1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
|
#
# 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.
# ===========
# = history =
# ===========
# ~/bin/OpenMx/inst/models/failing/SimpleConfidenceIntervals.R
# 2017-04-14 04:35PM TBATES: update for mxFitFunctionMultigroup(c("MZ", "DZ"))
# 2019-03-24 05:06PM TBATES: Still FAILING at line > 167)
require(OpenMx)
# j repeatedly gets set to a large value at subnp.cpp line 893.
#
# This is the same algorithmic problem with RSOLNP and CSOLNP we talked
# about some time ago. So, CSOLNP needs boundaries for some problems to
# get to the optimum. If you remove the boundary, it will get stuck in a
# loop.
if (mxOption(NULL,"Default optimizer") != 'CSOLNP') stop("SKIP")
# mxOption(NULL, "Default optimizer", "NPSOL")
#Prepare Data
# -----------------------------------------------------------------------
data(twinData)
summary(twinData)
selVars <- c('bmi1','bmi2')
mzfData <- as.matrix(subset(twinData, zyg==1, c(bmi1,bmi2)))
dzfData <- as.matrix(subset(twinData, zyg==3, c(bmi1,bmi2)))
colMeans(mzfData,na.rm=TRUE)
colMeans(dzfData,na.rm=TRUE)
cov(mzfData,use="complete")
cov(dzfData,use="complete")
#Fit ACE Model with RawData and Matrices Input
# -----------------------------------------------------------------------
twinACEModel <- mxModel("twinACE",
mxModel("common",
# Matrices X, Y, and Z to store a, c, and e path coefficients
mxMatrix("Full", nrow=1, ncol=1, free=T, values=.6, lbound=.1, label="a", name="X"),
mxMatrix("Full", nrow=1, ncol=1, free=T, values=sqrt(.6), label="c", name="Y"),
mxMatrix("Full", nrow=1, ncol=1, free=T, values=.6, lbound=.1, label="e", name="Z"),
# Matrices A, C, and E compute variance components
mxAlgebra(expression=X %*% t(X), name="A"),
mxAlgebra(expression=Y %*% t(Y), name="C"),
mxAlgebra(expression=Z %*% t(Z), name="E"),
mxMatrix("Full", nrow=1, ncol=2, free=T, values= 20, lbound=0, ubound=30, label="mean", name="expMean"),
# Algebra for expected variance/covariance matrix in MZ
mxAlgebra(name="expCovMZ",
rbind(cbind(A+C+E, A+C),
cbind(A+C , A+C+E))
),
# Algebra for expected variance/covariance matrix in DZ
# note use of 0.5, converted to 1*1 matrix
mxAlgebra(name="expCovDZ", expression=
rbind(cbind(A+C+E , 0.5%x%A+C),
cbind(0.5%x%A+C , A+C+E))
)
),
mxModel("MZ",
mxData(observed=mzfData, type="raw"),
mxFitFunctionML(),
mxExpectationNormal("common.expCovMZ", means="common.expMean", dimnames=selVars)
),
mxModel("DZ",
mxData(observed=dzfData, type="raw"),
mxFitFunctionML(),
mxExpectationNormal("common.expCovDZ", means="common.expMean", dimnames=selVars)
),
mxFitFunctionMultigroup(c("MZ", "DZ")),
mxCI(c("common.A", "common.C[,]", "common.E[1,1]"))
)
#Run ACE model
# -----------------------------------------------------------------------
twinACENoIntervals <- mxRun(twinACEModel, suppressWarnings = TRUE)
twinACENoIntervals <- mxRun(twinACEModel)
twinACEFit <- mxRun(twinACEModel, intervals=TRUE)
# summary(twinACEFit)
detail <- twinACEFit$compute$steps[['CI']]$output[['detail']]
print(detail)
omxCheckTrue(is.factor(detail[['side']]))
omxCheckEquals(levels(detail[['side']]), c('upper', 'lower'))
ci <- twinACEFit$output$confidenceIntervals
