data
08-Mar-2019
options(width=120, scipen=2, digits=2)
suppressPackageStartupMessages(library(OpenMx))
suppressPackageStartupMessages(library(rpf))
suppressPackageStartupMessages(library(ifaTools))
library(xtable)
options(xtable.type='html')
# Adjust the path in the next statement to load your data
data <- read.csv(na.strings=c(), file='fake.csv',stringsAsFactors=FALSE,check.names=FALSE)
colnames(data) <- mxMakeNames(colnames(data), unique=TRUE)
data[['case']] <- NULL # excluded
data[['twin']] <- NULL # excluded
data[['vetsaid']] <- NULL # excluded
data[['age']] <- NULL # excluded
data[['afqtrt']] <- NULL # excluded
data[['afqtms']] <- NULL # excluded
data[['afqtpct']] <- NULL # excluded
data[['afqtpcttran']] <- NULL # excluded
data[['zyg10']] <- NULL # excluded
data[['afqtwg']] <- NULL # excluded
factors <- "afqt"
numFactors <- length(factors)
spec <- list()
spec[1:100] <- list(rpf.grm(factors=numFactors,outcomes=2))
names(spec) <- c("afqt1n", "afqt2n", "afqt3n", "afqt4n", "afqt5n", "afqt6n",
"afqt7n", "afqt8n", "afqt9n", "afqt10n", "afqt11n", "afqt12n",
"afqt13n", "afqt14n", "afqt15n", "afqt16n", "afqt17n", "afqt18n",
"afqt19n", "afqt20n", "afqt21n", "afqt22n", "afqt23n", "afqt24n",
"afqt25n", "afqt26n", "afqt27n", "afqt28n", "afqt29n", "afqt30n",
"afqt31n", "afqt32n", "afqt33n", "afqt34n", "afqt35n", "afqt36n",
"afqt37n", "afqt38n", "afqt39n", "afqt40n", "afqt41n", "afqt42n",
"afqt43n", "afqt44n", "afqt45n", "afqt46n", "afqt47n", "afqt48n",
"afqt49n", "afqt50n", "afqt51n", "afqt52n", "afqt53n", "afqt54n",
"afqt55n", "afqt56n", "afqt57n", "afqt58n", "afqt59n", "afqt60n",
"afqt61n", "afqt62n", "afqt63n", "afqt64n", "afqt65n", "afqt66n",
"afqt67n", "afqt68n", "afqt69n", "afqt70n", "afqt71n", "afqt72n",
"afqt73n", "afqt74n", "afqt75n", "afqt76n", "afqt77n", "afqt78n",
"afqt79n", "afqt80n", "afqt81n", "afqt82n", "afqt83n", "afqt84n",
"afqt85n", "afqt86n", "afqt87n", "afqt88n", "afqt89n", "afqt90n",
"afqt91n", "afqt92n", "afqt93n", "afqt94n", "afqt95n", "afqt96n",
"afqt97n", "afqt98n", "afqt99n", "afqt100n")
missingColumns <- which(is.na(match(names(spec), colnames(data))))
if (length(missingColumns)) {
stop(paste('Columns missing in the data:', omxQuotes(names(spec)[missingColumns])))
}
for (col in c("afqt1n", "afqt2n", "afqt6n", "afqt8n", "afqt25n", "afqt49n"
)) {
data[[col]] <- mxFactor(data[[col]], levels=0:1)
}
for (col in c("afqt3n", "afqt4n", "afqt5n", "afqt7n", "afqt9n", "afqt10n",
"afqt11n", "afqt12n", "afqt13n", "afqt14n", "afqt15n", "afqt16n",
"afqt17n", "afqt18n", "afqt19n", "afqt20n", "afqt21n", "afqt22n",
"afqt23n", "afqt24n", "afqt26n", "afqt27n", "afqt28n", "afqt29n",
"afqt30n", "afqt31n", "afqt32n", "afqt33n", "afqt34n", "afqt35n",
"afqt36n", "afqt37n", "afqt38n", "afqt39n", "afqt40n", "afqt41n",
"afqt42n", "afqt43n", "afqt44n", "afqt45n", "afqt46n", "afqt47n",
"afqt48n", "afqt50n", "afqt51n", "afqt52n", "afqt53n", "afqt54n",
"afqt55n", "afqt56n", "afqt57n", "afqt58n", "afqt59n", "afqt60n",
"afqt61n", "afqt62n", "afqt63n", "afqt64n", "afqt65n", "afqt66n",
"afqt67n", "afqt68n", "afqt69n", "afqt70n", "afqt71n", "afqt72n",
"afqt73n", "afqt74n", "afqt75n", "afqt76n", "afqt77n", "afqt78n",
"afqt79n", "afqt80n", "afqt81n", "afqt82n", "afqt83n", "afqt84n",
