summary_network: The summary of the resulting network structures.

In HeteroGGM: Gaussian Graphical Model-Based Heterogeneity Analysis

The summary of the resulting network structures.

Description

Summarize the characteristics of the resulting network structures.

Usage

summary_network(opt_Mu_hat, opt_Theta_hat, data)

Arguments

opt_Mu_hat	A p * K0_hat matrix, the optional mean vectors of K0_hat subgroups.
opt_Theta_hat	n * p * K0_hat matrix, the optional precision matrices of K0_hat subgroups.
data	A n * p matrix, the design matrix.

Value

A list including the overlap of edges of different subgroups, the number of edges, and the names of connected nodes to each nodes in each subgroup.

Examples

data(example.data)
K <- 6
lambda <- genelambda.obo(nlambda1=5,lambda1_max=0.5,lambda1_min=0.1,
                         nlambda2=15,lambda2_max=1.5,lambda2_min=0.1,
                         nlambda3=10,lambda3_max=3.5,lambda3_min=0.5)

res <- GGMPF(lambda, example.data$data, K, initial.selection="K-means")
Theta_hat.list <- res$Theta_hat.list
Mu_hat.list <- res$Mu_hat.list
opt_num <- res$Opt_num
opt_Theta_hat <- Theta_hat.list[[opt_num]]
opt_Mu_hat <- Mu_hat.list[[opt_num]]
K_hat <- dim(opt_Theta_hat)[3]
K_hat

summ <- summary_network(opt_Mu_hat, opt_Theta_hat, example.data$data)
summ$Theta_summary$overlap
va_names <- c("1","6")
linked_node_names(summ, va_names, num_subgroup=1)
plot_network(summ, num_subgroup = c(1:K_hat), plot.mfrow = c(1,K_hat))

HeteroGGM documentation built on Oct. 11, 2023, 5:14 p.m.