hdEntropy {sigaR}R Documentation

Entropy estimation.

Description

The (differential) entropy of a high-dimensional multivariate random variable is estimated from a (high-dimensional matrix) under a normality or k-NN distributional assumption.

Usage

hdEntropy(Y, method = "normal", k = 1, center = TRUE, indKnn = TRUE)

Arguments

Y

(High-dimensional) matrix. Rows are assumed to represent the samples, and columns represent the samples' genes or traits.

method

Distributional assumption under which entropy is to be estimated.

k

k-nearest neighbor parameter.

center

Logical indicator: should the columns of Y be centered around zero?

indKnn

Logical indicator: should samples' individual contributions to the k-NN entropy be reported?

Value

The entropy estimate is returned as a numeric.

Author(s)

Wessel N. van Wieringen: w.vanwieringen@vumc.nl

References

Van Wieringen, W.N., Van der Vaart, A.W. (2011), "Statistical analysis of the cancer cell's molecular entropy using high-throughput data", Bioinformatics, 27(4), 556-563.

See Also

entropyTest.

Examples

data(pollackGE16)
hdEntropy(t(exprs(pollackGE16)), method="knn")

[Package sigaR version 1.28.0 Index]