output from the function simulation()

simulationresult

method name from any one of the JointNets methods

gm_method

a vector of lambda values for the JointNets method to run with

lambdas

extra parameters passed to the JointNets method such as lambda, epislon and etc, refer to each method for details (eg, ?simule)

return AUC score for JointNets method

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).

AUC: return AUC score for JointNets method

Description

Usage

Arguments

Value

Examples