# cat(deparse(round(ci[,'ubound'],4)))
omxCheckCloseEnough(ci[-2,'lbound'], c(0.556, 0.1537), .01)
omxCheckCloseEnough(ci[,'ubound'], c(0.683, 0.052, 0.1956), .005)
iterateMxRun <- function(model, maxIterations) {
model <- mxOption(model, "Optimality tolerance", 1e-6)
return(iterateMxRunHelper(mxRun(mxModel(model, mxComputeGradientDescent(maxMajorIter=150L))),
maxIterations, 1))
}
iterateMxRunHelper <- function(model, maxIterations, iteration) {
if (length(model$output) > 0 && model$output$status[[1]] == 0) {
return(model)
} else if (iteration < maxIterations) {
return(iterateMxRunHelper(mxRun(mxModel(model, mxComputeGradientDescent(maxMajorIter=150L))),
maxIterations, iteration + 1))
} else {
return(model)
}
}
twinACEIntervals <- twinACEFit
# twinACEIntervals$output <- list()
maxMisfit <- mxEval(objective, twinACEFit)[1,1] + 3.84
CIaupper <- mxModel(twinACEIntervals, name = 'A_CIupper',
mxConstraint(MZ.objective + DZ.objective < maxMisfit),
mxAlgebra(- common.A, name="upperCIa"),
mxFitFunctionAlgebra("upperCIa")
)
CIalower <- mxModel(twinACEIntervals, name = 'A_CIlower',
mxConstraint(MZ.objective + DZ.objective < maxMisfit),
mxAlgebra(common.A, name="lowerCIa"),
mxFitFunctionAlgebra("lowerCIa")
)
runCIalower <- suppressWarnings(iterateMxRun(CIalower, 3))
runCIaupper <- suppressWarnings(iterateMxRun(CIaupper, 3))
CIcupper <- mxModel(twinACEIntervals, name = 'C_CIupper',
mxConstraint(MZ.objective + DZ.objective < maxMisfit),
mxAlgebra(- common.C,name="upperCIc"),
mxFitFunctionAlgebra("upperCIc")
)
runCIcupper <- suppressWarnings(iterateMxRun(CIcupper, 3))
CIeupper <- mxModel(twinACEIntervals, name = 'E_CIupper',
mxConstraint(MZ.objective + DZ.objective < maxMisfit),
mxAlgebra(- common.E,name="upperCIe"),
mxFitFunctionAlgebra("upperCIe")
)
CIelower <- mxModel(twinACEIntervals, name = 'E_CIlower',
mxConstraint(MZ.objective + DZ.objective < maxMisfit),
mxAlgebra(common.E,name="lowerCIe"),
mxFitFunctionAlgebra("lowerCIe")
)
runCIelower <- suppressWarnings(iterateMxRun(CIelower, 3))
runCIeupper <- suppressWarnings(iterateMxRun(CIeupper, 3))
omxCheckCloseEnough(twinACEFit$output$confidenceIntervals[1, 'lbound'], mxEval(common.A, runCIalower), .01)
omxCheckCloseEnough(twinACEFit$output$confidenceIntervals[1, 'ubound'], mxEval(common.A, runCIaupper), .01)
# Can go either way
# omxCheckTrue(is.na(twinACEFit$output$confidenceIntervals[2, 'lbound']))
# Next check still failing under 2.7.9
# Still failing as of OpenMx version: 2.12.2.233 [GIT v2.12.2-233-ga7a310a]
omxCheckCloseEnough(twinACEFit$output$confidenceIntervals[2, 'ubound'], mxEval(common.C, runCIcupper), .001)
omxCheckCloseEnough(twinACEFit$output$confidenceIntervals[3, 'lbound'], mxEval(common.E, runCIelower), .005)
omxCheckCloseEnough(twinACEFit$output$confidenceIntervals[3, 'ubound'], mxEval(common.E, runCIeupper), .005)
twinACEParallel <- omxParallelCI(twinACENoIntervals)
if (0) {
twinACEFit$output$confidenceIntervals - twinACEParallel$output$confidenceIntervals
}
mask <- !is.na(twinACEFit$output$confidenceIntervals)
omxCheckCloseEnough(twinACEFit$output$confidenceIntervals[mask],
twinACEParallel$output$confidenceIntervals[mask], .001)
|
