returngraph.kdiffnet

output generated from kdiffnet function (kdiffnet class)

type of graph. There are two options:<ul>
<li>"task" (difference graph)</li>
<li>"neighbour" (zoom into nodes in the difference graph specified further by parameter
"index" (node id)</li>
</ul>

type

neighbouroption

subID

determines which node(s) to zoom into when parameter "type" is "neighbour".
This parameter could either be an integer or vector of integers representing node ids
(zoom into one node or multiple nodes)

index

This function can return an igraph object from kdiffnet result for user to work with directly

An end-to-end package for learning multiple sparse Gaussian graphical models and nonparanormal models from Heterogeneous Data with Additional Knowledge. It is able to simulate multiple related graphs as well as produce samples drawn from them. Multiple state-of-the-art sparse Gaussian graphical model estimators are included to both multiple and difference estimation. Graph visualization is available in 2D as well as 3D, designed specifically for brain. Moreover, a set of evaluation metrics are integrated for easy exploration with model validity. Finally, classification using graphical model is achieved with Quadratic Discriminant Analysis. The package comes with multiple demos with datasets from various fields. Methods references: SIMULE (Wang B et al. (2017) <doi:10.1007/s10994-017-5635-7>), WSIMULE (Singh C et al. (2017) <arXiv:1709.04090v2>), DIFFEE (Wang B et al. (2018) <arXiv:1710.11223>), JEEK (Wang B et al. (2018) <arXiv:1806.00548>), JGL(Danaher P et al. (2012) <arXiv:1111.0324>) and kdiffnet (Sekhon A et al, preprint for publication).

returngraph.kdiffnet: return igraph object from kdiffnet result specified by user input

Description

Usage

Arguments

Value

Details

Examples