lavaan (version 0.5-9)

simulateData: 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, 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")

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

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 pa

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.

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, v

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 and the variances of exogenous latent variables are included in the model and 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 matric

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).

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".

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.

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

                      f3 ~~ 4*f3
                      x1 ~~ 10*x1
                      x2 ~~ 5*x2
                      x3 ~~ 4*x3
                    '

# generate data
myData <- simulateData(population.model, sample.nobs=1000L)

# 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)