sle.test: Test association between sparse leading eigen-value and...

In GabrielHoffman/decorate: Differential Epigenetic Coregulation Test

Test association between sparse leading eigen-value and variable

Description

Score impact of each sample on sparse leading eigen-value and then peform test of association with variable using non-parametric test

Usage

sle.test(
  Y,
  variable,
  method = c("pearson", "kendall", "spearman"),
  rho = 0,
  sumabs = 1
)

Arguments

Y	data matrix with samples on rows and variables on columns
variable	variable with number of entries must equal nrow(Y). Can be discrete or continuous.
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.

Details

The statistical test used depends on the variable specified. if variable is factor with multiple levels, use Kruskal-Wallis test if variable is factor with 2 levels, use Wilcoxon test if variable is continuous, use Wilcoxon test

Value

list of p-value, estimate and method used

See Also

sle.score delaneau.test

Examples

# load iris data
data(iris)

# variable is factor with multiple levels
# use kruskal.test
sle.test( iris[,1:4], iris[,5] )

# variable is factor with 2 levels
# use wilcox.test
sle.test( iris[1:100,1:4], iris[1:100,5] )

# variable is continuous
# use cor.test with spearman
sle.test( iris[,1:4], iris[,1] )

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