Description Usage Arguments Details Value Author(s) References Examples
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.
1 2 | diffee(C, D, lambda = 0.05, covType = "cov", intertwined = FALSE,
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. |
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. |
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)
$graphs |
A matrix of the estimated sparse changes between two Gaussian Graphical Models |
$share |
null |
Beilun Wang
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
1 2 3 4 | library(JointNets)
data(exampleData)
result = diffee(exampleData[[1]], exampleData[[2]], 0.45)
plot(result)
|
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