inst/models/passing/checkStandardizedLoadingsEtcetera.R
In OpenMx/OpenMx: Extended Structural Equation Modelling
#
# 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.
require(OpenMx)
data(demoOneFactor)
Rff <- mxMatrix(type="Stand",nrow=1,ncol=1,free=F,name="Rff")
L <- mxMatrix(type="Full",nrow=5,ncol=1,free=T,values=0.2,labels=paste("l",1:5,sep=""),lbound=-2,ubound=2,name="L")
I <- mxMatrix(type="Iden",nrow=5,ncol=5,name="I")
C <- mxAlgebra(L %*% Rff %*% t(L),name="C")
U <- mxAlgebra(I-(I*C),name="U")
SD <- mxMatrix(type="Full",nrow=5,ncol=1,free=T,values=0.6,
labels=c("sd1","sd2","sd3","sd4","sd5"),lbound=0,ubound=2,name="SD")
Sigma <- mxAlgebra( (C+U) * (SD%*%t(SD)), name="Sigma", dimnames=list(colnames(demoOneFactor),colnames(demoOneFactor)))
factorModelMatrix <- mxModel(
"OneFactorMatrix", Rff, L, I, C, U, SD, Sigma,
mxExpectationNormal(covariance="Sigma"),
mxFitFunctionML(),
mxCI("L"),
mxData(observed=cov(demoOneFactor), type="cov", numObs=500)
)
factorMatrixFit <- mxRun(factorModelMatrix, suppressWarnings = T)
omxCheckError(mxStandardizeRAMpaths(factorMatrixFit),"model 'OneFactorMatrix' does not use RAM expectation")
manifests <- names(demoOneFactor)
latents <- c("G")
factorModelPath <- mxModel("OneFactorPath",
type="RAM",
manifestVars = manifests,
latentVars = latents,
mxPath(from=latents, to=manifests,
labels=paste("L",1:5,sep="")),
mxPath(from=manifests, arrows=2),
mxPath(from=latents, arrows=2,
free=FALSE, values=1.0),
mxData(cov(demoOneFactor), type="cov",
numObs=500))
omxCheckWarning(
mxStandardizeRAMpaths(factorModelPath,T),
"standard errors will not be computed because model 'OneFactorPath' has not yet been run, and no matrix was provided for argument 'cov'")
factorPathFit <- mxRun(factorModelPath, suppressWarnings = T)
( matrixFitPar <- summary(factorMatrixFit)$parameters )
( pathFitPar <- summary(factorPathFit)$parameters )
( zpath <- mxStandardizeRAMpaths(factorPathFit,T) )
if (0) {
options(digits = 12)
print(factorPathFit$output$fit)
}
omxCheckCloseEnough(factorPathFit$output$fit, -3660.59669, 1e-3)
if (0) {
max(abs(matrixFitPar$Estimate[1:5] - zpath$Std.Value[1:5]))
}
omxCheckCloseEnough(matrixFitPar$Estimate[1:5],zpath$Std.Value[1:5],5e-5)
omxCheckCloseEnough(matrixFitPar$Std.Error[1:5],zpath$Std.SE[1:5],5e-5)
omxCheckCloseEnough(
zpath$Std.Value,
c(0.891309322741553,0.932554560675801,0.943846635668634,0.962362487593728,0.972555617431608,0.205567691193994,
0.130341991362763,0.109153528337001,0.0738584424724116,0.0541355710022234,1),
3e-4)
omxCheckCloseEnough(
zpath$Std.SE,
c(0.0097233354792961,0.00640855820968981,0.00551439004191031,0.00400750800616116,0.00327547260416904,
0.0173329991211512,0.0119526603725942,0.0104094769784555,0.00771335074796404,0.00637115856365944,0),
5e-5)
#Check errors and warnings:
pointlessAlg <- mxModel(factorPathFit, mxAlgebra(1))
omxCheckWarning(
mxStandardizeRAMpaths(pointlessAlg),
"MxModel 'OneFactorPath' was modified since it was run."
