network.test: High-Dimensional Differential Network Inference

In sen-zhao/DCA: Differential Connectivity Analysis: Testing Differences in Structures of High-Dimensional Networks

Description

Testing differential connectivity in high-dimensional networks

Usage

network.test(X, est.method = "MB", test.method = "GraceI", sample.split = FALSE, 
alpha = 0.05, test.level = "node", rule = "OR")

Arguments

X	a list of standardized design matrices with the same number of columns.
est.method	method for network estimation: neighborhood selection ("MB").
test.method	method for hypothesis testing: GraceI ("GraceI"), LDPE ("LDPE"), ridge ("ridge") or SKAT ("SKAT").
sample.split	whether samples need to be randomly splitted for estimation and testing.
alpha	alpha level of type-I error rate.
test.level	node-wise ("node") or edge-wise ("edge").
rule	"AND" or "OR" rule for the estimation.

Details

This function tests whether multiple networks have the same set of edges, controlling type-I error rate.

Value

An R ‘list’ with elements:

diffnet

a matrix or vector of differential connections.

Author(s)

Sen Zhao

sen-zhao/DCA documentation built on May 7, 2019, 7:17 p.m.