simulateData: Simulate Data From a Lavaan Model Syntax

In lavaan: Latent Variable Analysis

Simulate Data From a Lavaan Model Syntax

Description

Simulate data starting from a lavaan model syntax.

Usage

simulateData(model = NULL, model.type = "sem", meanstructure = FALSE, 
    int.ov.free = TRUE, int.lv.free = FALSE, 
    marker.int.zero = FALSE, conditional.x = FALSE, fixed.x = FALSE, 
    orthogonal = FALSE, std.lv = TRUE, auto.fix.first = FALSE, 
    auto.fix.single = FALSE, auto.var = TRUE, auto.cov.lv.x = TRUE, 
    auto.cov.y = TRUE, ..., sample.nobs = 500L, ov.var = NULL, 
    group.label = paste("G", 1:ngroups, sep = ""), skewness = NULL, 
    kurtosis = NULL, seed = NULL, empirical = FALSE, 
    return.type = "data.frame", return.fit = FALSE,
    debug = FALSE, standardized = FALSE)

Arguments

model	A description of the user-specified model. Typically, the model is described using the lavaan model syntax. See model.syntax for more information. Alternatively, a parameter table (eg. the output of the lavaanify() function) is also accepted.
model.type	Set the model type: possible values are "cfa", "sem" or "growth". This may affect how starting values are computed, and may be used to alter the terminology used in the summary output, or the layout of path diagrams that are based on a fitted lavaan object.
meanstructure	If TRUE, the means of the observed variables enter the model. If "default", the value is set based on the user-specified model, and/or the values of other arguments.
int.ov.free	If FALSE, the intercepts of the observed variables are fixed to zero.
int.lv.free	If FALSE, the intercepts of the latent variables are fixed to zero.
marker.int.zero	Logical. Only relevant if the metric of each latent variable is set by fixing the first factor loading to unity. If TRUE, it implies meanstructure = TRUE and std.lv = FALSE, and it fixes the intercepts of the marker indicators to zero, while freeing the means/intercepts of the latent variables. Only works correcly for single group, single level models.
conditional.x	If TRUE, we set up the model conditional on the exogenous ‘x’ covariates; the model-implied sample statistics only include the non-x variables. If FALSE, the exogenous ‘x’ variables are modeled jointly with the other variables, and the model-implied statistics refect both sets of variables. If "default", the value is set depending on the estimator, and whether or not the model involves categorical endogenous variables.
fixed.x	If TRUE, the exogenous ‘x’ covariates are considered fixed variables and the means, variances and covariances of these variables are fixed to their sample values. If FALSE, they are considered random, and the means, variances and covariances are free parameters. If "default", the value is set depending on the mimic option.
orthogonal	If TRUE, the exogenous latent variables are assumed to be uncorrelated.
std.lv	If TRUE, the metric of each latent variable is determined by fixing their variances to 1.0. If FALSE, the metric of each latent variable is determined by fixing the factor loading of the first indicator to 1.0.
auto.fix.first	If TRUE, the factor loading of the first indicator is set to 1.0 for every latent variable.
auto.fix.single	If TRUE, the residual variance (if included) of an observed indicator is set to zero if it is the only indicator of a latent variable.
auto.var	If TRUE, the (residual) variances of both observed and latent variables are set free.
auto.cov.lv.x	If TRUE, the covariances of exogenous latent variables are included in the model and set free.
auto.cov.y	If TRUE, the covariances of dependent variables (both observed and latent) are included in the model and set free.
...	additional arguments passed to the lavaan function.
sample.nobs	Number of observations. If a vector, multiple datasets are created. If return.type = "matrix" or return.type = "cov", a list of length(sample.nobs) is returned, with either the data or covariance matrices, each one based on the number of observations as specified in sample.nobs. If return.type = "data.frame", all datasets are merged and a group variable is added to mimic a multiple group dataset.
ov.var	The user-specified variances of the observed variables.
group.label	The group labels that should be used if multiple groups are created.
skewness	Numeric vector. The skewness values for the observed variables. Defaults to zero.
kurtosis	Numeric vector. The kurtosis values for the observed variables. Defaults to zero.
seed	Set random seed.
empirical	Logical. If TRUE, the implied moments (Mu and Sigma) specify the empirical not population mean and covariance matrix.
return.type	If "data.frame", a data.frame is returned. If "matrix", a numeric matrix is returned (without any variable names). If "cov", a covariance matrix is returned (without any variable names).
return.fit	If TRUE, return the fitted model that has been used to generate the data as an attribute (called "fit"); this may be useful for inspection.
debug	If TRUE, debugging information is displayed.
standardized	If TRUE, the residual variances of the observed variables are set in such a way such that the model implied variances are unity. This allows regression coefficients and factor loadings (involving observed variables) to be specified in a standardized metric.

Details

Model parameters can be specified by fixed values in the lavaan model syntax. If no fixed values are specified, the value zero will be assumed, except for factor loadings and variances, which are set to unity by default. By default, multivariate normal data are generated. However, by providing skewness and/or kurtosis values, nonnormal multivariate data can be generated, using the Vale & Maurelli (1983) method.

Value

The generated data. Either as a data.frame (if return.type="data.frame"), a numeric matrix (if return.type="matrix"), or a covariance matrix (if return.type="cov").

Examples

# specify population model
population.model <- ' f1 =~ x1 + 0.8*x2 + 1.2*x3
                      f2 =~ x4 + 0.5*x5 + 1.5*x6
                      f3 =~ x7 + 0.1*x8 + 0.9*x9

                      f3 ~ 0.5*f1 + 0.6*f2
                    '

# generate data
set.seed(1234)
myData <- simulateData(population.model, sample.nobs=100L)

# population moments
fitted(sem(population.model))

# sample moments
round(cov(myData), 3)
round(colMeans(myData), 3)

# fit model
myModel <- ' f1 =~ x1 + x2 + x3
             f2 =~ x4 + x5 + x6
             f3 =~ x7 + x8 + x9
             f3 ~ f1 + f2 '
fit <- sem(myModel, data=myData)
summary(fit)

lavaan documentation built on Sept. 27, 2024, 9:07 a.m.