)
pointlessConstraint <- mxModel(factorModelPath, mxConstraint(1==1))
pointlessConstraint <- mxRun(pointlessConstraint)
#NPSOL populates the 'hessian' slot of the output with its own final Hessian when there is no MxComputeNumericDeriv step:
if(mxOption(NULL,"Default optimizer") != "NPSOL"){
plan <- omxDefaultComputePlan()
plan$steps <- list(plan$steps$GD, plan$steps$RE)
nohess <- mxModel(factorModelPath, plan)
nohess <- mxRun(nohess)
omxCheckWarning(
mxStandardizeRAMpaths(nohess,T),
"argument 'SE=TRUE' requires model to have a nonempty 'vcov' output slot, or a non-NULL value for argument 'cov'; continuing with 'SE' coerced to 'FALSE'"
)
}
#Make more models and check mxStandardizeRAMpaths()'s output for multigroup:
data("twinData", package="OpenMx")
selVars <- c('bmi1','bmi2')
aceVars <- c("A1","C1","E1","A2","C2","E2")
mzfData <- as.matrix(subset(twinData, zyg==1, selVars))
dzfData <- as.matrix(subset(twinData, zyg==3, selVars))
cov(mzfData, use="pairwise.complete.obs")
cov(dzfData, use="pairwise.complete.obs")
share <- mxModel("share", type="RAM",
manifestVars=selVars,
latentVars=aceVars,
mxPath(from=aceVars, arrows=2, free=FALSE, values=1),
mxPath(from="one", to=aceVars, arrows=1, free=FALSE, values=0),
mxPath(from="one", to=selVars, arrows=1, free=TRUE, values=20, labels= c("mean","mean")),
mxPath(from="C1", to="C2", arrows=2, free=FALSE, values=1),
mxPath(from=c("A1","C1","E1"), to="bmi1", arrows=1, free=TRUE, values=.6, label=c("a","c","e")),
mxPath(from=c("A2","C2","E2"), to="bmi2", arrows=1, free=TRUE, values=.6, label=c("a","c","e"))
)
MZ = mxModel(share, name="MZ",
mxPath(from="A1", to="A2", arrows=2, free=FALSE, values=1),
mxData(mzfData, type="raw")
)
DZ = mxModel(share, name="DZ",
mxPath(from="A1", to="A2", arrows=2, free=FALSE, values=.5),
mxData(dzfData, type="raw")
)
twinACEModel <- mxModel("twinACE", MZ, DZ,
mxAlgebra(MZ.objective + DZ.objective, name="twin"),
mxFitFunctionAlgebra("twin")
)
myLongitudinalDataCov <- matrix(
c(6.362, 4.344, 4.915, 5.045, 5.966,
4.344, 7.241, 5.825, 6.181, 7.252,
4.915, 5.825, 9.348, 7.727, 8.968,
5.045, 6.181, 7.727, 10.821, 10.135,
5.966, 7.252, 8.968, 10.135, 14.220),
nrow=5,
dimnames=list(
c("x1","x2","x3","x4","x5"),
c("x1","x2","x3","x4","x5"))
)
myLongitudinalDataMean <- c(9.864, 11.812, 13.612, 15.317, 17.178)
names(myLongitudinalDataMean) <- c("x1","x2","x3","x4","x5")
growthCurveModel <- mxModel("LinearGrowthCurveModel_MatrixSpecification",
mxData(myLongitudinalDataCov,
type="cov",
numObs=500,
mean=myLongitudinalDataMean),
mxMatrix(
type="Full",
nrow=7,
ncol=7,
free=F,
values=c(0,0,0,0,0,1,0,
0,0,0,0,0,1,1,
0,0,0,0,0,1,2,
0,0,0,0,0,1,3,
0,0,0,0,0,1,4,
0,0,0,0,0,0,0,
0,0,0,0,0,0,0),
byrow=TRUE,
name="AA"),
mxMatrix(
type="Symm",
nrow=7,
ncol=7,
free=c(T, F, F, F, F, F, F,
F, T, F, F, F, F, F,
F, F, T, F, F, F, F,
F, F, F, T, F, F, F,
F, F, F, F, T, F, F,
F, F, F, F, F, T, T,
F, F, F, F, F, T, T),
values=c(0,0,0,0,0, 0, 0,
0,0,0,0,0, 0, 0,
