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
187
|
#
# 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)
library(testthat)
#This test does not need to be run with all three GD optimizers:
if(mxOption(NULL,"Default optimizer")!="CSOLNP"){stop("SKIP")}
options(mxCondenseMatrixSlots=TRUE) #<--Saves memory
require(mvtnorm)
#Generate data:
set.seed(476)
A <- matrix(0,1000,1000) #<--Empty GRM
A[lower.tri(A)] <- runif(499500, -0.025, 0.025)
A <- A + t(A)
diag(A) <- runif(1000,0.95,1.05) #<--GRM now complete
y <- t(rmvnorm(1,sigma=A*0.5)) #<--Phenotype 'y' has a "population" variance of 1 and h2 of 0.5
y <- y + rnorm(1000,sd=sqrt(0.5))
x <- rnorm(1000) #<--Covariate 'x' is actually independent of the phenotype.
#Merge variables into data matrix:
dat <- cbind(y,x)
colnames(dat) <- c("y","x") #<--Column names
ge <- mxExpectationGREML(V="V",yvars="y", Xvars="x", addOnes=T)
gff <- mxFitFunctionGREML(dV=c(va="A",ve="I"))
plan <- mxComputeSequence(steps=list(
mxComputeNewtonRaphson(fitfunction="fitfunction"),
mxComputeOnce('fitfunction', c('fit','gradient','hessian','ihessian')),
mxComputeStandardError(),
mxComputeReportDeriv(),
mxComputeReportExpectation()
))
mxdat <- mxData(observed = dat, type="raw", sort=FALSE)
testmod <- mxModel(
"GREML_1GRM_1trait_A", #<--Model name
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = var(y)/2, labels = "ve", lbound = 0.0001,
name = "Ve"),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = var(y)/2, labels = "va", name = "Va"),
mxMatrix("Iden",nrow=1000,name="I"),
mxMatrix("Symm",nrow=1000,free=F,values=A,name="A"),
mxAlgebra((A%x%Va) + (I%x%Ve), name="V"),
mxAlgebra(Va/(Va+Ve), name="h2"),
mxdat, #<--MxData object
ge, #<--GREML expectation
gff, #<--GREML fitfunction
plan #<--Custom compute plan
)
testrun <- mxRun(testmod)
summary(testrun)
#Drop the covariate:
ge2 <- mxExpectationGREML(V="V",yvars="y", addOnes=T)
testmod2 <- mxModel(
"GREML_1GRM_1trait_B", #<--Model name
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = var(y)/2, labels = "ve", lbound = 0.0001,
name = "Ve"),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = var(y)/2, labels = "va", name = "Va"),
mxMatrix("Iden",nrow=1000,name="I"),
mxMatrix("Symm",nrow=1000,free=F,values=A,name="A"),
mxAlgebra((A%x%Va) + (I%x%Ve), name="V"),
mxAlgebra(Va/(Va+Ve), name="h2"),
mxdat, #<--MxData object
ge2, #<--GREML expectation
gff, #<--GREML fitfunction
plan #<--Custom compute plan
)
testrun2 <- mxRun(testmod2)
summary(testrun2)
#Model with fewer free parameters has "better" fit?:
testrun$fitfunction$result
testrun2$fitfunction$result
#But MLfit looks OK:
testrun$fitfunction$MLfit
testrun2$fitfunction$MLfit
omxCheckWarning(
mxCompare(testrun,testrun2),
"the names of the fixed effects in MxModels 'GREML_1GRM_1trait_A' and 'GREML_1GRM_1trait_B' do not match; comparison of REML fit values is only valid for models that use the same covariates"
)
rm(testmod,testmod2,testrun,testrun2,A,dat,y,ge,ge2,gff,mxdat,plan,x); gc()
set.seed(1234)
dat <- cbind(rnorm(100),rep(1,100))
colnames(dat) <- c("y","x")
ge <- mxExpectationGREML(V="V",yvars="y", Xvars="x", addOnes=FALSE)
gff <- mxFitFunctionGREML(dV=c(ve="I"))
plan <- mxComputeSequence(steps=list(
mxComputeNewtonRaphson(fitfunction="fitfunction"),
mxComputeOnce('fitfunction', c('fit','gradient','hessian','ihessian')),
mxComputeStandardError(),
mxComputeReportDeriv(),
mxComputeReportExpectation()
))
remlmod <- mxModel(
"GREMLtest",
mxData(observed = dat, type="raw", sort=FALSE),
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"),
ge,
gff,
plan
)
remlrun <- mxRun(remlmod)
mlmod <- mxModel(
"MLtest",
mxData(observed = dat, type="raw"),
mxMatrix(type = "Full", nrow = 1, ncol=1, free=T, values = 2, labels = "ve", lbound = 0.0001, name = "Ve"),
mxMatrix(type="Full",nrow=1,ncol=1,free=T,values=0.1,labels="mu",name="Mu"),
mxExpectationNormal(covariance="Ve",means="Mu",dimnames="y"),
mxFitFunctionML()
)
mlrun <- mxRun(mlmod)
omxCheckError(
mxCompare(remlrun,mlrun),
"MxModel 'GREMLtest' has a fitfunction of class 'MxFitFunctionGREML', but MxModel 'MLtest' has a fitfunction of class 'MxFitFunctionML'"
)
data(factorExample1)
indicators <- names(factorExample1)
latents <- c("F1")
loadingLabels <- paste("b_", indicators, sep="")
uniqueLabels <- paste("U_", indicators, sep="")
meanLabels <- paste("M_", indicators, sep="")
factorVarLabels <- paste("Var_", latents, sep="")
oneFactorCov1 <- mxModel("Single Factor Covariance Model with Fixed Variance",
type="RAM",
manifestVars=indicators,
latentVars=latents,
mxPath(from=latents, to=indicators,
# arrows=1, all=TRUE,
arrows=1, connect="all.pairs",
free=TRUE, values=.2,
labels=loadingLabels),
mxPath(from=indicators,
arrows=2,
free=TRUE, values=.8,
labels=uniqueLabels),
mxPath(from=latents,
arrows=2,
free=FALSE, values=1,
labels=factorVarLabels),
mxData(observed=cov(factorExample1), type="cov", numObs=500)
)
oneFactorCov1Out <- mxRun(oneFactorCov1)
oneFactorCovWLS <- mxModel(oneFactorCov1Out, name='WLS',
mxData(factorExample1, 'raw'),
mxFitFunctionWLS()
)
oneFactorCovWLS <- omxSetParameters(model=oneFactorCovWLS,labels="b_x1",free=F,values=0)
oneFactorCovWLSOut <- mxRun(oneFactorCovWLS)
expect_error(mxCompare(oneFactorCov1Out,oneFactorCovWLSOut),
"but MxModel 'WLS' has 'r'Wr' fit units")
|
