Description Usage Arguments Details Value Author(s) References Examples
Estimate DIFFerential networks via an Elementary Estimator under a highdimensional 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 softthreshold function as T_v. If thre = "hard", it means that we choose hardthreshold function as T_v. 
The DIFFEE algorithm is a fast and scalable Learning algorithm of Sparse Changes in HighDimensional 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 HighDimensional 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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