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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.
# -----------------------------------------------------------------------------
# Adapted from demo/BivariateSaturated_PathRaw.R
# -----------------------------------------------------------------------------
require(OpenMx)
require(MASS)
# Load Libraries
# -----------------------------------------------------------------------------
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: to fix an inconsistency with the mvtnorm::rmvnorm function in the MASS package.
selVars <- c("X","Y")
dimnames(testData) <- list(NULL, selVars)
summary(testData)
cov(testData)
# Simulate Data
# -----------------------------------------------------------------------------
bivSatModel2 <- mxModel("bivSat2",
manifestVars= selVars,
mxPath(
from=c("X", "Y"),
arrows=2,
free=T,
values=1,
lbound=.01,
labels=c("varX","varY")
),
mxPath(
from="X",
to="Y",
arrows=2,
free=T,
values=.2,
lbound=.01,
labels="covXY"
),
mxPath(
from="one",
to=c("X", "Y"),
arrows=1,
free=T),
mxData(
observed=testData,
type="raw",
),
type="RAM"
)
bivSatFit2 <- mxRun(bivSatModel2)
EM2 <- mxEval(M, bivSatFit2)
EC2 <- mxEval(S, bivSatFit2)
LL2 <- mxEval(objective, bivSatFit2)
SL2 <- summary(bivSatFit2)$SaturatedLikelihood
Chi2 <- LL2-SL2
# Example 2: Saturated Model with Raw Data and Path input
# -----------------------------------------------------------------------------
omxCheckCloseEnough(LL2, 5407.037,.001)
omxCheckCloseEnough(c(EC2),c(1.066, 0.475, 0.475, 0.929),.001)
omxCheckCloseEnough(c(EM2),c(0.058, 0.006),.001)
#Now, make sure the following runs:
bivSatFit2 <- mxBootstrap(bivSatFit2)
mxBootstrapStdizeRAMpaths(bivSatFit2)
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