entropy | R Documentation |
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
.
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)
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 |
lambda.freqs |
shrinkage intensity (for "shrink" option). |
verbose |
verbose option (for "shrink" option). |
... |
option passed on to |
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.
entropy
returns an estimate of the Shannon entropy.
freqs
returns a vector with estimated bin frequencies (if available).
Korbinian Strimmer (https://strimmerlab.github.io).
entropy-package
, discretize
.
# 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")
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