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#
# Copyright 2007-2020 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.
library(OpenMx)
library(testthat)
context("GREML Error Detection")
options(mxCondenseMatrixSlots=TRUE)
mxOption(NULL,"Analytic Gradients","Yes")
require(mvtnorm)
omxCheckError(mxExpectationGREML(V=1),
"argument 'V' is not of type 'character' (the name of the expected covariance matrix)")
set.seed(1234)
dat <- cbind(rnorm(100),rep(1,100))
colnames(dat) <- c("y","x")
testmod <- mxModel(
"GREMLtest",
#mxData(observed=dat, type="raw", sort=F),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = 2, labels = "ve", lbound = 0.0001, name = "Ve"),
mxMatrix("Iden",nrow=100,name="I",condenseSlots=T),
mxAlgebra(I %x% Ve,name="V"),
mxExpectationGREML(V="V",Xvars=list("x"),yvars="y",addOnes=F),
mxFitFunctionGREML()
)
omxCheckError(mxRun(testmod),
"the GREML expectation function does not have a dataset associated with it in model 'GREMLtest'")
testmod <- mxModel(
testmod,
mxData(observed=dat, type="raw", sort=F),
mxMatrix("Full",1,1,F,labels="data.y",name="Z")
)
omxCheckError(mxRun(testmod),
"definition variables are incompatible (and unnecessary) with GREML expectation")
testmod <- mxModel(
"GREMLtest",
mxData(observed=dat, type="raw", sort=F),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = 2, labels = "ve", lbound = 0.0001, name = "Ve"),
mxMatrix("Full",nrow=100,ncol=99,free=F,values=diag(100)[,-100],name="V",condenseSlots=T),
mxExpectationGREML(V="V",dataset.is.yX=T),
mxFitFunctionGREML()
)
omxCheckError(mxRun(testmod),
"'V' matrix is not square")
testmod$V <- mxMatrix("Full",nrow=99,ncol=99,free=F,values=diag(100)[-100,-100],name="V",condenseSlots=T)
omxCheckError(mxRun(testmod),
"y and V matrices do not have equal numbers of rows")
testmod <- mxModel(
"GREMLtest",
mxData(observed=dat, type="raw", sort=F),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = 1, labels = "ve", lbound = 0.0001, name = "Ve"),
mxMatrix("Iden",nrow=100,name="I",condenseSlots=T),
mxAlgebra(I %x% Ve,name="V"),
mxExpectationGREML(V="V",dataset.is.yX=TRUE,casesToDropFromV=101L),
mxFitFunctionGREML()
)
omxCheckWarning(
mxRun(testmod, suppressWarnings=TRUE),
"casesToDrop vector in GREML expectation contains indices greater than the number of datapoints")
testmod <- mxModel(
"GREMLtest",
mxData(observed=dat, type="raw", sort=F),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = 2, labels = "ve", lbound = 0.0001, name = "Ve"),
mxMatrix("Iden",nrow=100,name="I",condenseSlots=T),
mxMatrix("Iden",nrow=99,name="J",condenseSlots=T),
mxAlgebra(I %x% Ve,name="V"),
mxExpectationGREML(V="V", dataset.is.yX = T),
mxFitFunctionGREML(dV = c(ve="J"))
)
omxCheckError(mxRun(testmod),
"all derivatives of V must have the same dimensions as V")
testmod <- mxModel(
"GREMLtest",
mxData(observed=dat, type="raw", sort=F),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = 2, labels = "ve", lbound = 0.0001, name = "Ve"),
mxMatrix("Iden",nrow=100,name="I",condenseSlots=T),
mxAlgebra(I %x% Ve,name="V"),
mxExpectationGREML(V="V", dataset.is.yX=T),
mxFitFunctionGREML(dV = c(ve="J"))
)
omxCheckError(mxRun(testmod),
"The reference 'J' does not exist. It is used by named reference 'GREMLtest.fitfunction' .")
