example.data

<p>A dataset with 200 genes and 2 classes of data, each with 100 observations.  The two classes' data matrices are stored in a list.</p>

datasets

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. Reference: Danaher P, Wang P, Witten DM. (2013)
<doi:10.1111/rssb.12033>.

Patrick Danaher

Performs the Joint Graphical Lasso for Sparse Inverse Covariance
Estimation on Multiple Classes

example.data function

<p>The format is:
List of 2
 $ : num [1:100, 1:200] 0.395 -2.03 -1.704 -0.469 1.75 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : NULL
  .. ..$ : chr [1:200] "gene 1" "gene 2" "gene 3" "gene 4" ...
 $ : num [1:100, 1:200] -1.548 1.45 -0.812 -0.589 0.69 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : NULL
  .. ..$ : chr [1:200] "gene 1" "gene 2" "gene 3" "gene 4" ...</p>

example.data: Toy two-class gene expression dataset.

Description

Usage

Arguments

Format

Examples