0,0,0,0,0, 0, 0,
0,0,0,0,0, 0, 0,
0,0,0,0,0, 0, 0,
0,0,0,0,0, 1,0.5,
0,0,0,0,0,0.5, 1),
labels=c("residual", NA, NA, NA, NA, NA, NA,
NA, "residual", NA, NA, NA, NA, NA,
NA, NA, "residual", NA, NA, NA, NA,
NA, NA, NA, "residual", NA, NA, NA,
NA, NA, NA, NA, "residual", NA, NA,
NA, NA, NA, NA, NA, "vari", "cov",
NA, NA, NA, NA, NA, "cov", "vars"),
byrow= TRUE,
name="SS"),
mxMatrix(
type="Full",
nrow=5,
ncol=7,
free=F,
values=c(1,0,0,0,0,0,0,
0,1,0,0,0,0,0,
0,0,1,0,0,0,0,
0,0,0,1,0,0,0,
0,0,0,0,1,0,0),
byrow=T,
name="F"),
mxMatrix("Full", nrow=1, ncol=7,
values=c(0,0,0,0,0,1,1),
free=c(F,F,F,F,F,T,T),
labels=c(NA,NA,NA,NA,NA,"meani","means"),
name="M"),
mxFitFunctionML(),mxExpectationRAM("AA","SS","F","M",dimnames=c("x1","x2","x3","x4","x5","intercept","slope"))
)
bigmod <- mxModel(
model="foo",factorModelPath,factorModelMatrix,twinACEModel,growthCurveModel,
mxFitFunctionMultigroup(c("OneFactorPath","OneFactorMatrix","twinACE",
"LinearGrowthCurveModel_MatrixSpecification"))
)
bigrun <- mxRun(bigmod, suppressWarnings = T)
zpath2 <- mxStandardizeRAMpaths(bigrun,T)
omxCheckEquals(names(zpath2),c("OneFactorPath","twinACE","LinearGrowthCurveModel_MatrixSpecification"))
omxCheckEquals(names(zpath2[[2]]),c("MZ","DZ"))
#Tests for scaled elements of M matrix:
omxCheckCloseEnough(
bigrun$twinACE$MZ$M$values[1,1] / sqrt(mxGetExpected(bigrun$twinACE$MZ,"covariance")[1,1]),
zpath2$twinACE$MZ$Std.Value[15],
1e-12
)
omxCheckCloseEnough(
bigrun$twinACE$DZ$M$values[1,1] / sqrt(mxGetExpected(bigrun$twinACE$DZ,"covariance")[1,1]),
zpath2$twinACE$DZ$Std.Value[15],
1e-12
)
omxCheckEquals(colnames(bigrun$LinearGrowthCurveModel_MatrixSpecification$expectation$UnfilteredExpCov),
bigrun$LinearGrowthCurveModel_MatrixSpecification$expectation$dims)
omxCheckCloseEnough(
bigrun$LinearGrowthCurveModel_MatrixSpecification$M$values[6] /
sqrt(bigrun$LinearGrowthCurveModel_MatrixSpecification$expectation$UnfilteredExpCov[6,6]),
zpath2$LinearGrowthCurveModel_MatrixSpecification$Std.Value[18],
1e-12
)
omxCheckCloseEnough(
bigrun$LinearGrowthCurveModel_MatrixSpecification$M$values[7] /
sqrt(bigrun$LinearGrowthCurveModel_MatrixSpecification$expectation$UnfilteredExpCov[7,7]),
zpath2$LinearGrowthCurveModel_MatrixSpecification$Std.Value[19],
1e-12
)
bigmod2 <- mxModel(
model="foo",mxModel(factorModelPath,independent=T),mxModel(factorModelMatrix,independent=T),
twinACEModel,growthCurveModel,
mxFitFunctionMultigroup(c("twinACE","LinearGrowthCurveModel_MatrixSpecification"))
)
bigrun2 <- mxRun(bigmod2, suppressWarnings = T)
zpath3 <- mxStandardizeRAMpaths(bigrun2,T)
omxCheckEquals(names(zpath3),c("OneFactorPath","twinACE","LinearGrowthCurveModel_MatrixSpecification"))
omxCheckEquals(names(zpath3[[2]]),c("MZ","DZ"))
omxCheckEquals(sum(zpath3$OneFactorPath==zpath,na.rm=T),93)
#Tests for scaled elements of M matrix:
omxCheckCloseEnough(
bigrun2$twinACE$MZ$M$values[1,1] / sqrt(mxGetExpected(bigrun2$twinACE$MZ,"covariance")[1,1]),
zpath3$twinACE$MZ$Std.Value[15],
