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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.
require(OpenMx)
#Simulate Data
set.seed(200)
rs=.5
xy <- mvtnorm::rmvnorm (1000, c(0,0), matrix(c(1,rs,rs,1),2,2))
testData <- xy
testData <- testData[, order(apply(testData, 2, var))[2:1]] #put the data columns in order from largest to smallest variance
# Note: Users do NOT have to re-order their data columns. This is only to make data generation the same on different operating systems.
selVars <- c('X','Y')
dimnames(testData) <- list(NULL, selVars)
summary(testData)
cov(testData)
#Fit Saturated Model with RawData and Matrices Input using Cholesky Decomposition
bivCorModel <- mxModel("bivCor",
mxMatrix(
type="Full",
nrow=2,
ncol=2,
free=c(T,T,F,T),
values=c(1,.2,0,1),
name="Chol"
),
mxAlgebra(
expression=Chol %*% t(Chol),
name="expCov"
),
mxMatrix(
type="Full",
nrow=1,
ncol=2,
free=T,
values=c(0,0),
name="expMean"
),
mxData(
observed=testData,
type="raw"
),
mxFitFunctionML(),mxExpectationNormal(
covariance="expCov",
means="expMean",
dimnames=selVars
)
)
bivCorFit <- mxRun(bivCorModel)
EM <- mxEval(expMean, bivCorFit)
EC <- mxEval(expCov, bivCorFit)
LL <- mxEval(objective, bivCorFit);
#Test for Covariance=Zero
bivCorModelSub <-mxModel(bivCorModel,
mxMatrix(
type="Full",
nrow=2,
ncol=2,
free=c(T,F,F,T),
values=c(1,0,0,1),
name="Chol"
)
)
bivCorFitSub <- mxRun(bivCorModelSub)
EMs <- mxEval(expMean, bivCorFitSub)
ECs <- mxEval(expCov, bivCorFitSub)
LLs <- mxEval(objective, bivCorFitSub);
Chi= LLs-LL;
LRT= rbind(LL,LLs,Chi); LRT
#Compare OpenMx results to Mx results (LL: likelihood; EC: expected covariance, EM: expected means)
omxCheckCloseEnough(LL, 5407.036, .001)
omxCheckCloseEnough(c(EC), c(1.0656, 0.4752, 0.4752, 0.9292), .001)
omxCheckCloseEnough(c(EM), c(0.0582, 0.0063), .001)
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