"afqt85n", "afqt86n", "afqt87n", "afqt88n", "afqt89n", "afqt90n",
"afqt91n", "afqt92n", "afqt93n", "afqt94n", "afqt95n", "afqt96n",
"afqt97n", "afqt98n", "afqt99n", "afqt100n")) {
data[[col]] <- mxFactor(data[[col]], levels=c("0", "1"), exclude="NA")
}
#set.seed(1) # uncomment to get the same starting values every time
startingValues <- mxSimplify2Array(lapply(spec, rpf.rparam))
rownames(startingValues) <- paste0('p', 1:nrow(startingValues))
rownames(startingValues)[1:numFactors] <- factors
imat <- mxMatrix(name='item', values=startingValues, free=!is.na(startingValues))
# Remove non-factor columns (if any)
data <- data[,sapply(data, is.factor)]
# Remove all-missing rows
data <- data[rowSums(!is.na(data[,names(spec)])) != 0,]
itemModel <- mxModel(model='itemModel', imat,
mxData(observed=data, type='raw'),
mxExpectationBA81(ItemSpec=spec),
mxFitFunctionML())
emStep <- mxComputeEM('itemModel.expectation', 'scores',
mxComputeNewtonRaphson(), verbose=2L,
information='oakes1999', infoArgs=list(fitfunction='fitfunction'))
computePlan <- mxComputeSequence(list(EM=emStep,
HQ=mxComputeHessianQuality(),
SE=mxComputeStandardError()))
itemModel <- mxModel(itemModel, computePlan)
m1Fit <- mxRun(itemModel)## Running itemModel with 200 parametersm1Grp <- as.IFAgroup(m1Fit, minItemsPerScore=1L)
if(0) {
fake <- rpf.sample(1300, grp=m1Grp)
write.csv(file="fake.csv", fake, row.names = FALSE)
}An item factor model was fit with 1 factors (afqt), -2LL=\(99981.66\). The condition number of the information matrix was 138.
got <- sumScoreEAPTest(m1Grp)
df <- data.frame(score=as.numeric(names(got[['observed']])),
expected=got[['expected']], observed=got[['observed']])
df <- melt(df, id='score', variable.name='source', value.name='n')
ggplot(df, aes(x=score, y=n, color=source)) + geom_line()
basis <- rep(0, length(factors))
basis[1] <- 1
plotInformation(m1Grp, width=5, basis=basis)
summary(m1Fit, refModels=mxRefModels(itemModel, run = TRUE))## Running Independence itemModel with 100 parameters## Summary of itemModel
##
## free parameters:
## name matrix row col Estimate Std.Error
## 1 itemModel.item[1,1] item afqt afqt1n 0.519306 0.165
## 2 itemModel.item[2,1] item p2 afqt1n 3.500406 0.175
## 3 itemModel.item[1,2] item afqt afqt2n 0.851001 0.190
## 4 itemModel.item[2,2] item p2 afqt2n 3.954312 0.230
## 5 itemModel.item[1,3] item afqt afqt3n 1.301895 0.316
## 6 itemModel.item[2,3] item p2 afqt3n 5.405965 0.473
## 7 itemModel.item[1,4] item afqt afqt4n 0.423217 0.115
## 8 itemModel.item[2,4] item p2 afqt4n 2.627637 0.116
## 9 itemModel.item[1,5] item afqt afqt5n 1.533642 0.199
## 10 itemModel.item[2,5] item p2 afqt5n 4.277461 0.282
## 11 itemModel.item[1,6] item afqt afqt6n 1.454759 0.252
## 12 itemModel.item[2,6] item p2 afqt6n 4.941308 0.379
## 13 itemModel.item[1,7] item afqt afqt7n 0.961298 0.138
## 14 itemModel.item[2,7] item p2 afqt7n 3.152675 0.162
## 15 itemModel.item[1,8] item afqt afqt8n 1.527558 0.157
## 16 itemModel.item[2,8] item p2 afqt8n 3.430762 0.197
## 17 itemModel.item[1,9] item afqt afqt9n 0.801311 0.160
## 18 itemModel.item[2,9] item p2 afqt9n 3.530204 0.187
## 19 itemModel.item[1,10] item afqt afqt10n 0.782948 0.143
