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.
diffee(C, D, lambda = 0.05, covType = "cov", intertwined = FALSE,
thre = "soft")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.
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.
A positive number. The hyperparameter controls the sparsity level of the matrices. The \(\lambda_n\) in the following section: Details.
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.
indicate whether to use intertwined covariance matrix
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 \(T_v\).
A matrix of the estimated sparse changes between two Gaussian Graphical Models
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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) https://arxiv.org/abs/1710.11223.
if labels are provided in the datalist as column names, result will contain labels (to be plotted)
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
# NOT RUN {
library(JointNets)
data(exampleData)
result = diffee(exampleData[[1]], exampleData[[2]], 0.45)
plot(result)
# }
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