1e-12
)
omxCheckCloseEnough(
bigrun2$twinACE$DZ$M$values[1,1] / sqrt(mxGetExpected(bigrun2$twinACE$DZ,"covariance")[1,1]),
zpath3$twinACE$DZ$Std.Value[15],
1e-12
)
omxCheckCloseEnough(
bigrun2$LinearGrowthCurveModel_MatrixSpecification$M$values[6] /
sqrt(bigrun2$LinearGrowthCurveModel_MatrixSpecification$expectation$UnfilteredExpCov[6,6]),
zpath3$LinearGrowthCurveModel_MatrixSpecification$Std.Value[18],
1e-12
)
omxCheckCloseEnough(
bigrun2$LinearGrowthCurveModel_MatrixSpecification$M$values[7] /
sqrt(bigrun2$LinearGrowthCurveModel_MatrixSpecification$expectation$UnfilteredExpCov[7,7]),
zpath3$LinearGrowthCurveModel_MatrixSpecification$Std.Value[19],
1e-12
)
bigmod3 <- mxModel(
model="foo",mxModel(factorModelPath,independent=T),mxModel(factorModelMatrix,independent=T),
mxModel(twinACEModel,independent=T),mxModel(growthCurveModel,independent=T))
bigrun3 <- mxRun(bigmod3, suppressWarnings = T)
zpath4 <- mxStandardizeRAMpaths(bigrun3,T)
omxCheckEquals(sum(zpath4$OneFactorPath==zpath3$OneFactorPath,na.rm=T),93)
omxCheckEquals(names(zpath4),c("OneFactorPath","twinACE","LinearGrowthCurveModel_MatrixSpecification"))
omxCheckEquals(names(zpath4[[2]]),c("MZ","DZ"))
#Tests for scaled elements of M matrix:
omxCheckCloseEnough(
bigrun3$twinACE$MZ$M$values[1,1] / sqrt(mxGetExpected(bigrun3$twinACE$MZ,"covariance")[1,1]),
zpath4$twinACE$MZ$Std.Value[15],
1e-12
)
omxCheckCloseEnough(
bigrun3$twinACE$DZ$M$values[1,1] / sqrt(mxGetExpected(bigrun3$twinACE$DZ,"covariance")[1,1]),
zpath4$twinACE$DZ$Std.Value[15],
1e-12
)
omxCheckCloseEnough(
bigrun3$LinearGrowthCurveModel_MatrixSpecification$M$values[6] /
sqrt(bigrun3$LinearGrowthCurveModel_MatrixSpecification$expectation$UnfilteredExpCov[6,6]),
zpath4$LinearGrowthCurveModel_MatrixSpecification$Std.Value[18],
1e-12
)
omxCheckCloseEnough(
bigrun3$LinearGrowthCurveModel_MatrixSpecification$M$values[7] /
sqrt(bigrun3$LinearGrowthCurveModel_MatrixSpecification$expectation$UnfilteredExpCov[7,7]),
zpath4$LinearGrowthCurveModel_MatrixSpecification$Std.Value[19],
1e-12
)
#Regression test to ensure (1) that if the "container" model uses RAM expectation, its results are included in the standardized-paths output,
#and (2) that its element of the output list is named after it:
littlemod <- mxModel(factorModelPath, mxModel(growthCurveModel, independent=T))
littlerun <- mxRun(littlemod)
zpath5 <- mxStandardizeRAMpaths(littlerun)
omxCheckEquals(names(zpath5)[1],factorModelPath$name)
omxCheckEquals(zpath4$OneFactorPath$Std.Value,zpath5$OneFactorPath$Std.Value)
omxCheckEquals(zpath4$LinearGrowthCurveModel_MatrixSpecification$Std.Value,zpath5$LinearGrowthCurveModel_MatrixSpecification$Std.Value)
# #Test mxTryHard(), since the initial run of 'bigmod' gets Code Red:
# set.seed(420)
# bigrun_again <- mxTryHard(bigmod)
# omxCheckEquals(bigrun_again$output$status$code,0)
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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.
