sle.score: Score impact of each sample on sparse leading eigen-value

In GabrielHoffman/decorate: Differential Epigenetic Coregulation Test

Score impact of each sample on sparse leading eigen-value

Description

Score impact of each sample on sparse leading eigen-value. Compute correlation using all samples (i.e. C), then compute correlation omitting sample i (i.e. Ci). The score the sample i is based on sparse leading eigen-value of the diffrence between C and Ci.

Usage

sle.score(
  Y,
  method = c("pearson", "kendall", "spearman"),
  rho = 0.05,
  sumabs = 1
)

Arguments

Y	data matrix with samples on rows and variables on columns
method	specify which correlation method: "pearson", "kendall" or "spearman"
rho	a positive constant such that cor(Y) + diag(rep(rho,p)) is positive definite.
sumabs	regularization paramter. Value of 1 gives no regularization, sumabs*sqrt(p) is the upperbound of the L_1 norm of v,controling the sparsity of solution. Must be between 1/sqrt(p) and 1.

Value

score for each sample measure impact on correlation structure

See Also

sle.test

Examples

# load iris data
data(iris)

# Evalaute score on each sample
sle.score( iris[,1:4] )

GabrielHoffman/decorate documentation built on May 23, 2023, 1:29 a.m.