Examples

# three vectors that correspond to three continuous random variables
x <- c(0.0, 0.2, 0.2, 0.7, 0.9, 0.9, 0.9, 0.9, 1.0)
y <- c(1.0, 2.0,  12, 8.0, 1.0, 9.0, 0.0, 3.0, 9.0)
z <- c(3.0, 7.0, 2.0,  11,  10,  10,  14, 2.0,  11)

# corresponding count table estimated by "uniform width" algorithm
discretize3D(x,y,z, "uniform_width")

# corresponding count table estimated by "uniform frequency" algorithm
discretize3D(x,y,z, "uniform_frequency")

Informeasure documentation built on Nov. 8, 2020, 7:20 p.m.