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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_PathCov.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
# Path style model input - Covariance matrix data input
#
# RevisionHistory:
# Hermine Maes -- 2009.10.08 updated & reformatted
# Ross Gore -- 2011.06.06 added Model, Data & Field metadata
# Hermine Maes -- 2014.11.02 piecewise specification
# -----------------------------------------------------------------------------
require(OpenMx)
# Load Library
# -----------------------------------------------------------------------------
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")
SimpleDataCov <- myRegDataCov[c("x","y"),c("x","y")]
SimpleDataMeans <- myRegDataMeans[c(2,3)]
myRegDataMeans<-c(0.05416, 2.57393)
# Prepare Data
# -----------------------------------------------------------------------------
# dataset
dataCov <- mxData( observed=SimpleDataCov, type="cov", numObs=100,
means=SimpleDataMeans )
# variance paths
varPaths <- mxPath( from=c("x", "y"), arrows=2,
free=TRUE, values = c(1, 1), labels=c("varx", "residual") )
# regression weights
regPaths <- mxPath( from="x", to="y", arrows=1,
free=TRUE, values=1, labels="beta1" )
# means and intercepts
means <- mxPath( from="one", to=c("x", "y"), arrows=1,
free=TRUE, values=c(1, 1), labels=c("meanx", "beta0") )
uniRegModel <- mxModel(model="Simple Regression Path Specification", type="RAM",
dataCov, manifestVars=c("x", "y"), varPaths, regPaths, means)
# Create an MxModel object
# -----------------------------------------------------------------------------
uniRegFit <- mxRun(uniRegModel)
summary(uniRegFit)
uniRegFit$output
omxCheckCloseEnough(coef(uniRegFit)[["beta0"]], 2.54776, 0.001)
omxCheckCloseEnough(coef(uniRegFit)[["beta1"]], 0.48312, 0.001)
omxCheckCloseEnough(coef(uniRegFit)[["residual"]], 0.672, 0.01)
omxCheckCloseEnough(coef(uniRegFit)[["meanx"]], 0.05412, 0.001)
omxCheckCloseEnough(coef(uniRegFit)[["varx"]], 1.10483, 0.001)
# Compare OpenMx results to Mx results
# -----------------------------------------------------------------------------
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