discretize2D: Discretize 2-dimensional continuous data into bins

In Informeasure: R implementation of Information measures

Description

The function of discretize2D is used to assign the observations of two sets of continuous random variables to bins, and returns a corresponding two-dimensional count table. Two of the most common discretization methods are available: "uniform width" and "uniform frequency".

Usage

discretize2D(x, y, 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.
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

discretize2D returns a 2-dimensional count table.

Examples

# two numeric vectors that correspond to two 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)

# corresponding count table estimated by "uniform width" algorithm
discretize2D(x,y, "uniform_width")

# corresponding count table estimated by "uniform frequency" algorithm
discretize2D(x,y, "uniform_frequency")

Example output

               
                [0,4] (4,8] (8,12]
  [0,0.333]         2     0      1
  (0.333,0.667]     0     0      0
  (0.667,1]         3     1      2
   
    1 2 3
  1 1 1 1
  2 1 1 1
  3 1 1 1

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