diffee {diffee} | R Documentation |
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>.
diffee(C, D, lambda = 0.05, covType = "cov", thre = "soft")
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 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. |
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. |
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) <arXiv:1710.11223>.
diffNet |
A matrix of the estimated sparse changes between two Gaussian Graphical Models |
Beilun Wang
Beilun Wang, Arshdeep Sekhon, Yanjun Qi (2018). Fast and Scalable Learning of Sparse Changes in High-Dimensional Gaussian Graphical Model Structure. <arXiv:1710.11223>
## Not run: data(exampleData) result = diffee(exampleData[[1]], exampleData[[2]], 0.45) plot.diffee(result) ## End(Not run)