## 20 itemModel.item[2,10] item p2 afqt10n 3.246794 0.164
## 21 itemModel.item[1,11] item afqt afqt11n 1.529634 0.239
## 22 itemModel.item[2,11] item p2 afqt11n 4.826001 0.359
## 23 itemModel.item[1,12] item afqt afqt12n 0.745030 0.095
## 24 itemModel.item[2,12] item p2 afqt12n 2.045126 0.098
## 25 itemModel.item[1,13] item afqt afqt13n 1.210869 0.117
## 26 itemModel.item[2,13] item p2 afqt13n 2.464664 0.127
## 27 itemModel.item[1,14] item afqt afqt14n 1.523256 0.154
## 28 itemModel.item[2,14] item p2 afqt14n 3.371762 0.192
## 29 itemModel.item[1,15] item afqt afqt15n 0.749700 0.100
## 30 itemModel.item[2,15] item p2 afqt15n 2.210445 0.104
## 31 itemModel.item[1,16] item afqt afqt16n 0.539293 0.102
## 32 itemModel.item[2,16] item p2 afqt16n 2.328559 0.104
## 33 itemModel.item[1,17] item afqt afqt17n 1.050151 0.167
## 34 itemModel.item[2,17] item p2 afqt17n 3.706741 0.210
## 35 itemModel.item[1,18] item afqt afqt18n 1.895393 0.336
## 36 itemModel.item[2,18] item p2 afqt18n 5.939548 0.575
## 37 itemModel.item[1,19] item afqt afqt19n 0.275385 0.172
## 38 itemModel.item[2,19] item p2 afqt19n 3.511748 0.169
## 39 itemModel.item[1,20] item afqt afqt20n 0.996381 0.209
## 40 itemModel.item[2,20] item p2 afqt20n 4.243695 0.269
## 41 itemModel.item[1,21] item afqt afqt21n 1.203860 0.129
## 42 itemModel.item[2,21] item p2 afqt21n 2.869236 0.149
## 43 itemModel.item[1,22] item afqt afqt22n 1.295165 0.127
## 44 itemModel.item[2,22] item p2 afqt22n 2.746438 0.145
## 45 itemModel.item[1,23] item afqt afqt23n 1.337227 0.124
## 46 itemModel.item[2,23] item p2 afqt23n 2.592688 0.137
## 47 itemModel.item[1,24] item afqt afqt24n 1.289744 0.118
## 48 itemModel.item[2,24] item p2 afqt24n 2.421514 0.127
## 49 itemModel.item[1,25] item afqt afqt25n 0.432004 0.256
## 50 itemModel.item[2,25] item p2 afqt25n 4.414619 0.267
## 51 itemModel.item[1,26] item afqt afqt26n 0.698465 0.121
## 52 itemModel.item[2,26] item p2 afqt26n 2.790414 0.131
## 53 itemModel.item[1,27] item afqt afqt27n 0.569492 0.110
## 54 itemModel.item[2,27] item p2 afqt27n 2.523196 0.114
## 55 itemModel.item[1,28] item afqt afqt28n 0.966989 0.108
## 56 itemModel.item[2,28] item p2 afqt28n 2.375701 0.117
## 57 itemModel.item[1,29] item afqt afqt29n 0.893294 0.097
## 58 itemModel.item[2,29] item p2 afqt29n 1.977114 0.099
## 59 itemModel.item[1,30] item afqt afqt30n 1.067445 0.103
## 60 itemModel.item[2,30] item p2 afqt30n 2.060613 0.106
## 61 itemModel.item[1,31] item afqt afqt31n 1.198214 0.111
## 62 itemModel.item[2,31] item p2 afqt31n 2.241908 0.117
## 63 itemModel.item[1,32] item afqt afqt32n 1.158323 0.100
## 64 itemModel.item[2,32] item p2 afqt32n 1.739936 0.097
## 65 itemModel.item[1,33] item afqt afqt33n 0.677663 0.158
## 66 itemModel.item[2,33] item p2 afqt33n 3.449058 0.176
## 67 itemModel.item[1,34] item afqt afqt34n 0.906744 0.239
## 68 itemModel.item[2,34] item p2 afqt34n 4.501976 0.302
## 69 itemModel.item[1,35] item afqt afqt35n 1.889090 0.208
## 70 itemModel.item[2,35] item p2 afqt35n 4.342541 0.294
## 71 itemModel.item[1,36] item afqt afqt36n 1.139331 0.135
## 72 itemModel.item[2,36] item p2 afqt36n 3.075710 0.161
## 73 itemModel.item[1,37] item afqt afqt37n 1.777994 0.148
## 74 