exampleData

<p>A simulated toy dataset that includes 2 data matrices (from 2 related tasks). Each data matrix is about 100 features observed in 200 samples. The two data matrices are about exactly the same set of 100 features. This multi-task dataset is generated from two related random graphs. Please run demo(fasjem) to learn the basic functions provided by this package.  For further details, please read the original paper:  &lt;http://proceedings.mlr.press/v54/wang17e/wang17e.pdf&gt;.</p>

datasets

This is an R implementation of "A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models" (FASJEM). The FASJEM algorithm can be used to estimate multiple related precision matrices. For instance, it can identify context-specific gene networks from multi-context gene expression datasets. By performing data-driven network inference from high-dimensional and heterogonous data sets, this tool  can help users effectively translate aggregated data into knowledge that take the form of graphs among entities. Please run demo(fasjem) to learn the basic functions provided by this package. For more details, please see <http://proceedings.mlr.press/v54/wang17e/wang17e.pdf>.

Beilun Wang

fasjem

A Fast and Scalable Joint Estimator for Learning Multiple
Related Sparse Gaussian Graphical Models

Yanjun Qi

exampleData function

<p>The format is:
List of 2 matrices
 $ : num [1:200, 1:100] -0.0982 -0.2417 -1.704 0.4 ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : NULL
  .. ..$ : NULL
 $ : num [1:200, 1:100] -0.161 0.41 0.17 0. ...
  ..- attr(*, "dimnames")=List of 2
  .. ..$ : NULL
  .. ..$ : NULL</p>

exampleData: A simulated toy dataset that includes 2 data matrices (about 2 related tasks).

Description

Usage

Arguments

Format

Examples