gen_tsGVAR: Generate time-series GVAR model for multiple (heterogeneous)...

In IVPP: Invariance Partial Pruning Test

Generate time-series GVAR model for multiple (heterogeneous) individuals

Description

This function generates time-series GVAR model for multiple individuals that demonstrates difference or simularity. Currently generating temporal and contemporaneous networks

Usage

gen_tsGVAR(n_node = 6, p_rewire_temp = 0.5, p_rewire_cont = 0.5, n_persons = 1)

Arguments

n_node	an integer denoting the number of nodes
p_rewire_temp	a numeric value between 0-1 denoting the extent of individual difference in the temporal network
p_rewire_cont	a numeric value between 0-1 denoting the extent of individual difference in the contemporaneous network
n_persons	an integer denoting the number of individuals to generate tsGVAR for

Details

beta can be transposed to obtain the temporal network; PDC is the partial directed correlation matrix, which is a standardized version of temporal network; kappa is the precision matrix denoting conditional (in)dependence, which is a inverse of covariance matrix denoting the (dependence) among variables; kappa can be further standardized to the contemporaneous networks (omega_zeta_within)

Value

A list of beta, PDC, kappa and contemporaneous networks

Author(s)

Xinkai Du Maintainer: Xinkai Du [e-mail]

Examples

library(IVPP)

# Generate the network
net_ls <- gen_tsGVAR(n_node = 6,
                     p_rewire_temp = 0.5,
                     p_rewire_cont = 0,
                     n_persons = 2)

IVPP documentation built on June 8, 2025, 11:09 a.m.