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#
# Copyright 2007-2021 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: BivariateStd-OpenMx100214.R
# Author: Steven M. Boker
# Date: Sun Feb 14 12:12:17 EST 2010
#
# This program fits a bivariate model to the multiData simulated data.
#
#
# ---------------------------------------------------------------------
# Revision History
# -- Sun Feb 14 12:12:15 EST 2010
# Created BivariateStd-OpenMx100214.R.
#
# ---------------------------------------------------------------------
# ----------------------------------
# Read libraries and set options.
require(OpenMx)
# ----------------------------------
# Read the data and print descriptive statistics.
data(multiData1)
# ----------------------------------
# Build an OpenMx bivariate regression model using y and x1
manifests <- c("x1", "x2", "y")
multiData1Cov <- cov(multiData1[,c(1,2,5)])
biRegModel <- mxModel("Bivariate Regression of y on x1 and x2",
type="RAM",
manifestVars=manifests,
mxPath(from=c("x1","x2"), to="y",
arrows=1,
free=TRUE, values=.2, labels=c("b1", "b2")),
mxPath(from=manifests,
arrows=2,
free=TRUE, values=.8,
labels=c("VarX1", "VarX2", "VarE")),
mxPath(from="x1", to="x2",
arrows=2,
free=TRUE, values=.2,
labels=c("CovX1X2")),
mxData(observed=multiData1Cov, type="cov", numObs=500)
)
biRegModelOut <- mxRun(biRegModel)
omxCheckError(confint(biRegModelOut, parm="foobar"), "Parameter 'foobar' not recognized")
ci <- confint(biRegModelOut)
brmSum <- summary(biRegModelOut)
omxCheckCloseEnough(brmSum$CFI, 1, 1e-5)
omxCheckCloseEnough(brmSum$TLI, 1, 1e-6)
omxCheckCloseEnough(brmSum$RMSEA, 0, 1e-6)
omxCheckTrue(all(is.na(brmSum$RMSEACI)))
# ----------------------------------
# check for correct values
expectVal <- c(0.44791, 0.43271, 1.13643, 0.58111, 1.5556, 1.41199 )
expectSE <- c(0.0555, 0.0474, 0.0721, 0.0651, 0.0987, 0.0896)
# cat(deparse(round(biRegModelOut$output$estimate, 5)))
omxCheckCloseEnough(expectVal, biRegModelOut$output$estimate, 0.001)
omxCheckCloseEnough(expectSE,
as.vector(biRegModelOut$output[['standardErrors']]), 0.001)
omxCheckCloseEnough(1851.391, biRegModelOut$output$minimum, 0.001)
omxCheckEquals(brmSum$optimizerEngine, mxOption(NULL, "Default optimizer"))
omxCheckCloseEnough(unlist(ci['b1',]), c(.339, .556), .01)
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