testmod <- mxModel(
"GREMLtest",
mxData(observed = matrix(dat[,1],1,100,dimnames=list(NULL,paste("y",1:100,sep=""))), type="raw"),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = 2, labels = "ve", lbound = 0.0001, name = "Ve"),
mxMatrix("Iden",nrow=100,name="I",condenseSlots=T),
mxMatrix("Zero",1,100,name="Zm"),
mxAlgebra(I %x% Ve,name="V"),
mxExpectationNormal(covariance="V",means="Zm", dimnames=paste("y",1:100,sep="")),
mxFitFunctionGREML()
)
omxCheckError(mxRun(testmod),
"GREML fitfunction is currently only compatible with GREML expectation")
testmod <- mxModel(
"GREMLtest",
mxData(observed=dat, type="raw", sort=F),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = 2, labels = "ve", lbound = 0.0001, name = "Ve"),
mxMatrix("Iden",nrow=100,name="I",condenseSlots=T),
mxAlgebra(I %x% Ve,name="V"),
mxExpectationGREML(V="V",Xvars=list("x"),yvars="y",addOnes=F),
mxFitFunctionGREML()
)
omxCheckError(mxRefModels(testmod),
"Reference models for GREML expectation are not implemented")
omxCheckError(mxModel(
"GREMLtest",
mxData(observed=dat, type="raw", sort=F),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = 2, labels = "ve", lbound = 0.0001, name = "Ve"),
mxMatrix("Iden",nrow=100,name="I",condenseSlots=T),
mxAlgebra(I %x% Ve,name="V"),
mxExpectationGREML(V="V",Xvars=list("x"),yvars="y",addOnes=F),
mxFitFunctionGREML(autoDerivType="muneric")
),"'muneric' should be one of 'semiAnalyt' and 'numeric'")
testmod <- mxModel(
"GREMLtest",
mxData(observed=dat, type="raw", sort=F),
mxMatrix(type = "Unit", nrow = 100, ncol=100, name = "V", condenseSlots = T),
mxExpectationGREML(V="V",Xvars=list("x"),yvars="y",addOnes=F),
mxFitFunctionGREML()
)
omxCheckError(mxRun(testmod),
"Expected covariance matrix is non-positive-definite at initial values")
testmod <- mxModel(
"GREMLtest",
mxData(observed=dat, type="raw", sort=F),
mxMatrix(type = "Unit", nrow = 100, ncol=100, name = "V", condenseSlots = T),
mxExpectationGREML(V="V",Xvars=list("x"),yvars="y",addOnes=F),
mxFitFunctionML()
)
omxCheckError(mxRun(testmod),
"Expected covariance matrix is non-positive-definite at initial values")
z <- matrix(-1,100,2)
colnames(z) <- c("z1","z2")
dat2 <- cbind(dat,z)
testmod <- mxModel(
"GREMLtest",
mxData(observed=dat2, type="raw", sort=F),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = 2, labels = "ve", lbound = 0.0001, name = "Ve"),
mxMatrix("Iden",nrow=100,name="I",condenseSlots=T),
mxAlgebra(I %x% Ve,name="V"),
mxExpectationGREML(V="V",Xvars=list(c("x","z1","z2")),yvars="y",addOnes=F),
mxFitFunctionGREML()
)
omxCheckError(mxRun(testmod),
"Cholesky factorization failed at initial values; possibly, the matrix of covariates is rank-deficient")
testmod <- mxModel(
"GREMLtest",
mxData(observed=dat2, type="raw", sort=F),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = 2, labels = "ve", lbound = 0.0001, name = "Ve"),
mxMatrix("Iden",nrow=100,name="I",condenseSlots=T),
mxAlgebra(I %x% Ve,name="V"),
mxExpectationGREML(V="V",Xvars=list(c("x","z1","z2")),yvars="y",addOnes=F),
mxFitFunctionML()
)
omxCheckError(mxRun(testmod),
"Cholesky factorization failed at initial values; possibly, the matrix of covariates is rank-deficient")
set.seed(476)
A1 <- matrix(0,100,100)
A1[lower.tri(A1)] <- runif(4950, -0.025, 0.025)
A1 <- A1 + t(A1)
diag(A1) <- runif(100,0.95,1.05)
A2 <- matrix(0,100,100)
A2[lower.tri(A2)] <- runif(4950, -0.025, 0.025)
A2 <- A2 + t(A2)
diag(A2) <- runif(100,0.95,1.05)
y <- t(rmvnorm(1,sigma=A1*0.25)+rmvnorm(1,sigma=A2*0.25))
y <- y + rnorm(100,sd=sqrt(0.5))
x <- rnorm(100)
dat3 <- cbind(y,x)
rm(x,y)
colnames(dat3) <- c("y","x")
testmod <- mxModel(
"GREMLtest",
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values =0.5, labels = "ve", lbound = 0.0001,
name = "Ve"),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = 0.25, labels = "va1", name = "Va1"),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = 0.25, labels = "va2", name = "Va2"),
