gen_tsGVAR {IVPP} | R Documentation |
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 xinkai.du.xd@gmail.com
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)