Nothing
#
# Copyright 2007-2018 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: OneFactorJoint_MatrixRawRAM.R
# Author: Ryne Estabrook
# Date: 2014.05.09
#
# ModelType: Factor
# DataType: Ordinal
# Field: None
#
# Purpose:
# One Factor model to estimate factor loadings, residual variances, means and thresholds
# RAM Matrix style model input - Raw data input
#
# RevisionHistory:
# Hermine Maes -- 2014.11.02 piecewise specification
# Michael Hunter -- 2016.10.20 improve format
#------------------------------------------------------------------------------
# Load Library
require(OpenMx)
#------------------------------------------------------------------------------
# Prepare Data
data(myFADataRaw)
oneFactorJoint <- myFADataRaw[,c("x1","x2","x3","z1","z2","z3")]
oneFactorJoint$z1 <- mxFactor(oneFactorJoint$z1, levels=c(0, 1))
oneFactorJoint$z2 <- mxFactor(oneFactorJoint$z2, levels=c(0, 1))
oneFactorJoint$z3 <- mxFactor(oneFactorJoint$z3, levels=c(0, 1, 2))
dataRaw <- mxData(observed=oneFactorJoint, type="raw")
#------------------------------------------------------------------------------
# Create an MxModel object
# asymmetric paths
matrA <- mxMatrix( type="Full", nrow=7, ncol=7,
free=c(rep(c(F,F,F,F,F,F,T),6),rep(F,7)),
values=c(rep(c(0,0,0,0,0,0,1),6),rep(F,7)),
labels=rbind(cbind(matrix(NA,6,6),matrix(paste("l",1:6,sep=""),6,1)),
matrix(NA,1,7)),
byrow=TRUE, name="A" )
# symmetric paths
labelsS <- matrix(NA,7,7); diag(labelsS) <- c(paste("e",1:6,sep=""),"varF1")
matrS <- mxMatrix( type="Symm", nrow=7, ncol=7,
free= rbind(cbind(matrix(as.logical(diag(3)),3,3),matrix(F,3,4)),
matrix(F,4,7)),
values=diag(7), labels=labelsS, byrow=TRUE, name="S" )
# filter matrix
matrF <- mxMatrix( type="Full", nrow=6, ncol=7,
free=FALSE, values=cbind(diag(6),matrix(0,6,1)),
byrow=TRUE, name="F" )
# means
matrM <- mxMatrix( type="Full", nrow=1, ncol=7,
free=c(T,T,T,F,F,F,F), values=c(1,1,1,0,0,0,0),
labels=c("meanx1","meanx2","meanx3","meanz1","meanz2","meanz3",NA),
name="M" )
thresh <- mxMatrix( type="Full", nrow=2, ncol=3,
free=c(TRUE,TRUE,TRUE,FALSE,FALSE,TRUE),
values=c(-1,0,-.5,NA,NA,1.2), byrow=TRUE, name="thresh" )
exp <- mxExpectationRAM("A","S","F","M",
dimnames=c("x1","x2","x3","z1","z2","z3","F1"),
thresholds="thresh", threshnames=c("z1","z2","z3"))
funML <- mxFitFunctionML()
oneFactorJointModel <- mxModel("Common Factor Model Matrix Specification",
dataRaw, matrA, matrS, matrF, matrM, thresh, exp, funML)
#------------------------------------------------------------------------------
# Fit the model with mxRun
oneFactorJointFit <- mxRun(oneFactorJointModel)
#------------------------------------------------------------------------------
# Print a summary of the results
summary(oneFactorJointFit)
coef(oneFactorJointFit)
#------------------------------------------------------------------------------
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