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#
# 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: UnivariateStd-OpenMx100214.R
# Author: Steven M. Boker
# Date: Sun Feb 14 12:13:20 EST 2010
#
# This program fits a univariate model to the multiData simulated data.
#
#
# ---------------------------------------------------------------------
# Revision History
# -- Sun Feb 14 12:13:16 EST 2010
# Created UnivariateStd-OpenMx100214.R.
#
# ---------------------------------------------------------------------
# ----------------------------------
# Read libraries and set options.
require(OpenMx)
# ----------------------------------
# Read the data and print descriptive statistics.
data(multiData1)
# ----------------------------------
# Build an OpenMx univariate regression model using y and x1
manifests <- c("x1", "y")
multiData1Cov <- cov(multiData1[,c(1,5)])
uniRegModel <- mxModel("Univariate Regression of y on x1",
type="RAM",
manifestVars=manifests,
mxPath(from="x1", to="y", arrows=1,
free=TRUE, values=.2, labels="b1"),
mxPath(from=manifests, arrows=2,
free=TRUE, values=.8, labels=c("VarX1", "VarE")),
mxData(observed=multiData1Cov, type="cov", numObs=500)
)
uniRegModelOut <- mxRun(uniRegModel, suppressWarnings=TRUE)
summary(uniRegModelOut)
#---------------------
# check values: uniRegModelOut
expectVal <- c(0.66918, 1.13643, 1.64763)
expectSE <-c(0.053902, 0.07209, 0.104518)
# cat(deparse(round(uniRegModelOut$output$estimate, 5)))
omxCheckCloseEnough(expectVal, uniRegModelOut$output$estimate, 0.001)
omxCheckCloseEnough(expectSE,
as.vector(uniRegModelOut$output[['standardErrors']]), 0.001)
omxCheckCloseEnough(1313.6145, uniRegModelOut$output$minimum, 0.001)
omxCheckEquals(uniRegModelOut$output$fitUnits, "-2lnL")
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