diffee: Fast and Scalable Learning of Sparse Changes in...

In JointNets: End-to-End Sparse Gaussian Graphical Model Simulation, Estimation, Visualization, Evaluation and Application

Description

Estimate DIFFerential networks via an Elementary Estimator under a high-dimensional situation. Please run demo(diffee) to learn the basics. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018) https://arxiv.org/abs/1710.11223.

Usage

diffee(C, D, lambda = 0.05, covType = "cov", intertwined = FALSE,
  thre = "soft")

Arguments

C	A input matrix for the 'control' group. It can be data matrix or covariance matrix. If C is a symmetric matrix, the matrices are assumed to be covariance matrix.
D	A input matrix for the 'disease' group. It can be data matrix or covariance matrix. If D is a symmetric matrix, the matrices are assumed to be covariance matrix.
lambda	A positive number. The hyperparameter controls the sparsity level of the matrices. The λ_n in the following section: Details.
covType	A parameter to decide which Graphical model we choose to estimate from the input data. If covType = "cov", it means that we estimate multiple sparse Gaussian Graphical models. This option assumes that we calculate (when input X represents data directly) or use (when X elements are symmetric representing covariance matrices) the sample covariance matrices as input to the simule algorithm. If covType = "kendall", it means that we estimate multiple nonparanormal Graphical models. This option assumes that we calculate (when input X represents data directly) or use (when X elements are symmetric representing correlation matrices) the kendall's tau correlation matrices as input to the simule algorithm.
intertwined	indicate whether to use intertwined covariance matrix
thre	A parameter to decide which threshold function to use for T_v. If thre = "soft", it means that we choose soft-threshold function as T_v. If thre = "hard", it means that we choose hard-threshold function as T_v.

Details

The DIFFEE algorithm is a fast and scalable Learning algorithm of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure. It solves the following equation:

\min\limits_{Δ}||Δ||_1

Subject to :

([T_v(\hat{Σ}_{d})]^{-1} - [T_v(\hat{Σ}_{c})]^{-1})||_{∞} ≤ λ_n

Please also see the equation (2.11) in our paper. The λ_n is the hyperparameter controlling the sparsity level of the matrix and it is the lambda in our function. For further details, please see our paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018) https://arxiv.org/abs/1710.11223.

if labels are provided in the datalist as column names, result will contain labels (to be plotted)

Value

$graphs	A matrix of the estimated sparse changes between two Gaussian Graphical Models
$share	null

Author(s)

Beilun Wang

References

Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018). Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure. https://arxiv.org/abs/1710.11223

Examples

library(JointNets)
data(exampleData)
result = diffee(exampleData[[1]], exampleData[[2]], 0.45)
plot(result)

JointNets documentation built on July 30, 2019, 1:02 a.m.