simDFM {EGAnet}  R Documentation 
Function to simulate data following a dynamic factor model (DFM). Two DFMs are currently available: the direct autoregressive factor score model (Engle & Watson, 1981; Nesselroade, McArdle, Aggen, and Meyers, 2002) and the dynamic factor model with random walk factor scores.
simDFM(
variab,
timep,
nfact,
error,
dfm = c("DAFS", "RandomWalk"),
loadings,
autoreg,
crossreg,
var.shock,
cov.shock,
burnin = 1000
)
variab 
Number of variables per factor. 
timep 
Number of time points. 
nfact 
Number of factors. 
error 
Value to be used to construct a diagonal matrix Q. This matrix is p x p covariance matrix Q that will generate random errors following a multivariate normal distribution with mean zeros. The value provided is squared before constructing Q. 
dfm 
A string indicating the dynamical factor model to use. Current options are:

loadings 
Magnitude of the loadings. 
autoreg 
Magnitude of the autoregression coefficients. 
crossreg 
Magnitude of the crossregression coefficients. 
var.shock 
Magnitude of the random shock variance. 
cov.shock 
Magnitude of the random shock covariance 
burnin 
Number of n first samples to discard when computing the factor scores. Defaults to 1000. 
Hudson F. Golino <hfg9s at virginia.edu>
Engle, R., & Watson, M. (1981). A onefactor multivariate time series model of metropolitan wage rates. Journal of the American Statistical Association, 76(376), 774781.
Nesselroade, J. R., McArdle, J. J., Aggen, S. H., & Meyers, J. M. (2002). Dynamic factor analysis models for representing process in multivariate timeseries. In D. S. Moskowitz & S. L. Hershberger (Eds.), Multivariate applications book series. Modeling intraindividual variability with repeated measures data: Methods and applications, 235265.
## Not run:
\donttest{
# Estimate EGA network
data1 < simDFM(variab = 5, timep = 50, nfact = 3, error = 0.05,
dfm = "DAFS", loadings = 0.7, autoreg = 0.8,
crossreg = 0.1, var.shock = 0.18,
cov.shock = 0.36, burnin = 1000)
}
## End(Not run)