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#
# Copyright 2007-2021 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.
#------------------------------------------------------------------------------
# Author: Michael D. Hunter
# Date: 2012.10.02
# Filename: wlsContTest.R
# Purpose: Test the mxDataWLS function for continuous-only data in an
# OpenMx model.
#------------------------------------------------------------------------------
#------------------------------------------------------------------------------
# Revision history
# Wed 20 Aug 2014 13:25:04 Central Daylight Time -- Michael Hunter modified to use mxDataWLS.
#--------------------------------------
# Needed packages
library(testthat)
require(OpenMx)
data(Bollen)
suppressWarnings(RNGversion("3.5"))
set.seed(1)
got <- mxGenerateData(Bollen[, 1:8], nrows=10)
omxCheckEquals(nrow(got), 10)
#--------------------------------------
# Set up model matrices
manvar <- names(Bollen[, 1:8])
lval <- matrix(
c(1, 0,
1, 0,
1, 0,
1, 0,
0, 1,
0, 1,
0, 1,
0, 1),
byrow=TRUE,
ncol=2, nrow=8)
lfre <- matrix(as.logical(lval), ncol=2)
lfre[1, 1] <- FALSE
lfre[5, 2] <- FALSE
llab <- matrix(c(paste("lam", 1:4, sep=""), rep(NA, 8), paste("lam", 1:4, sep="")), ncol=2)
lx <- mxMatrix(name="Lam", values=lval, free=lfre, ncol=2, nrow=8, labels=llab, dimnames=list(manvar, c("F1", "F2")))
td <- mxMatrix(name="Theta", type="Symm", ncol=8,
values=
c(.8, 0, 0, 0, .2, 0, 0, 0,
.8, 0, .2, 0, .2, 0, 0,
.8, 0, 0, 0, .2, 0,
.8, 0, 0, 0, .2,
.8, 0, 0, 0,
.8, 0, .2,
.8, 0,
.8),
free=c(T,F,F,F,T,F,F,F,
T,F,T,F,T,F,F,
T,F,F,F,T,F,
T,F,F,F,T,
T,F,F,F,
T,F,T,
T,F,
T),
dimnames=list(manvar, manvar)
)
diag(td$labels) <- paste("var", 1:8, sep="")
selMat <- matrix(
c(5,1,
4,2,
6,2,
7,3,
8,4,
8,6), ncol=2, byrow=TRUE)
td$labels[selMat] <- paste("cov", c(51, 42, 62, 73, 84, 86), sep="")
td$labels[selMat[,2:1]] <- paste("cov", c(51, 42, 62, 73, 84, 86), sep="")
ph <- mxMatrix(name="Phi", type="Symm", ncol=2, free=T, values=c(.8, .2, .8), labels=paste("phi", c(1, 12, 2), sep=""), dimnames=list(c("F1", "F2"), c("F1", "F2")))
#--------------------------------------
# Set-up WLS model
wlsMod <- mxModel("Test case for WLS Objective function from Bollen 1989",
lx, ph, td,
mxExpectationLISREL(LX=lx$name, PH=ph$name, TD=td$name),
mxFitFunctionWLS(),
mxData(Bollen[, 1:8], 'raw')
)
dwlsMod <- mxModel(wlsMod, mxFitFunctionWLS("DWLS"))
ulsMod <- mxModel(wlsMod, mxFitFunctionWLS("ULS"))
# Run WLS model
wlsRun <- mxRun(wlsMod)
summary(wlsRun)
omxCheckTrue(is.null(wlsRun$output$calculatedHessian))
expect_equal(length(mxGetExpected(wlsRun, 'standvector')),
summary(wlsRun)$observedStatistics)
dwlsRun <- mxRun(dwlsMod)
ulsRun <- mxRun(ulsMod)
expect_equal(median(log(diag(wlsRun$data$observedStats$acov))),
median(log(diag(dwlsRun$data$observedStats$acov))), 1)
expect_equal(diag(wlsRun$data$observedStats$fullWeight),
diag(dwlsRun$data$observedStats$fullWeight))
print(cbind(coef(wlsRun), coef(dwlsRun), coef(ulsRun)))
omxCheckCloseEnough(vechs(cor(cbind(coef(wlsRun), coef(dwlsRun), coef(ulsRun)))),
c(.931, .922, .996), 1e-3)
