demo/OneFactorModel_PathCov.R

In OpenMx: Extended Structural Equation Modelling

#
#   Copyright 2007-2019 by the individuals mentioned in the source code history
#
#   Licensed under the Apache License, Version 2.0 (the "License");
#   you may not use this file except in compliance with the License.
#   You may obtain a copy of the License at
# 
#        http://www.apache.org/licenses/LICENSE-2.0
# 
#   Unless required by applicable law or agreed to in writing, software
#   distributed under the License is distributed on an "AS IS" BASIS,
#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
#   See the License for the specific language governing permissions and
#   limitations under the License.


# -----------------------------------------------------------------------------
# Program: OneFactorModel_PathCov.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 - Covariance matrix 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
# -----------------------------------------------------------------------------

myFADataCov<-matrix(
	c(0.997, 0.642, 0.611, 0.672, 0.637, 0.677,
	  0.642, 1.025, 0.608, 0.668, 0.643, 0.676,
	  0.611, 0.608, 0.984, 0.633, 0.657, 0.626,
	  0.672, 0.668, 0.633, 1.003, 0.676, 0.665,
	  0.637, 0.643, 0.657, 0.676, 1.028, 0.654,
	  0.677, 0.676, 0.626, 0.665, 0.654, 1.020),
	nrow=6,
	dimnames=list(
		c("x1","x2","x3","x4","x5","x6"),
		c("x1","x2","x3","x4","x5","x6"))
)

myFADataMeans <- c(2.988, 3.011, 2.986, 3.053, 3.016, 3.010)
names(myFADataMeans) <- c("x1","x2","x3","x4","x5","x6")
# Prepare Data
# -----------------------------------------------------------------------------
		
dataCov      <- mxData( observed=myFADataCov, type="cov", numObs=500, mean=myFADataMeans )
# 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",
                        dataCov, 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 
# -----------------------------------------------------------------------------