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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: SimpleRegression_MatrixRaw.R
# Author: Ryne Estabrook
# Date: 2009.08.01
#
# ModelType: Regression
# DataType: Continuous
# Field: None
#
# Purpose:
# Simple Regression model to estimate effect of
# independent on dependent variables
# Matrix style model input - Raw data input
#
# RevisionHistory:
# Hermine Maes -- 10 08 2009 updated & reformatted
# Ross Gore -- 06 06 2011 added Model, Data & Field metadata
# Hermine Maes -- 2014.11.02 piecewise specification
# -----------------------------------------------------------------------------
require(OpenMx)
# Load Library
# -----------------------------------------------------------------------------
data(myRegDataRaw)
SimpleDataRaw <- myRegDataRaw[,c("x","y")]
# Prepare Data
# -----------------------------------------------------------------------------
dataRaw <- mxData( observed=SimpleDataRaw, type="raw" )
matrA <- mxMatrix( type="Full", nrow=2, ncol=2,
free=c(F,F,T,F), values=c(0,0,1,0),
labels=c(NA,NA,"beta1",NA), byrow=TRUE, name="A" )
matrS <- mxMatrix( type="Symm", nrow=2, ncol=2,
free=c(T,F,F,T), values=c(1,0,0,1),
labels=c("varx",NA,NA,"residual"), byrow=TRUE, name="S" )
matrF <- mxMatrix( type="Iden", nrow=2, ncol=2, name="F" )
matrM <- mxMatrix( type="Full", nrow=1, ncol=2,
free=c(T,T), values=c(0,0),
labels=c("meanx","beta0"), name="M")
expRAM <- mxExpectationRAM("A","S","F","M", dimnames=c("x","y"))
funML <- mxFitFunctionML()
uniRegModel <- mxModel("Simple Regression Matrix Specification",
dataRaw, matrA, matrS, matrF, matrM, expRAM, funML)
# Create an MxModel object
# -----------------------------------------------------------------------------
uniRegFit<-mxRun(uniRegModel)
# Fit the model with mxRun
# -----------------------------------------------------------------------------
summary(uniRegFit)
# Print a summary of the results
# -----------------------------------------------------------------------------
uniRegFit$output
# Print the outputs populated by mxRun
# -----------------------------------------------------------------------------
omxCheckCloseEnough(coef(uniRegFit)[["beta0"]], 2.5478, 0.001)
omxCheckCloseEnough(coef(uniRegFit)[["beta1"]], 0.4831, 0.001)
omxCheckCloseEnough(coef(uniRegFit)[["residual"]], 0.6652, 0.001)
omxCheckCloseEnough(coef(uniRegFit)[["meanx"]], 0.0542, 0.001)
omxCheckCloseEnough(coef(uniRegFit)[["varx"]], 1.1053, 0.001)
# Compare OpenMx results to Mx results
# -----------------------------------------------------------------------------
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