#TODO Fix summary for WLS data/fitfunctions
# Standard errors correct?
# observed statistics are not 0
# degrees of freedom should be correct when obs stats is fixed
# -2 log likelihood should be "Fit Function Value"?
# AIC, BIC for WLS?
# Compare parameter estimates
# WLS estimated parameters reported in Bollen (1989, p. 428, Table 9.4)
bollenParam <- c(l1=1.00, l2=1.11, l3=1.05, l4=1.16, e1=1.30, e2=7.12, e3=3.53, e4=2.72, e5=2.29, e6=3.77, e7=2.60, e8=3.45)
fitParam <- c(mxEval(Lam, wlsRun)[1:4,1], diag(mxEval(Theta, wlsRun)))
omxCheckCloseEnough(bollenParam, fitParam, epsilon=0.01)
j1 <- omxManifestModelByParameterJacobian(wlsRun, standardize = TRUE)
# Tedious to check all entries, but if variances match then
# other labels are probably correct.
expect_equivalent(j1[1:8, paste0('var',1:8)], diag(8))
#--------------------------------------
# Marginals should be fairly close to cumulants
wlsMO <- omxAugmentDataWithWLSSummary(mxData(Bollen[,1:8], 'raw'), allContinuousMethod = "marginals")
dwlsMO <- omxAugmentDataWithWLSSummary(mxData(Bollen[,1:8], 'raw'), "DWLS", allContinuousMethod = "marginals")
omxCheckCloseEnough(cor(vech(wlsMO$observedStats$cov),
vech(wlsRun$data$observedStats$cov)), 1, 5e-3)
omxCheckCloseEnough(cor(vech(wlsMO$observedStats$asymCov[-1:-8,-1:-8]),
vech(wlsRun$data$observedStats$asymCov)), 1, .31)
omxCheckCloseEnough(cor(diag(dwlsMO$observedStats$useWeight)[-1:-8],
diag(dwlsRun$data$observedStats$useWeight)), 1, .21)
#--------------------------------------
# Re-run with ML
mlMod <- mxModel(wlsMod, name="Rerun WLS model from Bollen as ML",
mxFitFunctionML(),
mxData(cov(Bollen[ , 1:8]), "cov", numObs=nrow(Bollen))
)
mlRun <- mxRun(mlMod)
#--------------------------------------
# Compare estimates
mlSum <- summary(mlRun)
wlsSum <- summary(wlsRun)
rms <- function(x, y){sqrt(mean((x-y)^2))}
# parameters are sort of close
expect_equal(rms(mlSum$parameters[,5], wlsSum$parameters[,5]), 0, 0.65)
# standard errors are close
expect_equal(rms(mlSum$parameters[,6], wlsSum$parameters[,6]), 0, 0.18)
# Chi square is on par
expect_equal(mlSum$Chi - wlsSum$Chi, 0, 11)
# Chi square df are the same
omxCheckEquals(mlSum$ChiDoF, wlsSum$ChiDoF)
# RMSEA confidence intervals overlap
omxCheckTrue( (wlsSum$RMSEA < mlSum$RMSEACI[2]) & (wlsSum$RMSEA > mlSum$RMSEACI[1]))
omxCheckTrue( (mlSum$RMSEA < wlsSum$RMSEACI[2]) & (mlSum$RMSEA >= wlsSum$RMSEACI[1]))
#--------------------------------------
wlsMod$data$observed[,1] <- 0.
expect_error(mxRun(wlsMod), "Test case for WLS Objective function from Bollen 1989.data: 'y1' has observed variance less than 1.49012e-08")
wlsMod <- mxModel(wlsMod, mxFitFunctionWLS(allContinuousMethod= 'marginals'))
expect_error(mxRun(wlsMod), "WLS Objective function from Bollen 1989.data: 'y1' has observed variance less than 1.49012e-08")
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