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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.
# ---------------------------------------------------------------------
# Program: RAMInverseTest.R
# Author: Michael D. Hunter
# Date: 2012.03.23
#
# This program tests the RAM inverse speedup using a latent variable
# regression example.
#
# ---------------------------------------------------------------------
# Revision History
# Fri Mar 23 22:45:52 Central Daylight Time 2012 -- Michael Hunter Created RAMInverseTest.R
# from Steven Boker's test IntroSEM-ThreeLatentMultipleRegTest1.R
# Sat Mar 24 01:40:52 Central Daylight Time 2012 -- Michael Hunter changed model to use ML instead of FIML
#
#
# ---------------------------------------------------------------------
#------------------------------------------------------------------------
#------------------------------------------------------------------------
#------------------------------------------------------------------------
# BEGIN COPIED SECTION FROM model/passing/IntroSEM-ThreeLatentMultipleRegTest1.R
# ----------------------------------
# Read libraries and set options.
require(OpenMx)
# ----------------------------------
# Read the data and print descriptive statistics.
data(latentMultipleRegExample1)
numberFactors <- 3
indicators <- names(latentMultipleRegExample1)
numberIndicators <- length(indicators)
totalVars <- numberIndicators + numberFactors
# ----------------------------------
# Build an Old-style RAM OpenMx single factor FIML model with fixed variance
latents <- paste("F", 1:numberFactors, sep="")
loadingLabels <- paste("b_F", rep(1:numberFactors, each=numberIndicators),
rep(indicators, numberFactors), sep="")
loadingLabels
uniqueLabels <- paste("U_", indicators, sep="")
meanLabels <- paste("M_", indicators, sep="")
factorVarLabels <- paste("Var_", latents, sep="")
latents1 <- latents[1]
indicators1 <- indicators[1:4]
loadingLabels1 <- paste("b_F1", indicators[1:4], sep="")
latents2 <- latents[2]
indicators2 <- indicators[5:8]
loadingLabels2 <- paste("b_F2", indicators[5:8], sep="")
latents3 <- latents[3]
indicators3 <- indicators[9:12]
loadingLabels3 <- paste("b_F3", indicators[9:12], sep="")
threeLatentOrthoRaw1 <- mxModel("threeLatentOrthogonal",
type="RAM",
manifestVars=indicators,
latentVars=latents,
mxPath(from=latents1, to=indicators1,
# arrows=1, all=TRUE,
arrows=1, connect="all.pairs",
free=TRUE, values=.2,
labels=loadingLabels1),
mxPath(from=latents2, to=indicators2,
# arrows=1, all=TRUE,
arrows=1, connect="all.pairs",
free=TRUE, values=.2,
labels=loadingLabels2),
mxPath(from=latents3, to=indicators3,
# arrows=1, all=TRUE,
arrows=1, connect="all.pairs",
free=TRUE, values=.2,
labels=loadingLabels3),
mxPath(from=latents1, to=indicators1[1],
arrows=1,
free=FALSE, values=1),
mxPath(from=latents2, to=indicators2[1],
arrows=1,
free=FALSE, values=1),
mxPath(from=latents3, to=indicators3[1],
arrows=1,
free=FALSE, values=1),
mxPath(from=indicators,
arrows=2,
free=TRUE, values=.8,
labels=uniqueLabels),
mxPath(from=latents,
arrows=2,
free=TRUE, values=.8,
labels=factorVarLabels),
mxPath(from="one", to=indicators,
arrows=1, free=TRUE, values=.1,
labels=meanLabels),
mxData(observed=cov(latentMultipleRegExample1), type="cov", numObs=nrow(latentMultipleRegExample1), means=colMeans(latentMultipleRegExample1))
)
threeLatentOrthoRaw1Out <- mxRun(threeLatentOrthoRaw1, suppressWarnings=TRUE)
summary(threeLatentOrthoRaw1Out)
# model 2
threeLatentObliqueRaw1 <- mxModel(threeLatentOrthoRaw1,
# mxPath(from=latents,to=latents,all=TRUE,
mxPath(from=latents,to=latents,connect="unique.pairs",
arrows=2,
free=TRUE, values=.3),
mxPath(from=latents,
arrows=2,
free=TRUE, values=.8,
labels=factorVarLabels),
name="threeLatentOblique"
)
threeLatentObliqueRaw1Out <- mxRun(threeLatentObliqueRaw1, suppressWarnings=TRUE)
summary(threeLatentObliqueRaw1Out)
# model 3
threeLatentMultipleReg1 <- mxModel(threeLatentOrthoRaw1,
mxPath(from="F1",to="F2",
arrows=2,
free=TRUE, values=.3),
mxPath(from=c("F1","F2"), to="F3",
arrows=1,
free=TRUE, values=.2,
labels=c("b1", "b2")),
name="threeLatentMultipleReg"
)
threeLatentMultipleReg1Out <- mxRun(threeLatentMultipleReg1, suppressWarnings=TRUE)
# END COPIED SECTION FROM models/passing/IntroSEM-ThreeLatentMultipleRegTest1.R
#------------------------------------------------------------------------
#------------------------------------------------------------------------
#------------------------------------------------------------------------
#------------------------------------------------------------------------
# Note that the A matrix is nilpotent of nontrivial order, 3
AM <- threeLatentMultipleReg1$A$values
AM
AM %*% AM
AM %*% AM %*% AM
#------------------------------------------------------------------------
# Turn off the RAM inverse speed up
threeLatentMultipleReg1B <- mxOption(threeLatentMultipleReg1, "RAM Inverse Optimization", "No")
brun <- mxRun(threeLatentMultipleReg1B) # This causes an error when the fast inverse is different from the slow
#------------------------------------------------------------------------
# Compare the run times of the same model using the RAM speed up or not
summary(threeLatentMultipleReg1Out)$wallTime
summary(brun)$wallTime
#------------------------------------------------------------------------
# Compare the estimated parameters of the same model using the speed up or not
omxCheckCloseEnough(threeLatentMultipleReg1Out$output$estimate, brun$output$estimate, epsilon=0.001)
#------------------------------------------------------------------------
# End
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