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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: MultiRegStd-OpenMx100214.R
# Author: Steven M. Boker
# Date: Sun Feb 14 12:25:16 EST 2010
#
# This program fits a multiple regression model to the
# multiData simulated data.
#
#
# ---------------------------------------------------------------------
# Revision History
# -- Sun Feb 14 12:25:21 EST 2010
# Created MultiRegStd-OpenMx100214.R.
#
# ---------------------------------------------------------------------
# ----------------------------------
# Read libraries and set options.
require(OpenMx)
# ----------------------------------
# Read the data and print descriptive statistics.
data(multiData1)
# ----------------------------------
# Build an OpenMx multiple regression model using y and x1
predictors <- c("x1", "x2", "x3", "x4")
outcomes <- c("y")
manifests <- names(multiData1)
multiData1Cov <- cov(multiData1)
multiRegModel <- mxModel("Multiple Regression of y on x1, x2, x3, and x4",
type="RAM",
manifestVars=manifests,
mxPath(from=predictors, to=outcomes,
arrows=1,
free=TRUE, values=.2,
labels=c("b1", "b2", "b3", "b4")),
mxPath(from=outcomes,
arrows=2,
free=TRUE, values=.8,
labels=c("VarE")),
mxPath(from=predictors, to=predictors,
# arrows=2, all=TRUE,
arrows=2, connect="unique.pairs",
free=TRUE, values=.2),
mxPath(from=manifests,
arrows=2,
free=TRUE, values=.8,
labels=c("VarX1", "VarX2", "VarX3", "VarX4", "VarE")),
mxData(observed=multiData1Cov, type="cov", numObs=500)
)
multiRegModelOut <- mxRun(multiRegModel, suppressWarnings=TRUE)
summary(multiRegModelOut)
# ----------------------------------
# check for correct values
expectVal <- c(0.04427, 0.30698, 0.39864, 0.47186, 1.13643, 0.58111,
1.5556, 0.63498, 0.56491, 2.10277, 0.59054, 0.43436, 0.65067, 2.55298, 0.53338)
expectSE <- c(0.037002, 0.029577, 0.025352, 0.022138, 0.072093, 0.065091,
0.098683, 0.074968, 0.084998, 0.133396, 0.080868, 0.091494, 0.107955,
0.161951, 0.033835)
# cat(deparse(round(multiRegModelOut$output$estimate, 5)))
omxCheckCloseEnough(expectVal, multiRegModelOut$output$estimate, 0.001)
omxCheckCloseEnough(expectSE,
as.vector(multiRegModelOut$output[['standardErrors']]), 0.001)
omxCheckCloseEnough(3020.43369, multiRegModelOut$output$minimum, 0.001)
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