require(OpenMx)
data(demoOneFactor)
Rff <- mxMatrix(type="Stand",nrow=1,ncol=1,free=F,name="Rff")
L <- mxMatrix(type="Full",nrow=5,ncol=1,free=T,values=0.2,labels=paste("l",1:5,sep=""),lbound=-2,ubound=2,name="L")
I <- mxMatrix(type="Iden",nrow=5,ncol=5,name="I")
C <- mxAlgebra(L %*% Rff %*% t(L),name="C")
U <- mxAlgebra(I-(I*C),name="U")
SD <- mxMatrix(type="Full",nrow=5,ncol=1,free=T,values=0.6,
labels=c("sd1","sd2","sd3","sd4","sd5"),lbound=0,ubound=2,name="SD")
Sigma <- mxAlgebra( (C+U) * (SD%*%t(SD)), name="Sigma", dimnames=list(colnames(demoOneFactor),colnames(demoOneFactor)))
factorModelMatrix <- mxModel(
"OneFactorMatrix", Rff, L, I, C, U, SD, Sigma,
mxExpectationNormal(covariance="Sigma"),
mxFitFunctionML(),
mxCI("L"),
mxData(observed=cov(demoOneFactor), type="cov", numObs=500)
)
factorMatrixFit <- mxRun(factorModelMatrix, suppressWarnings = T)
omxCheckError(mxStandardizeRAMpaths(factorMatrixFit),"model 'OneFactorMatrix' does not use RAM expectation")
manifests <- names(demoOneFactor)
latents <- c("G")
factorModelPath <- mxModel("OneFactorPath",
type="RAM",
manifestVars = manifests,
latentVars = latents,
mxPath(from=latents, to=manifests,
labels=paste("L",1:5,sep="")),
mxPath(from=manifests, arrows=2),
mxPath(from=latents, arrows=2,
free=FALSE, values=1.0),
mxData(cov(demoOneFactor), type="cov",
numObs=500))
omxCheckWarning(
mxStandardizeRAMpaths(factorModelPath,T),
"standard errors will not be computed because model 'OneFactorPath' has not yet been run, and no matrix was provided for argument 'cov'")
factorPathFit <- mxRun(factorModelPath, suppressWarnings = T)
( matrixFitPar <- summary(factorMatrixFit)$parameters )
( pathFitPar <- summary(factorPathFit)$parameters )
( zpath <- mxStandardizeRAMpaths(factorPathFit,T) )
if (0) {
options(digits = 12)
print(factorPathFit$output$fit)
}
omxCheckCloseEnough(factorPathFit$output$fit, -3660.59669, 1e-3)
if (0) {
max(abs(matrixFitPar$Estimate[1:5] - zpath$Std.Value[1:5]))
}
omxCheckCloseEnough(matrixFitPar$Estimate[1:5],zpath$Std.Value[1:5],5e-5)
omxCheckCloseEnough(matrixFitPar$Std.Error[1:5],zpath$Std.SE[1:5],5e-5)
omxCheckCloseEnough(
zpath$Std.Value,
c(0.891309322741553,0.932554560675801,0.943846635668634,0.962362487593728,0.972555617431608,0.205567691193994,
0.130341991362763,0.109153528337001,0.0738584424724116,0.0541355710022234,1),
3e-4)
omxCheckCloseEnough(
zpath$Std.SE,
c(0.0097233354792961,0.00640855820968981,0.00551439004191031,0.00400750800616116,0.00327547260416904,
0.0173329991211512,0.0119526603725942,0.0104094769784555,0.00771335074796404,0.00637115856365944,0),
5e-5)
#Check errors and warnings:
pointlessAlg <- mxModel(factorPathFit, mxAlgebra(1))
omxCheckWarning(
mxStandardizeRAMpaths(pointlessAlg),
"MxModel 'OneFactorPath' was modified since it was run."
