entropy (version 1.2.0)

entropy: Estimating Entropy From Observed Counts

Description

entropy estimates the Shannon entropy H of the random variable Y from the corresponding observed counts y.

freqs estimates bin frequencies from the counts y.

Usage

entropy(y, lambda.freqs, method=c("ML", "MM", "Jeffreys", "Laplace", "SG",
    "minimax", "CS", "NSB", "shrink"), unit=c("log", "log2", "log10"), verbose=TRUE, ...)
freqs(y, lambda.freqs, method=c("ML", "MM", "Jeffreys", "Laplace", "SG",
    "minimax", "CS", "NSB", "shrink"), verbose=TRUE)

Arguments

y

vector of counts.

method

the method employed to estimate entropy (see Details).

unit

the unit in which entropy is measured. The default is "nats" (natural units). For computing entropy in "bits" set unit="log2".

lambda.freqs

shrinkage intensity (for "shrink" option).

verbose

verbose option (for "shrink" option).

...

option passed on to entropy.NSB.

Value

• entropy returns an estimate of the Shannon entropy.

  freqs returns a vector with estimated bin frequencies (if available).

code

method="NSB"

Details

The entropy function allows to estimate entropy from observed counts by a variety of methods:

• method="ML":

{maximum likelihood, see entropy.empirical } method="MM":{bias-corrected maximum likelihood, see entropy.MillerMadow } method="Jeffreys":{entropy.Dirichlet with a=1/2 } method="Laplace":{entropy.Dirichlet with a=1 } method="SG":{entropy.Dirichlet with a=a=1/length(y) } method="minimax":{entropy.Dirichlet with a=sqrt(sum(y))/length(y } method="CS":{see entropy.ChaoShen } method="NSB":{see entropy.NSB } method="shrink":{see entropy.shrink }

See Also

entropy-package, discretize.

Examples

# load entropy library 
library("entropy")

# observed counts for each bin
y = c(4, 2, 3, 0, 2, 4, 0, 0, 2, 1, 1)  

entropy(y, method="ML")
entropy(y, method="MM")
entropy(y, method="Jeffreys")
entropy(y, method="Laplace")
entropy(y, method="SG")
entropy(y, method="minimax")
entropy(y, method="CS")
#entropy(y, method="NSB")
entropy(y, method="shrink")