entropy (version 1.1.9)

discretize: Discretize a Continuous Random Variable

Description

discretize puts observations from a continuous random variable into bins and returns the corresponding table of counts.

Usage

discretize( x, numBins, r=range(x) )

Arguments

x

vector of observations.

numBins

number of bins.

r

range of the random variable (default: observed range).

Value

• discretize returns a vector containing the counts for each bin.

Details

All bins have the same width. It is determined by the length of the range divided by the number of bins.

See Also

entropy.

Examples

# load entropy library 
library("entropy")

# sample from continuous uniform distribution
x1 = runif(10000)
hist(x1, xlim=c(0,1), freq=FALSE)

# discretize into 10 categories
y1 = discretize(x1, numBins=10, r=c(0,1))
y1

# compute entropy from counts
entropy(y1) # empirical estimate near theoretical maximum
log(10) # theoretical value for discrete uniform distribution with 10 bins 

# sample from a non-uniform distribution 
x2 = rbeta(10000, 750, 250)
hist(x2, xlim=c(0,1), freq=FALSE)

# discretize into 10 categories and estimate entropy
y2 = discretize(x2, numBins=10, r=c(0,1))
y2
entropy(y2) # almost zero