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#
# 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_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
# -----------------------------------------------------------------------------
require(OpenMx)
# Load Library
# -----------------------------------------------------------------------------
data(myFADataRaw)
# Prepare Data
# -----------------------------------------------------------------------------
myFADataRaw <- myFADataRaw[,c("x1","x2","x3","x4","x5","x6")]
oneFactorModel <- mxModel("parent",
mxAlgebra(-2 * sum(log(likelihoods.fitfunction)), name = "algObjective"),
mxFitFunctionAlgebra("algObjective"),
mxModel("likelihoods",
type="RAM",
mxData(
observed=myFADataRaw,
type="raw"
),
manifestVars=c("x1","x2","x3","x4","x5","x6"),
latentVars="F1",
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")
),
# residual variances
# -------------------------------------
mxPath(from="F1",
arrows=2,
free=TRUE,
values=1,
labels ="varF1"
),
# latent variance
# -------------------------------------
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")
),
# factor loadings
# -------------------------------------
mxPath(from="one",
to=c("x1","x2","x3","x4","x5","x6","F1"),
arrows=1,
free=c(TRUE,TRUE,TRUE,TRUE,TRUE,TRUE,FALSE),
values=c(1,1,1,1,1,1,0),
labels =c("meanx1","meanx2","meanx3","meanx4","meanx5","meanx6",NA)
),
# means
# -------------------------------------
mxExpectationRAM("A", "S", "F", "M"),
mxFitFunctionML(vector=TRUE)
) # close model 'likelihoods'
) #close model 'parent'
# 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
# -----------------------------------------------------------------------------
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