diffee {diffee} R Documentation

## 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 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 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

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[], exampleData[], 0.45)
plot.diffee(result)

## End(Not run)


[Package diffee version 1.1.0 Index]