mlVARsim: Simulates an mlVAR model and data

In mlVAR: Multi-Level Vector Autoregression

Simulates an mlVAR model and data

Description

Simulates an mlVAR model and data with a random variance-covariance matrix for the random effects.

Usage

mlVARsim(nPerson = 10, nNode = 5, nTime = 100, lag = 1, thetaVar = rep(1,nNode),
DF_theta = nNode * 2, mu_SD = c(1, 1), init_beta_SD = c(0.1, 1), fixedMuSD = 1, 
shrink_fixed = 0.9, shrink_deviation = 0.9)

Arguments

nPerson	Number of subjects
nNode	Number of variables
nTime	Number of observations per person
lag	The maximum lag to be used
thetaVar	Contemporaneous fixed effect variances
DF_theta	Degrees of freedom in simulating person-specific contemporaneous covariances (e.g., the individual differences in contemporaneous effects)
mu_SD	Range of standard deviation for the means
init_beta_SD	Initial range of standard deviations for the temporal effects
fixedMuSD	Standard deviation used in sampling the fixed effects
shrink_fixed	Shrinkage factor for shrinking the fixed effects if the VAR model is not stationary
shrink_deviation	Shrinkage factor for shrinking the random effects variance if the VAR model is not stationary

Author(s)

Sacha Epskamp ([e-mail])

mlVAR documentation built on May 29, 2024, 2:15 a.m.