The Joint Graphical Lasso is a generalized method for estimating Gaussian graphical models/ sparse inverse covariance matrices/ biological networks on multiple classes of data. We solve JGL under two penalty functions: The Fused Graphical Lasso (FGL), which employs a fused penalty to encourage inverse covariance matrices to be similar across classes, and the Group Graphical Lasso (GGL), which encourages similar network structure between classes. FGL is recommended over GGL for most applications.
|Date of publication||2013-04-16 21:27:09|
|Maintainer||Patrick Danaher <email@example.com>|
crit: Calculate the critical value of the FGL objective funciton.
example.data: Toy two-class gene expression dataset.
gcrit: Calculate the critical value of the GGL objective funciton.
JGL: Joint Graphical Lasso
JGL-internal: Internal JGL functions
JGL-package: Joint Graphical Lasso
net.degree: List the degree of every node in all classes.
net.edges: List the edges in a network
net.hubs: Get degrees of most connected nodes.
net.neighbors: Get network neighbors of a node
screen.fgl: Quickly identify connected features in the solution to FGL
screen.ggl: Quickly identify connected features in the solution to GGL
subnetworks: Identify subnetwork membership