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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: MultivariateRegression_MatrixRaw.R
# Author: Ryne Estabrook
# Date: 2009.08.01
#
# ModelType: Regression
# DataType: Continuous
# Field: None
#
# Purpose:
# Multivariate Regression model to estimate effect of
# independent on dependent variables
# Matrix style model input - Raw data input
#
# RevisionHistory:
# Hermine Maes -- 2009.10.08 updated & reformatted
# Ross Gore -- 2011.06.15 added Model, Data & Field metadata
# Hermine Maes -- 2014.11.02 piecewise specification
# -----------------------------------------------------------------------------
require(OpenMx)
# Load Library
# -----------------------------------------------------------------------------
data(myRegDataRaw)
# Prepare Data
# -----------------------------------------------------------------------------
dataRaw <- mxData( observed=myRegDataRaw, type="raw" )
matrA <- mxMatrix( type="Full", nrow=4, ncol=4,
free= c(F,T,F,T, F,F,F,F, F,T,F,T, F,F,F,F),
values=c(0,1,0,1, 0,0,0,0, 0,1,0,1, 0,0,0,0),
labels=c(NA,"betawx",NA,"betawz",
NA, NA, NA, NA,
NA,"betayx",NA,"betayz",
NA, NA, NA, NA),
byrow=TRUE, name="A" )
matrS <- mxMatrix( type="Symm", nrow=4, ncol=4,
free=c(T,F,F,F, F,T,F,T, F,F,T,F, F,T,F,T),
values=c(1, 0,0, 0, 0, 1,0,.5, 0, 0,1, 0, 0,.5,0, 1),
labels=c("residualw", NA, NA, NA,
NA, "varx", NA, "covxz",
NA, NA, "residualy", NA,
NA, "covxz", NA, "varz"),
byrow=TRUE, name="S" )
matrF <- mxMatrix( type="Iden", nrow=4, ncol=4, name="F" )
matrM <- mxMatrix( type="Full", nrow=1, ncol=4,
free=c(T,T,T,T), values=c(0,0,0,0),
labels=c("betaw","meanx","betay","meanz"), name="M" )
exp <- mxExpectationRAM("A","S","F","M", dimnames=c("w","x","y","z") )
funML <- mxFitFunctionML()
multivariateRegModel <- mxModel("Multiple Regression Matrix Specification",
dataRaw, matrA, matrS, matrF, matrM, exp, funML)
# Create an MxModel object
# -----------------------------------------------------------------------------
multivariateRegFit<-mxRun(multivariateRegModel)
summary(multivariateRegFit)
multivariateRegFit$output
omxCheckCloseEnough(coef(multivariateRegFit)[["betay"]], 1.6332, 0.001)
omxCheckCloseEnough(coef(multivariateRegFit)[["betayx"]], 0.4246, 0.001)
omxCheckCloseEnough(coef(multivariateRegFit)[["betayz"]], 0.2260, 0.001)
omxCheckCloseEnough(coef(multivariateRegFit)[["residualy"]], 0.6267, 0.001)
omxCheckCloseEnough(coef(multivariateRegFit)[["betaw"]], 0.5139, 0.001)
omxCheckCloseEnough(coef(multivariateRegFit)[["betawx"]], -0.2310, 0.001)
omxCheckCloseEnough(coef(multivariateRegFit)[["betawz"]], 0.5122, 0.001)
omxCheckCloseEnough(coef(multivariateRegFit)[["residualw"]], 0.5914, 0.001)
omxCheckCloseEnough(coef(multivariateRegFit)[["varx"]], 1.1053, 0.001)
omxCheckCloseEnough(coef(multivariateRegFit)[["varz"]], 0.8275, 0.001)
omxCheckCloseEnough(coef(multivariateRegFit)[["covxz"]], 0.2862, 0.001)
omxCheckCloseEnough(coef(multivariateRegFit)[["meanx"]], 0.0542, 0.001)
omxCheckCloseEnough(coef(multivariateRegFit)[["meanz"]], 4.0611, 0.001)
# Compare OpenMx results to Mx results
# -----------------------------------------------------------------------------
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