sle.score: Score impact of each sample on sparse leading eigen-value In GabrielHoffman/decorate: Differential Epigenetic Coregulation Test

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

 ```1 2 3 4 5 6``` ```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

 ```1 2 3 4 5``` ```# load iris data data(iris) # Evalaute score on each sample sle.score( iris[,1:4] ) ```