entropy.shrink {entropy} | R Documentation |
entropy.shrink
estimates the Shannon entropy H of the random variable Y
from the corresponding observed counts y
by plug-in of shrinkage estimate
of the bin frequencies.
mi.shrink
estimates the corresponding mutual information of two random variables.
freq.shrink
estimates the bin frequencies from the counts y
using a James-Stein-type shrinkage estimator. The default shrinkage target is the uniform,
unless otherwise specified.
entropy.shrink(y, unit=c("log", "log2", "log10"), target=1/length(y), verbose=TRUE) mi.shrink(y, unit=c("log", "log2", "log10"), target=1/length(y), verbose=TRUE) freqs.shrink(y, target=1/length(y), verbose=TRUE)
y |
vector or matrix of counts. |
unit |
the unit in which entropy is measured. |
target |
the shrinkage target for the frequencies (default: uniform distribution). |
verbose |
report shrinkage intensity. |
The shrinkage estimator is a James-Stein-type estimator. It is essentially
a entropy.Dirichlet
estimator, where the pseudocount is
estimated from the data.
For details see Hausser and Strimmer (2008).
entropy.shrink
returns an estimate of the Shannon entropy.
mi.shrink
returns an estimate of mutual information.
freqs.shrink
returns the underlying frequencies.
In all instances the estimated shrinkage intensity is attached to the returned
value in the attribute lambda.freqs
.
Korbinian Strimmer (http://strimmerlab.org).
Hausser, J., and K. Strimmer. 2008. Entropy inference and the James-Stein estimator, with application to nonlinear gene association networks. See http://arxiv.org/abs/0811.3579 for publication details.
entropy
, entropy.Dirichlet
,
entropy.NSB
,
entropy.ChaoShen
,
entropy.plugin
, mi.plugin
.
# load entropy library library("entropy") # observed counts for each bin y = c(4, 2, 3, 0, 2, 4, 0, 0, 2, 1, 1) # shrinkage estimate entropy.shrink(y) # contigency table with counts for two discrete variables y = rbind( c(1,2,3), c(6,5,4) ) # shrinkage estimate of mutual information mi.shrink(y)