Learn R Programming

fasjem (version 1.1.2)

plot.fasjem: Plotting functions for displaying the list of multiple graphs generated by the fasjem algorithm

Description

Plotting function for fasjem objects. This function plots either the shared graph, the task-specific networks, the networks or the neighborhood networks for a certain node. Please run demo(fasjem) to learn the basic functions provided by this package. For further details, please read the original paper: <http://proceedings.mlr.press/v54/wang17e/wang17e.pdf>.

Usage

# S3 method for fasjem
plot(x, type="graph", subID=NULL, index=NULL, ...)

Arguments

x

fasjem object

type

Plotting type. This argument defines which type of network(s) to plot. There are four options: "graph": plot the networks. The different colors represent the different graphs.

"share": plot the shared graph.

"sub": plot subject-specific networks.

"neighbor": plot the neighborhood networks for a given node. The different colors represent the different graphs.

subID

If type="sub", subID indicates to plot the task-specific network for the task whose index == subID.

index

If type="neighbor", index indicates the row number of the node to be investigated. This function plots its neighborhood subgraphs from each graph of the multiple graphs generated by fasjem algorithm.

...

Additional arguments to pass to plot function

Details

Plotting function for fasjem objects. It plots the results obtained from running fasjem algorithm.

References

Beilun Wang, Ji Gao, Yanjun Qi (2017). A Fast and Scalable Joint Estimator for Learning Multiple Related Sparse Gaussian Graphical Models. <http://proceedings.mlr.press/v54/wang17e/wang17e.pdf>

See Also

fasjem

Examples

Run this code
# NOT RUN {
  
# }
# NOT RUN {
    data(exampleData)
    results = fasjem(X = exampleData, method = "fasjem-g", 0.1, 0.1, 0.1, 0.05, 10)
    plot.fasjem(results)
    plot.fasjem(results, type="share")
    plot.fasjem(results, type="sub", subID=1)
    plot.fasjem(results, type="neighbor", index=50)
  
# }

Run the code above in your browser using DataLab