#
# 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: OneFactorOrdinal_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
# -----------------------------------------------------------------------------
require(OpenMx)
# Load Library
# -----------------------------------------------------------------------------
data(myFADataRaw)
oneFactorOrd <- myFADataRaw[,c("z1","z2","z3")]
oneFactorOrd$z1 <- mxFactor(oneFactorOrd$z1, levels=c(0, 1))
oneFactorOrd$z2 <- mxFactor(oneFactorOrd$z2, levels=c(0, 1))
oneFactorOrd$z3 <- mxFactor(oneFactorOrd$z3, levels=c(0, 1, 2))
# Prepare Data
# -----------------------------------------------------------------------------
dataRaw <- mxData(oneFactorOrd, type="raw")
# asymmetric paths
matrA <- mxMatrix( type="Full", nrow=4, ncol=4,
free=c(F,F,F,T,
F,F,F,T,
F,F,F,T,
F,F,F,F),
values=c(0,0,0,1,
0,0,0,1,
0,0,0,1,
0,0,0,0),
labels=c(NA,NA,NA,"l1",
NA,NA,NA,"l2",
NA,NA,NA,"l3",
NA,NA,NA,NA),
byrow=TRUE, name="A" )
# symmetric paths
matrS <- mxMatrix( type="Symm", nrow=4, ncol=4,
free=FALSE,
values=diag(4),
labels=c("e1", NA, NA, NA,
NA,"e2", NA, NA,
NA, NA,"e3", NA,
NA, NA, NA, "varF1"),
byrow=TRUE, name="S" )
# filter matrix
matrF <- mxMatrix( type="Full", nrow=3, ncol=4,
free=FALSE, values=c(1,0,0,0, 0,1,0,0, 0,0,1,0),
byrow=TRUE, name="F" )
# means
matrM <- mxMatrix( type="Full", nrow=1, ncol=4,
free=FALSE, values=0,
labels=c("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("z1","z2","z3","F1"),
thresholds="thresh", threshnames=c("z1","z2","z3"))
funML <- mxFitFunctionML()
oneFactorOrdinalModel <- mxModel("Common Factor Model Matrix Specification",
dataRaw, matrA, matrS, matrF, matrM, thresh, exp, funML)
# Create an MxModel object
# -----------------------------------------------------------------------------
oneFactorOrdinalFit <- mxRun(oneFactorOrdinalModel)
# Fit the model with mxRun
# -----------------------------------------------------------------------------
summary(oneFactorOrdinalFit)
coef(oneFactorOrdinalFit)
# Print a summary of the results
# -----------------------------------------------------------------------------
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