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,
variation = FALSE
)
```

## 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:

--- Simulates data using the direct autoregressive factor score model. This is the default method`DAFS`

--- Simulates data using a dynamic factor model with random walk factor scores`RandomWalk`

- 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.

- variation
Boolean. Whether parameters should be varied. Defaults to

`FALSE`

. Set to`TRUE`

to add slight variation to all parameters

## 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.