)
pointlessConstraint <- mxModel(factorModelPath, mxConstraint(1==1))
pointlessConstraint <- mxRun(pointlessConstraint)
#NPSOL populates the 'hessian' slot of the output with its own final Hessian when there is no MxComputeNumericDeriv step:
if(mxOption(NULL,"Default optimizer") != "NPSOL"){
plan <- omxDefaultComputePlan()
plan$steps <- list(plan$steps$GD, plan$steps$RE)
nohess <- mxModel(factorModelPath, plan)
nohess <- mxRun(nohess)
omxCheckWarning(
mxStandardizeRAMpaths(nohess,T),
"argument 'SE=TRUE' requires model to have a nonempty 'vcov' output slot, or a non-NULL value for argument 'cov'; continuing with 'SE' coerced to 'FALSE'"
)
}
#Make more models and check mxStandardizeRAMpaths()'s output for multigroup:
data("twinData", package="OpenMx")
selVars <- c('bmi1','bmi2')
aceVars <- c("A1","C1","E1","A2","C2","E2")
mzfData <- as.matrix(subset(twinData, zyg==1, selVars))
dzfData <- as.matrix(subset(twinData, zyg==3, selVars))
cov(mzfData, use="pairwise.complete.obs")
cov(dzfData, use="pairwise.complete.obs")
share <- mxModel("share", type="RAM",
manifestVars=selVars,
latentVars=aceVars,
mxPath(from=aceVars, arrows=2, free=FALSE, values=1),
mxPath(from="one", to=aceVars, arrows=1, free=FALSE, values=0),
mxPath(from="one", to=selVars, arrows=1, free=TRUE, values=20, labels= c("mean","mean")),
mxPath(from="C1", to="C2", arrows=2, free=FALSE, values=1),
mxPath(from=c("A1","C1","E1"), to="bmi1", arrows=1, free=TRUE, values=.6, label=c("a","c","e")),
mxPath(from=c("A2","C2","E2"), to="bmi2", arrows=1, free=TRUE, values=.6, label=c("a","c","e"))
)
MZ = mxModel(share, name="MZ",
mxPath(from="A1", to="A2", arrows=2, free=FALSE, values=1),
mxData(mzfData, type="raw")
)
DZ = mxModel(share, name="DZ",
mxPath(from="A1", to="A2", arrows=2, free=FALSE, values=.5),
mxData(dzfData, type="raw")
)
twinACEModel <- mxModel("twinACE", MZ, DZ,
mxAlgebra(MZ.objective + DZ.objective, name="twin"),
mxFitFunctionAlgebra("twin")
)
myLongitudinalDataCov <- matrix(
c(6.362, 4.344, 4.915, 5.045, 5.966,
4.344, 7.241, 5.825, 6.181, 7.252,
4.915, 5.825, 9.348, 7.727, 8.968,
5.045, 6.181, 7.727, 10.821, 10.135,
5.966, 7.252, 8.968, 10.135, 14.220),
nrow=5,
dimnames=list(
c("x1","x2","x3","x4","x5"),
c("x1","x2","x3","x4","x5"))
)
myLongitudinalDataMean <- c(9.864, 11.812, 13.612, 15.317, 17.178)
names(myLongitudinalDataMean) <- c("x1","x2","x3","x4","x5")
growthCurveModel <- mxModel("LinearGrowthCurveModel_MatrixSpecification",
mxData(myLongitudinalDataCov,
type="cov",
numObs=500,
mean=myLongitudinalDataMean),
mxMatrix(
type="Full",
nrow=7,
ncol=7,
free=F,
values=c(0,0,0,0,0,1,0,
0,0,0,0,0,1,1,
0,0,0,0,0,1,2,
0,0,0,0,0,1,3,
0,0,0,0,0,1,4,
0,0,0,0,0,0,0,
0,0,0,0,0,0,0),
byrow=TRUE,
name="AA"),
mxMatrix(
type="Symm",
nrow=7,
ncol=7,
free=c(T, F, F, F, F, F, F,
F, T, F, F, F, F, F,
F, F, T, F, F, F, F,
F, F, F, T, F, F, F,
F, F, F, F, T, F, F,
F, F, F, F, F, T, T,
F, F, F, F, F, T, T),
values=c(0,0,0,0,0, 0, 0,
