demo/OneFactorModel_PathRaw.R

In OpenMx: Extended Structural Equation Modelling

#
#   Copyright 2007-2019 by the individuals mentioned in the source code history
#
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# -----------------------------------------------------------------------------
# Program: OneFactorModel_PathRaw.R  
# Author: Ryne Estabrook
# Date: 2009.08.01 
#
# ModelType: Factor
# DataType: Continuous
# Field: None
#
# Purpose:
#      One Factor model to estimate factor loadings, residual variances and means
#      Path style model input - Raw data input
#
# RevisionHistory:
#      Hermine Maes -- 2009.10.08 updated & reformatted
#      Ross Gore -- 2011.06.06	added Model, Data & Field metadata
#      Hermine Maes -- 2014.11.02 piecewise specification
# -----------------------------------------------------------------------------

require(OpenMx)
# Load Library
# -----------------------------------------------------------------------------

data(myFADataRaw)
# Prepare Data
# -----------------------------------------------------------------------------

myFADataRaw <- myFADataRaw[,c("x1","x2","x3","x4","x5","x6")]

dataRaw      <- mxData( observed=myFADataRaw, type="raw" )
# residual variances
resVars      <- mxPath( from=c("x1","x2","x3","x4","x5","x6"), arrows=2,
                        free=TRUE, values=c(1,1,1,1,1,1),
                        labels=c("e1","e2","e3","e4","e5","e6") ) 
# latent variance
latVar       <- mxPath( from="F1", arrows=2,
                        free=TRUE, values=1, labels ="varF1" )
# factor loadings	
facLoads     <- mxPath( from="F1", to=c("x1","x2","x3","x4","x5","x6"), arrows=1,
                        free=c(FALSE,TRUE,TRUE,TRUE,TRUE,TRUE), values=c(1,1,1,1,1,1),
                        labels =c("l1","l2","l3","l4","l5","l6") )
# means
means        <- mxPath( from="one", to=c("x1","x2","x3","x4","x5","x6","F1"), arrows=1,
                        free=c(T,T,T,T,T,T,FALSE), values=c(1,1,1,1,1,1,0),
                        labels =c("meanx1","meanx2","meanx3",
                                  "meanx4","meanx5","meanx6",NA) ) 

oneFactorModel <- mxModel("Common Factor Model Path Specification", type="RAM",
                        manifestVars=c("x1","x2","x3","x4","x5","x6"), latentVars="F1",
                        dataRaw, resVars, latVar, facLoads, means)
# Create an MxModel object
# -----------------------------------------------------------------------------

oneFactorFit <- mxRun(oneFactorModel)      

summary(oneFactorFit)
coef(oneFactorFit)

omxCheckCloseEnough(coef(oneFactorFit)[["l2"]], 0.999, 0.01)
omxCheckCloseEnough(coef(oneFactorFit)[["l3"]], 0.959, 0.01)
omxCheckCloseEnough(coef(oneFactorFit)[["l4"]], 1.028, 0.01)
omxCheckCloseEnough(coef(oneFactorFit)[["l5"]], 1.008, 0.01)
omxCheckCloseEnough(coef(oneFactorFit)[["l6"]], 1.021, 0.01)
omxCheckCloseEnough(coef(oneFactorFit)[["varF1"]], 0.645, 0.01)
omxCheckCloseEnough(coef(oneFactorFit)[["e1"]], 0.350, 0.01)
omxCheckCloseEnough(coef(oneFactorFit)[["e2"]], 0.379, 0.01)
omxCheckCloseEnough(coef(oneFactorFit)[["e3"]], 0.389, 0.01)
omxCheckCloseEnough(coef(oneFactorFit)[["e4"]], 0.320, 0.01)
omxCheckCloseEnough(coef(oneFactorFit)[["e5"]], 0.370, 0.01)
omxCheckCloseEnough(coef(oneFactorFit)[["e6"]], 0.346, 0.01)
omxCheckCloseEnough(coef(oneFactorFit)[["meanx1"]], 2.988, 0.01)
omxCheckCloseEnough(coef(oneFactorFit)[["meanx2"]], 3.011, 0.01)
omxCheckCloseEnough(coef(oneFactorFit)[["meanx3"]], 2.986, 0.01)
omxCheckCloseEnough(coef(oneFactorFit)[["meanx4"]], 3.053, 0.01)
omxCheckCloseEnough(coef(oneFactorFit)[["meanx5"]], 3.016, 0.01)
omxCheckCloseEnough(coef(oneFactorFit)[["meanx6"]], 3.010, 0.01)
# Compare OpenMx results to Mx results 
# -----------------------------------------------------------------------------