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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.
# -----------------------------------------------------------------------
# Mixture distribution model: probabilistically diagnosed zygosity
#
# After Neale (2003) A finite mixture distribution model for data collected from twins.
# Twin Res 2003; 6:235-239
#
# ACE Model is specified with RawData and Matrix-style Input
#
# Uses two probabilities: that of correct zygosity diagnosis, pright;
# and that of incorrect, pwrong. These could be different for MZ and DZ pairs
# but are not here. Using definition variables each pair could have its own
# probability of correct diagnosis. That is not done here either.
#
# -----------------------------------------------------------------------
library(OpenMx)
library(testthat)
DataMZ <- suppressWarnings(try(read.table("models/passing/data/sim1.mz", header=FALSE), silent=TRUE))
if (is(DataMZ, "try-error")) DataMZ <- read.table("data/sim1.mz", header = F)
selVars <- c("T1", "T2", "pMZ")
names(DataMZ) <- selVars
frameMZ <- data.frame(pMZ = DataMZ$pMZ)
twinACEModel <- mxModel("twinACE",
# Matrix expMean for expected mean vector for MZ and DZ twins
mxMatrix("Full", nrow=1, ncol=2, free=TRUE, values=0,
label="mean", name="expMean"),
# Matrices X, Y, and Z to store the a, c, and e path coefficients
mxMatrix("Full", nrow=1, ncol=1, free=TRUE, values=.6, label="a", name="X"),
mxMatrix("Full", nrow=1, ncol=1, free=TRUE, values=.6, label="c", name="Y"),
mxMatrix("Full", nrow=1, ncol=1, free=TRUE, values=.6, label="e", name="Z", lbound=.1, ubound=10),
# Matrixes A, C, and E to compute A, C, and E variance components
mxAlgebra(X * t(X), name="A"),
mxAlgebra(Y * t(Y), name="C"),
mxAlgebra(Z * t(Z), name="E"),
# Matrix expCOVMZ for expected covariance matrix for MZ twins
mxAlgebra(rbind(cbind(A+C+E , A+C),
cbind(A+C , A+C+E)), name="expCovMZ"),
# Matrix expCOVMZ for expected covariance matrix for DZ twins
mxAlgebra(rbind(cbind(A+C+E , .5%x%A+C),
cbind(.5%x%A+C , A+C+E)), name="expCovDZ"),
#
# MZ likelihood is set up as pright*(Likelihood|Zygosity=MZ) + pwrong*(Likelihood|DZ)
# DZ likelihood is set up as pright*(Likelihood|Zygosity=DZ) + pwrong*(Likelihood|MZ)
#
# vector=TRUE argument to mxFitFunctionML(),mxExpectationNormal allows mixture distribution on individual likelihoods
#
mxModel("MZlike",
mxData(DataMZ, type="raw"),
mxExpectationNormal("twinACE.expCovMZ", "twinACE.expMean",c("T1","T2")),
mxFitFunctionML(vector=T)
),
mxModel("DZlike",
mxData(DataMZ, type="raw"),
mxExpectationNormal("twinACE.expCovDZ", "twinACE.expMean",c("T1","T2")),
mxFitFunctionML(vector=T)
),
mxMatrix(type="Full", nrow=dim(frameMZ)[1], ncol=1, values = frameMZ$pMZ, name="pMZ"),
mxMatrix(type="Unit", nrow=dim(frameMZ)[1], ncol=1, name="unit"),
mxAlgebra(-2*sum(log(pMZ * MZlike.objective + (unit-pMZ) * DZlike.objective)), name="twin"),
mxFitFunctionAlgebra("twin")
)
twinACEFit <- mxRun(twinACEModel)
omxCheckCloseEnough(twinACEFit$output$fit, 4715.549, 1e-2)
# ---
crazy <- mxModel(twinACEModel, mxPenaltyLASSO(c('a','c','e'),
lambda = 1, name="lasso"))
expect_error(mxRun(crazy), "The mind boggles")
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