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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: MultipleRegression_MatrixCov.R
# Author: Ryne Estabrook
# Date: 2009.08.01
#
# ModelType: Regression
# DataType: Continuous
# Field: None
#
# Purpose:
# Multiple Regression model to estimate effect of independent
# on dependent variables
# Matrix style model input - Covariance matrix data input
#
# RevisionHistory:
# Hermine Maes -- 2009.10.08 updated & reformatted
# Ross Gore -- 2011.06.15 added Model, Data & Field
# Hermine Maes -- 2014.11.02 piecewise specification
# -----------------------------------------------------------------------------
require(OpenMx)
# Load Libraries
# -----------------------------------------------------------------------------
myRegDataCov <- matrix(
c(0.808,-0.110, 0.089, 0.361,
-0.110, 1.116, 0.539, 0.289,
0.089, 0.539, 0.933, 0.312,
0.361, 0.289, 0.312, 0.836),
nrow=4,
dimnames=list(
c("w","x","y","z"),
c("w","x","y","z"))
)
myRegDataMeans <- c(2.582, 0.054, 2.574, 4.061)
names(myRegDataMeans) <- c("w","x","y","z")
MultipleDataCov <- myRegDataCov[c("x","y","z"),c("x","y","z")]
MultipleDataMeans <- myRegDataMeans[c(2,3,4)]
# Prepare Data
# -----------------------------------------------------------------------------
dataCov <- mxData( observed=MultipleDataCov, type="cov", numObs=100,
mean=MultipleDataMeans )
matrA <- mxMatrix( type="Full", nrow=3, ncol=3,
free= c(F,F,F, T,F,T, F,F,F),
values=c(0,0,0, 1,0,1, 0,0,0),
labels=c(NA,NA,NA, "betax",NA,"betaz", NA,NA,NA),
byrow=TRUE, name="A" )
matrS <- mxMatrix( type="Symm", nrow=3, ncol=3,
free=c(T,F,T, F,T,F, T,F,T),
values=c(1,0,.5, 0,1,0, .5,0,1),
labels=c("varx",NA,"covxz", NA,"residual",NA, "covxz",NA,"varz"),
byrow=TRUE, name="S" )
matrF <- mxMatrix( type="Iden", nrow=3, ncol=3, name="F" )
matrM <- mxMatrix( type="Full", nrow=1, ncol=3,
free=c(T,T,T), values=c(0,0,0),
labels=c("meanx","beta0","meanz"), name="M" )
exp <- mxExpectationRAM("A","S","F","M", dimnames=c("x","y","z") )
funML <- mxFitFunctionML()
multiRegModel <- mxModel("Multiple Regression Matrix Specification",
dataCov, matrA, matrS, matrF, matrM, exp, funML)
# Create an MxModel object
# -----------------------------------------------------------------------------
multiRegFit <- mxRun(multiRegModel)
summary(multiRegFit)
multiRegFit$output
omxCheckCloseEnough(coef(multiRegFit)[["beta0"]], 1.6312, 0.001)
omxCheckCloseEnough(coef(multiRegFit)[["betax"]], 0.4243, 0.001)
omxCheckCloseEnough(coef(multiRegFit)[["betaz"]], 0.2265, 0.001)
omxCheckCloseEnough(coef(multiRegFit)[["residual"]], 0.6272, 0.001)
omxCheckCloseEnough(coef(multiRegFit)[["varx"]], 1.1040, 0.001)
omxCheckCloseEnough(coef(multiRegFit)[["varz"]], 0.8276, 0.001)
omxCheckCloseEnough(coef(multiRegFit)[["covxz"]], 0.2861, 0.001)
omxCheckCloseEnough(coef(multiRegFit)[["meanx"]], 0.0540, 0.001)
omxCheckCloseEnough(coef(multiRegFit)[["meanz"]], 4.0610, 0.001)
# Compare OpenMx results to Mx results
# -----------------------------------------------------------------------------
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