0,0,0,0,0, 0, 0,
0,0,0,0,0, 0, 0,
0,0,0,0,0, 0, 0,
0,0,0,0,0, 0, 0,
0,0,0,0,0, 1,0.5,
0,0,0,0,0,0.5, 1),
labels=c("residual", NA, NA, NA, NA, NA, NA,
NA, "residual", NA, NA, NA, NA, NA,
NA, NA, "residual", NA, NA, NA, NA,
NA, NA, NA, "residual", NA, NA, NA,
NA, NA, NA, NA, "residual", NA, NA,
NA, NA, NA, NA, NA, "vari", "cov",
NA, NA, NA, NA, NA, "cov", "vars"),
byrow= TRUE,
name="SS"),
mxMatrix(
type="Full",
nrow=5,
ncol=7,
free=F,
values=c(1,0,0,0,0,0,0,
0,1,0,0,0,0,0,
0,0,1,0,0,0,0,
0,0,0,1,0,0,0,
0,0,0,0,1,0,0),
byrow=T,
name="F"),
mxMatrix("Full", nrow=1, ncol=7,
values=c(0,0,0,0,0,1,1),
free=c(F,F,F,F,F,T,T),
labels=c(NA,NA,NA,NA,NA,"meani","means"),
name="M"),
mxFitFunctionML(),mxExpectationRAM("AA","SS","F","M",dimnames=c("x1","x2","x3","x4","x5","intercept","slope"))
)
bigmod <- mxModel(
model="foo",factorModelPath,factorModelMatrix,twinACEModel,growthCurveModel,
mxFitFunctionMultigroup(c("OneFactorPath","OneFactorMatrix","twinACE",
"LinearGrowthCurveModel_MatrixSpecification"))
)
bigrun <- mxRun(bigmod, suppressWarnings = T)
zpath2 <- mxStandardizeRAMpaths(bigrun,T)
omxCheckEquals(names(zpath2),c("OneFactorPath","twinACE","LinearGrowthCurveModel_MatrixSpecification"))
omxCheckEquals(names(zpath2[[2]]),c("MZ","DZ"))
#Tests for scaled elements of M matrix:
omxCheckCloseEnough(
bigrun$twinACE$MZ$M$values[1,1] / sqrt(mxGetExpected(bigrun$twinACE$MZ,"covariance")[1,1]),
zpath2$twinACE$MZ$Std.Value[15],
1e-12
)
omxCheckCloseEnough(
bigrun$twinACE$DZ$M$values[1,1] / sqrt(mxGetExpected(bigrun$twinACE$DZ,"covariance")[1,1]),
zpath2$twinACE$DZ$Std.Value[15],
1e-12
)
omxCheckEquals(colnames(bigrun$LinearGrowthCurveModel_MatrixSpecification$expectation$UnfilteredExpCov),
bigrun$LinearGrowthCurveModel_MatrixSpecification$expectation$dims)
omxCheckCloseEnough(
bigrun$LinearGrowthCurveModel_MatrixSpecification$M$values[6] /
sqrt(bigrun$LinearGrowthCurveModel_MatrixSpecification$expectation$UnfilteredExpCov[6,6]),
zpath2$LinearGrowthCurveModel_MatrixSpecification$Std.Value[18],
1e-12
)
omxCheckCloseEnough(
bigrun$LinearGrowthCurveModel_MatrixSpecification$M$values[7] /
sqrt(bigrun$LinearGrowthCurveModel_MatrixSpecification$expectation$UnfilteredExpCov[7,7]),
zpath2$LinearGrowthCurveModel_MatrixSpecification$Std.Value[19],
1e-12
)
bigmod2 <- mxModel(
model="foo",mxModel(factorModelPath,independent=T),mxModel(factorModelMatrix,independent=T),
twinACEModel,growthCurveModel,
mxFitFunctionMultigroup(c("twinACE","LinearGrowthCurveModel_MatrixSpecification"))
)
bigrun2 <- mxRun(bigmod2, suppressWarnings = T)
zpath3 <- mxStandardizeRAMpaths(bigrun2,T)
omxCheckEquals(names(zpath3),c("OneFactorPath","twinACE","LinearGrowthCurveModel_MatrixSpecification"))
omxCheckEquals(names(zpath3[[2]]),c("MZ","DZ"))
omxCheckEquals(sum(zpath3$OneFactorPath==zpath,na.rm=T),93)
#Tests for scaled elements of M matrix:
omxCheckCloseEnough(
bigrun2$twinACE$MZ$M$values[1,1] / sqrt(mxGetExpected(bigrun2$twinACE$MZ,"covariance")[1,1]),