mxData(observed = dat3, type="raw", sort=FALSE),
mxExpectationGREML(V="V",yvars="y", Xvars="x", addOnes=T),
mxMatrix("Iden",nrow=100,name="I"),
mxMatrix("Symm",nrow=100,free=F,values=A1,name="A1"),
mxMatrix("Symm",nrow=100,free=F,values=A2,name="A2"),
mxAlgebra((A1%x%Va1) + (A2%x%Va2) + (I%x%Ve), name="V"),
mxMatrix(type="Full",nrow=1,ncol=1,free=F,values=0.64,name="aug"),
mxMatrix(type="Zero",nrow=1,ncol=1,name="Zilch"),
mxFitFunctionGREML(dV=c(ve="I",va1="A1",va2="A2"),aug="aug",augHess="Zilch")
)
omxCheckError(
mxRun(testmod),
"if argument 'augHess' has nonzero length, then argument 'augGrad' must as well")
testmod$fitfunction <- mxFitFunctionGREML(dV=c(ve="I",va1="A1",va2="A2"),aug="aug")
omxCheckError(
mxRun(testmod),
"if arguments 'dV' and 'aug' have nonzero length, then 'augGrad' must as well")
testmod$fitfunction <- mxFitFunctionGREML(dV=c(ve="I",va1="A1",va2="A2",va3="I"))
omxCheckError(
mxRun(testmod),
"length of argument 'dV' is greater than the number of explicit free parameters")
testmod <- mxModel(
"GREMLtest",
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values =0.5, labels = "ve", lbound = 0.0001,
name = "Ve"),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = 0.25, labels = "va1", name = "Va1"),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = 0.25, labels = "va2", name = "Va2"),
mxData(observed = dat3, type="raw", sort=FALSE),
mxExpectationGREML(V="V",yvars="y", Xvars="x", addOnes=T),
mxMatrix("Iden",nrow=100,name="I"),
mxMatrix("Symm",nrow=100,free=F,values=A1,name="A1"),
mxMatrix("Symm",nrow=100,free=F,values=A2,name="A2"),
mxAlgebra((A1%x%Va1) + (A2%x%Va2) + (I%x%Ve), name="V"),
mxComputeSequence(steps=list(
mxComputeNewtonRaphson(fitfunction="fitfunction"),
mxComputeOnce('fitfunction', c('fit','gradient','hessian','ihessian')),
mxComputeStandardError(),
mxComputeReportDeriv(),
mxComputeReportExpectation()
)),
mxFitFunctionGREML(dV=c(ve="I",va1="A1",va2="A2",va3="I"))
)
omxCheckError(
mxRun(testmod),
"length of argument 'dV' is greater than the number of explicit free parameters")
testmod <- mxModel(
"GREMLtest",
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values =0.5, labels = "ve", lbound = 0.0001,
name = "Ve"),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = 0.25, labels = "va1", name = "Va1"),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = 0.25, labels = "va2", name = "Va2"),
mxData(observed = dat3, type="raw", sort=FALSE),
mxExpectationGREML(V="V",yvars="y", Xvars="x", addOnes=T),
mxMatrix("Iden",nrow=100,name="I"),
mxMatrix("Symm",nrow=100,free=F,values=A1,name="A1"),
mxMatrix("Symm",nrow=100,free=F,values=A2,name="A2"),
mxAlgebra((A1%x%Va1) + (A2%x%Va2) + (I%x%Ve), name="V"),
mxComputeSequence(steps=list(
mxComputeNewtonRaphson(fitfunction="fitfunction"),
mxComputeOnce('fitfunction', c('fit','gradient','hessian')),
mxComputeStandardError(),
mxComputeReportDeriv(),
mxComputeReportExpectation()
)),
mxMatrix(type="Full",nrow=1,ncol=1,free=F,values=2.1,name="aug"),
mxMatrix(type="Zero",nrow=2,ncol=1,free=F,name="ag"),
mxMatrix(type="Zero",nrow=3,ncol=3,free=F,name="ah"),
mxFitFunctionGREML(dV=c(ve="I",va1="A1",va2="A2"),aug="aug",augGrad="ag",augHess="ah")
)
omxCheckError(
mxRun(testmod),
"matrix referenced by 'augGrad' must have as many elements as there are explicit free parameters")
testmod$ag <- mxMatrix(type="Zero",nrow=3,ncol=1,free=F,name="ag")
testmod$ah <- mxMatrix(type="Zero",nrow=2,ncol=3,free=F,name="ah")
omxCheckError(
mxRun(testmod),
"matrix referenced by 'augHess' must be square (instead of 2x3)")
testmod$ah <- mxMatrix(type="Zero",nrow=2,ncol=2,free=F,name="ah")
omxCheckError(
mxRun(testmod),
"Augmentation derivatives non-conformable (gradient is size 3 and Hessian is 2x2)")
mxOption(reset=TRUE)
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