kdiffnet: Fast and Scalable Estimator for Using Additional Knowledge in...

In JointNets: End-to-End Sparse Gaussian Graphical Model Simulation, Estimation, Visualization, Evaluation and Application

Description

The kdiffnet algorithm

Usage

kdiffnet(C, D, W, g = rep(1, 100), epsilon = 1, lambda = 0.05,
  knowledgeType = "EV", gamma = 4, covType = "cov",
  intertwined = FALSE, thre = "soft", rho = 0.05, iterMax = 20)

Arguments

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 soft-threshold function as T_v. If thre = "hard", it means that we choose hard-threshold 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

Value

$graphs	A matrix of the estimated sparse changes between two Gaussian Graphical Models
$share	null

Author(s)

Arshdeep Sekhon

Examples

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)

JointNets documentation built on July 30, 2019, 1:02 a.m.