Description Usage Arguments Value Author(s) Examples
The kdiffnet algorithm
1 2 3 
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. 
W 
known edge level additional knowledge. It is a square matrix of dimension p X p where p is the input dimension. 
g 
known node level additional knowledge. It is a vector of dimension 1 X p where p is the input dimension, each entry indicating membership of node to a group, 0 for a node belonging to no group. For example, in a dataset with dimension=3,g=c(0,1,1) indicates node 1 belongs to no group, and node 2 and node 3 belong to group index 1. 
epsilon 
A positive number. The hyperparameter controls the sparsity level of the groups in g of the difference matrix 
lambda 
A positive number. The hyperparameter controls the sparsity level of the difference matrix 
knowledgeType 
"EV": if use overlapping node and edge level additional knowledge,"E": if only edge level additional knowledge or "V": only group level knowledge 
gamma 
: A positive number. This hyperparameter is used in calculating each proximity during optimization 
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. 
rho 
A positive number. This hyperparameter controls the learning rate of the proximal gradient method. 
iterMax 
An integer. The max number of iterations in the optimization of the proximal algorithm 
$graphs 
A matrix of the estimated sparse changes between two Gaussian Graphical Models 
$share 
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Arshdeep Sekhon
1 2 3 4 5 6  library(JointNets)
data(exampleData)
result = kdiffnet(exampleData[[1]], exampleData[[2]],
W = matrix(1,20,20), g = rep(0,20),epsilon = 0.2,
lambda = 0.4,covType = "cov")
plot(result)

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