entropy: Estimating Entropy From Observed Counts

In entropy: Estimation of Entropy, Mutual Information and Related Quantities

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.

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

The freqs function estimates the underlying bin frequencies. Note that estimated frequencies are not available for method="MM", method="CS" and method="NSB". In these instances a vector containing NAs is returned.

Value

entropy returns an estimate of the Shannon entropy.

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

Author(s)

Korbinian Strimmer (https://strimmerlab.github.io).

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")

Example output

[1] 1.968382
[1] 2.152593
[1] 2.17948
[1] 2.257876
[1] 2.036888
[1] 2.154091
[1] 2.201137
Estimating optimal shrinkage intensity lambda.freq (frequencies): 0.7664 
[1] 2.379603
attr(,"lambda.freqs")
[1] 0.7663934

entropy documentation built on Oct. 3, 2021, 1:06 a.m.