entropyShrink: James-Stein Shrinkage Entropy Estimate

In synRNASeqNet: Synthetic RNA-Seq Network Generation and Mutual Information Estimates

Description

Computing the James-Stein Shrinkage Entropy Estimate of cellCounts.

Usage

entropyShrink(cellCounts, unit = unit, shrinkageTarget = shrinkageTarget)

Arguments

cellCounts	an integer vector (or matrix) representing the number of times each particular count is obtained.
unit	the unit in which entropy is measured. One of "bit" (log2, default), "ban" (log10) or "nat" (natural units).
shrinkageTarget	shrinkage target frequencies. If not specified (default) it is estimated in a James-Stein-type fashion (uniform distribution).

Value

The entropyShrink function returns the value of the entropy of that gene H(X) (or pair of genes H(X,Y)).

Author(s)

Luciano Garofano [e-mail], Stefano Maria Pagnotta, Michele Ceccarelli

References

James W., Stein C. (1961). Estimation with Quadratic Loss. Proceedings of the Fourth Berkeley Symposium on Mathematical Statistics and Probability, vol. 1 pp. 361-379.

See Also

entropyML, entropyMM, entropyBayes, entropyCS

Examples

simData <- simulatedData(p = 50, n = 100, mu = 100, sigma = 0.25,
                        ppower = 0.73, noise = FALSE)
cellCounts <- table(simData$counts[1, ])
eShrink <- entropyShrink(cellCounts, unit = "nat", shrinkageTarget = NULL)

Example output

Loading required package: parallel
Loading required package: parmigene
Loading required package: GenKern
Loading required package: KernSmooth
KernSmooth 2.23 loaded
Copyright M. P. Wand 1997-2009
Loading required package: igraph

Attaching package: 'igraph'

The following objects are masked from 'package:stats':

    decompose, spectrum

The following object is masked from 'package:base':

    union

synRNASeqNet documentation built on May 2, 2019, 6:01 a.m.