simModel: Create simModel from model specification and be ready for data analysis.

Description

This function will take model specification from SimSet that contains free parameters, starting values, and fixed values. It will transform the code to a specified SEM package and ready to analyze data.

Usage

simModel(object, ...)

Arguments

object

SimSet that provides model specification

...

Other values that will be explained specifically for each class

Value

SimModel that will be used for data analysis

Details in ...

start:SimRSet.c that saves all starting values in the model.
equalCon:SimEqualCon.c that save constraints specified by users. The default is no constraint.
package:Desired analysis package
estimator:The default isMLestimator. Other alternatives areGLS,WLS,MLM,MLF, andMLR. Check the sem function help file in thelavaanpackage for further details
auxiliary:The names or the index of the auxiliary variables in the data
indLab:The names of the variable in the model. The exogenous indicators should be listed first (from x1) and then endogenous indicators should be listed next (from y1).
factorLab:The names of the factors in the model. The exogenous factors should be listed first (from k1) and then endogenous factors should be listed next (from y1).

Examples

Run this code

loading <- matrix(0, 6, 2)
loading[1:3, 1] <- NA
loading[4:6, 2] <- NA
loadingValues <- matrix(0, 6, 2)
loadingValues[1:3, 1] <- 0.7
loadingValues[4:6, 2] <- 0.7
LX <- simMatrix(loading, loadingValues)
latent.cor <- matrix(NA, 2, 2)
diag(latent.cor) <- 1
RPH <- symMatrix(latent.cor, 0.5)
error.cor <- matrix(0, 6, 6)
diag(error.cor) <- 1
RTD <- symMatrix(error.cor)
CFA.Model <- simSetCFA(LX = LX, RPH = RPH, RTD = RTD)
SimModel <- simModel(CFA.Model)

library(lavaan)
loading <- matrix(0, 9, 3)
loading[1:3, 1] <- NA
loading[4:6, 2] <- NA
loading[7:9, 3] <- NA
HS.Model <- simParamCFA(LX = loading)
SimModel <- simModel(HS.Model, indLab=paste("x", 1:9, sep=""))
out <- run(SimModel, HolzingerSwineford1939)
summary(out)