diffee {diffee}

Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure

Description

Estimate DIFFerential networks via an Elementary Estimator under a high-dimensional situation. Please run demo(diffee) to learn the basic functions provided by this package. For further details, please read the original paper: Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018) <arXiv:1710.11223>.

Usage

diffee(C, D, lambda = 0.05, covType = "cov", 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. More details at <https://github.com/QData/DIFFEE>

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. More details at <https://github.com/QData/DIFFEE>

lambda

A positive number. The hyperparameter controls the sparsity level of the matrices. The \lambda_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.

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_{\Delta}||\Delta||_1

Subject to :

([T_v(\hat{\Sigma}_{d})]^{-1} - [T_v(\hat{\Sigma}_{c})]^{-1})||_{\infty} \le \lambda_n

Please also see the equation (2.11) in our paper. The \lambda_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) <arXiv:1710.11223>.

Value

diffNet

A matrix of the estimated sparse changes between two Gaussian Graphical Models

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. <arXiv:1710.11223>

Examples

## Not run: 
data(exampleData)
result = diffee(exampleData[[1]], exampleData[[2]], 0.45)
plot.diffee(result)

## End(Not run)