draw, called a code{paramSet}. This function is used internally to create data, and is available publicly for accessibility and debugging.createData(paramSet, n, indDist=NULL, sequential=FALSE, facDist=NULL,
errorDist=NULL, indLab=NULL, modelBoot=FALSE, realData=NULL)draw.SimDataDist sequential is FALSE.TRUE, use a sequential method to create data such that the data from factor are generated first and apply to a set of equations to obtain the data of indicators. If FALSE, create data directly from model-implied mean and covarSimDataDist sequential is TRUE.SimDataDist indicating the distribution of errors. If a single SimDataDist is specified, each error will be genrated with that distribution.x1, x2, ... xN.readData.mvrnorm function (from the MASS package) by Paul E. Johnson to create data from model implied covariance matrix if the data distribution object (SimDataDist mvrnorm function such that the data generated with the sample sizes of n and n + k (where k > 0) will be replicable in the first n rows.
It the data distribution object is specified, the copula model is used. If the copula argument is not specified in the data distribution object, the naive Gaussian copula is used. The correlation matrix is direct applied to the multivariate Gaussian copula. The correlation matrix will be equivalent to the Spearman's correlation (rank correlation) of the resulting data. If the copula argument is specified, such as ellipCopula, normalCopula, or archmCopula, the data-transformation method from Mair, Satorra, and Bentler (2012) is used. In brief, the data ($X$) are created from the multivariate copula. The covariance from the generated data is used as the starting point ($S$). Then, the target data ($Y$) with the target covariance as model-implied covariance matrix ($\Sigma_0$) can be created:
$$Y = XS^{-1/2}\Sigma^{1/2}_0.$$
See bindDist for further details.
For the model-based bootstrap, the transformation proposed by Yung & Bentler (1996) is used. This procedure is the expansion from the Bollen and Stine (1992) bootstrap including a mean structure. The model-implied mean vector and covariance matrix with trivial misspecification will be used in the model-based bootstrap if misspec is specified. See page 133 of Bollen and Stine (1992) for a reference.
Internally, parameters are first drawn, and data is then created from these parameters. Both of these steps are available via the draw and createData functions respectively.loading <- matrix(0, 6, 2)
loading[1:3, 1] <- NA
loading[4:6, 2] <- NA
LY <- bind(loading, 0.7)
latent.cor <- matrix(NA, 2, 2)
diag(latent.cor) <- 1
RPS <- binds(latent.cor, 0.5)
RTE <- binds(diag(6))
VY <- bind(rep(NA,6),2)
CFA.Model <- model(LY = LY, RPS = RPS, RTE = RTE, modelType = "CFA")
# Draw a parameter set for data generation.
param <- draw(CFA.Model)
# Generate data from the first group in the paramList.
dat <- createData(param[[1]], n = 200)Run the code above in your browser using DataLab