model.lavaan {simsem}

Build the data generation template and analysis template from the lavaan result

Description

Creates a data generation and analysis template (lavaan parameter table) for simulations with the lavaan result. Model misspecification may be added into the template by a vector, a matrix, or a list of vectors or matrices (for multiple groups).

Usage

model.lavaan(object, std = FALSE, LY = NULL, PS = NULL, RPS = NULL, 
	TE = NULL, RTE = NULL, BE = NULL, VTE = NULL, VY = NULL, VPS = NULL, 
	VE=NULL, TY = NULL, AL = NULL, MY = NULL, ME = NULL, KA = NULL, 
	GA = NULL)

Arguments

object

A lavaan object to be used to build the data generation and analysis template.

std

If TRUE, use the resulting standardized parameters for data generation. If FALSE, use the unstandardized parameters for data generation.

LY

Model misspecification in factor loading matrix from endogenous factors to Y indicators (need to be a matrix or a list of matrices).

PS

Model misspecification in residual covariance matrix among endogenous factors (need to be a symmetric matrix or a list of symmetric matrices).

RPS

Model misspecification in residual correlation matrix among endogenous factors (need to be a symmetric matrix or a list of symmetric matrices).

TE

Model misspecification in measurement error covariance matrix among Y indicators (need to be a symmetric matrix or a list of symmetric matrices).

RTE

Model misspecification in measurement error correlation matrix among Y indicators (need to be a symmetric matrix or a list of symmetric matrices).

BE

Model misspecification in regression coefficient matrix among endogenous factors (need to be a symmetric matrix or a list of symmetric matrices).

VTE

Model misspecification in measurement error variance of indicators (need to be a vector or a list of vectors).

VY

Model misspecification in total variance of indicators (need to be a vector or a list of vectors). NOTE: Either measurement error variance or indicator variance is specified. Both cannot be simultaneously specified.

VPS

Model misspecification in residual variance of factors (need to be a vector or a list of vectors).

VE

Model misspecification in total variance of of factors (need to be a vector or a list of vectors). NOTE: Either residual variance of factors or total variance of factors is specified. Both cannot be simulatneously specified.

TY

Model misspecification in measurement intercepts of Y indicators. (need to be a vector or a list of vectors).

AL

Model misspecification in endogenous factor intercept (need to be a vector or a list of vectors).

MY

Model misspecification in overall Y indicator means. (need to be a vector or a list of vectors). NOTE: Either measurement intercept of indicator mean can be specified. Both cannot be specified simultaneously.

ME

Model misspecification in total mean of endogenous factors (need to be a vector or a list of vectors). NOTE: Either endogenous factor intercept or total mean of endogenous factor is specified. Both cannot be simultaneously specified.

KA

Model misspecification in regression coefficient matrix from covariates to indicators (need to be a matrix or a list of matrices). KA is applicable when exogenous covariates are specified only.

GA

Model misspecification in regression coefficient matrix from covariates to factors (need to be a matrix or a list of matrices). KA is applicable when exogenous covariates are specified only.

Value

SimSem object that contains the data generation template (@dgen) and analysis template (@pt).

Author(s)

Sunthud Pornprasertmanit ([e-mail])

See Also

• model To build data generation and data analysis template for simulation.

• sim for simulations using the SimSem template.

• generate To generate data using the SimSem template.

• analyze To analyze real or generated data using the SimSem template.

• draw To draw parameters using the SimSem template.

Examples

library(lavaan)
HS.model <- ' visual  =~ x1 + x2 + x3
             textual =~ x4 + x5 + x6
             speed   =~ x7 + x8 + x9 '

fit <- cfa(HS.model, data=HolzingerSwineford1939)

# Create data generation and data analysis model from lavaan
# Data generation is based on standardized parameters
datamodel1 <- model.lavaan(fit, std=TRUE)

# Data generation is based on unstandardized parameters
datamodel2 <- model.lavaan(fit, std=FALSE)

# Data generation model with misspecification on cross-loadings
crossload <- matrix("runif(1, -0.1, 0.1)", 9, 3)
crossload[1:3, 1] <- 0
crossload[4:6, 2] <- 0
crossload[7:9, 3] <- 0
datamodel3 <- model.lavaan(fit, std=TRUE, LY=crossload)