zpath3$twinACE$MZ$Std.Value[15],
1e-12
)
omxCheckCloseEnough(
bigrun2$twinACE$DZ$M$values[1,1] / sqrt(mxGetExpected(bigrun2$twinACE$DZ,"covariance")[1,1]),
zpath3$twinACE$DZ$Std.Value[15],
1e-12
)
omxCheckCloseEnough(
bigrun2$LinearGrowthCurveModel_MatrixSpecification$M$values[6] /
sqrt(bigrun2$LinearGrowthCurveModel_MatrixSpecification$expectation$UnfilteredExpCov[6,6]),
zpath3$LinearGrowthCurveModel_MatrixSpecification$Std.Value[18],
1e-12
)
omxCheckCloseEnough(
bigrun2$LinearGrowthCurveModel_MatrixSpecification$M$values[7] /
sqrt(bigrun2$LinearGrowthCurveModel_MatrixSpecification$expectation$UnfilteredExpCov[7,7]),
zpath3$LinearGrowthCurveModel_MatrixSpecification$Std.Value[19],
1e-12
)
bigmod3 <- mxModel(
model="foo",mxModel(factorModelPath,independent=T),mxModel(factorModelMatrix,independent=T),
mxModel(twinACEModel,independent=T),mxModel(growthCurveModel,independent=T))
bigrun3 <- mxRun(bigmod3, suppressWarnings = T)
zpath4 <- mxStandardizeRAMpaths(bigrun3,T)
omxCheckEquals(sum(zpath4$OneFactorPath==zpath3$OneFactorPath,na.rm=T),93)
omxCheckEquals(names(zpath4),c("OneFactorPath","twinACE","LinearGrowthCurveModel_MatrixSpecification"))
omxCheckEquals(names(zpath4[[2]]),c("MZ","DZ"))
#Tests for scaled elements of M matrix:
omxCheckCloseEnough(
bigrun3$twinACE$MZ$M$values[1,1] / sqrt(mxGetExpected(bigrun3$twinACE$MZ,"covariance")[1,1]),
zpath4$twinACE$MZ$Std.Value[15],
1e-12
)
omxCheckCloseEnough(
bigrun3$twinACE$DZ$M$values[1,1] / sqrt(mxGetExpected(bigrun3$twinACE$DZ,"covariance")[1,1]),
zpath4$twinACE$DZ$Std.Value[15],
1e-12
)
omxCheckCloseEnough(
bigrun3$LinearGrowthCurveModel_MatrixSpecification$M$values[6] /
sqrt(bigrun3$LinearGrowthCurveModel_MatrixSpecification$expectation$UnfilteredExpCov[6,6]),
zpath4$LinearGrowthCurveModel_MatrixSpecification$Std.Value[18],
1e-12
)
omxCheckCloseEnough(
bigrun3$LinearGrowthCurveModel_MatrixSpecification$M$values[7] /
sqrt(bigrun3$LinearGrowthCurveModel_MatrixSpecification$expectation$UnfilteredExpCov[7,7]),
zpath4$LinearGrowthCurveModel_MatrixSpecification$Std.Value[19],
1e-12
)
#Regression test to ensure (1) that if the "container" model uses RAM expectation, its results are included in the standardized-paths output,
#and (2) that its element of the output list is named after it:
littlemod <- mxModel(factorModelPath, mxModel(growthCurveModel, independent=T))
littlerun <- mxRun(littlemod)
zpath5 <- mxStandardizeRAMpaths(littlerun)
omxCheckEquals(names(zpath5)[1],factorModelPath$name)
omxCheckEquals(zpath4$OneFactorPath$Std.Value,zpath5$OneFactorPath$Std.Value)
omxCheckEquals(zpath4$LinearGrowthCurveModel_MatrixSpecification$Std.Value,zpath5$LinearGrowthCurveModel_MatrixSpecification$Std.Value)
# #Test mxTryHard(), since the initial run of 'bigmod' gets Code Red:
# set.seed(420)
# bigrun_again <- mxTryHard(bigmod)
# omxCheckEquals(bigrun_again$output$status$code,0)
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