theta2rmat: Condition-adaptive fused graphical lasso

In Yafei611/CFGL: An adaptive procedure for inferring condition-specific gene co-expression network

Description

The function jointly construct gene co-expression network for multiple class using Condition-adaptive Fused Graphical Lasso. Pairwise screening matrics are required to adjust between-condition lasso penalty.

Usage

theta2rmat(theta, top_edge = NULL, min_edge = 0, keep.diag = F,
  verbose = F)

Arguments

Y	A list expression data which are n*p matrices. all matrices should have a same n and p.
lambda1	The tuning parameter for the graphical lasso penalty.
lambda2	The tuning parameter for the between condition group lasso penalty.

Value

CFGL produces a list that contains estimated inverse matrices and other necessary components.

Yafei611/CFGL documentation built on May 25, 2019, 2:23 p.m.