simDFM: Simulate data following a Dynamic Factor Model

In hfgolino/EGA: Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics

Simulate data following a Dynamic Factor Model

Description

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.

Usage

simDFM(
  variab,
  timep,
  nfact,
  error,
  dfm = c("DAFS", "RandomWalk"),
  loadings,
  autoreg,
  crossreg,
  var.shock,
  cov.shock,
  burnin = 1000
)

Arguments

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: DAFS — Simulates data using the direct autoregressive factor score model. This is the default method RandomWalk — Simulates data using a dynamic factor model with random walk factor scores
loadings	Magnitude of the loadings.
autoreg	Magnitude of the autoregression coefficients.
crossreg	Magnitude of the cross-regression 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.

Author(s)

Hudson F. Golino <hfg9s at virginia.edu>

References

Engle, R., & Watson, M. (1981). A one-factor multivariate time series model of metropolitan wage rates. Journal of the American Statistical Association, 76(376), 774-781.

Nesselroade, J. R., McArdle, J. J., Aggen, S. H., & Meyers, J. M. (2002). Dynamic factor analysis models for representing process in multivariate time-series. In D. S. Moskowitz & S. L. Hershberger (Eds.), Multivariate applications book series. Modeling intraindividual variability with repeated measures data: Methods and applications, 235-265.

Examples

## Not run: 
# 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 = 0.36,
cov.shock = 0.18, burnin = 1000)
## End(Not run)

hfgolino/EGA documentation built on Nov. 11, 2024, 9:28 p.m.