itemModel.item[2,37] item p2 afqt37n 2.849057 0.164
## 75 itemModel.item[1,38] item afqt afqt38n 1.271101 0.125
## 76 itemModel.item[2,38] item p2 afqt38n 2.688827 0.141
## 77 itemModel.item[1,39] item afqt afqt39n 0.957845 0.100
## 78 itemModel.item[2,39] item p2 afqt39n 2.059473 0.103
## 79 itemModel.item[1,40] item afqt afqt40n 1.738177 0.148
## 80 itemModel.item[2,40] item p2 afqt40n 2.921541 0.167
## 81 itemModel.item[1,41] item afqt afqt41n 0.686691 0.108
## 82 itemModel.item[2,41] item p2 afqt41n 2.480811 0.115
## 83 itemModel.item[1,42] item afqt afqt42n 0.726805 0.104
## 84 itemModel.item[2,42] item p2 afqt42n 2.355289 0.110
## 85 itemModel.item[1,43] item afqt afqt43n 1.011687 0.146
## 86 itemModel.item[2,43] item p2 afqt43n 3.332577 0.177
## 87 itemModel.item[1,44] item afqt afqt44n 0.570820 0.083
## 88 itemModel.item[2,44] item p2 afqt44n 1.660510 0.082
## 89 itemModel.item[1,45] item afqt afqt45n 0.841734 0.080
## 90 itemModel.item[2,45] item p2 afqt45n 1.002243 0.072
## 91 itemModel.item[1,46] item afqt afqt46n 0.810964 0.082
## 92 itemModel.item[2,46] item p2 afqt46n 1.284009 0.077
## 93 itemModel.item[1,47] item afqt afqt47n 1.304706 0.110
## 94 itemModel.item[2,47] item p2 afqt47n 1.988341 0.109
## 95 itemModel.item[1,48] item afqt afqt48n 1.448641 0.110
## 96 itemModel.item[2,48] item p2 afqt48n 1.566871 0.099
## 97 itemModel.item[1,49] item afqt afqt49n 0.629934 0.083
## 98 itemModel.item[2,49] item p2 afqt49n 1.604057 0.081
## 99 itemModel.item[1,50] item afqt afqt50n 1.148311 0.160
## 100 itemModel.item[2,50] item p2 afqt50n 3.592203 0.202
## 101 itemModel.item[1,51] item afqt afqt51n 1.156230 0.119
## 102 itemModel.item[2,51] item p2 afqt51n 2.611242 0.133
## 103 itemModel.item[1,52] item afqt afqt52n 0.599104 0.159
## 104 itemModel.item[2,52] item p2 afqt52n 3.447913 0.173
## 105 itemModel.item[1,53] item afqt afqt53n 1.232661 0.107
## 106 itemModel.item[2,53] item p2 afqt53n 1.978129 0.107
## 107 itemModel.item[1,54] item afqt afqt54n 1.923377 0.165
## 108 itemModel.item[2,54] item p2 afqt54n 3.237226 0.193
## 109 itemModel.item[1,55] item afqt afqt55n 1.266148 0.105
## 110 itemModel.item[2,55] item p2 afqt55n 1.761338 0.100
## 111 itemModel.item[1,56] item afqt afqt56n 0.976175 0.092
## 112 itemModel.item[2,56] item p2 afqt56n 1.622542 0.089
## 113 itemModel.item[1,57] item afqt afqt57n 0.616686 0.100
## 114 itemModel.item[2,57] item p2 afqt57n 2.267213 0.103
## 115 itemModel.item[1,58] item afqt afqt58n 0.650786 0.074
## 116 itemModel.item[2,58] item p2 afqt58n 0.989997 0.069
## 117 itemModel.item[1,59] item afqt afqt59n 0.485276 0.070
## 118 itemModel.item[2,59] item p2 afqt59n 0.919333 0.065
## 119 itemModel.item[1,60] item afqt afqt60n 0.390223 0.075
## 120 itemModel.item[2,60] item p2 afqt60n 1.377630 0.072
## 121 itemModel.item[1,61] item afqt afqt61n 1.068838 0.088
## 122 itemModel.item[2,61] item p2 afqt61n 1.016791 0.077
## 123 itemModel.item[1,62] item afqt afqt62n 1.346830 0.102
## 124 itemModel.item[2,62] item p2 afqt62n 1.179320 0.087
## 125 itemModel.item[1,63] item afqt afqt63n 0.539325 0.067
## 126 itemModel.item[2,63] item p2 afqt63n 0.466997 0.061
## 127 itemModel.item[1,64] item afqt afqt64n 1.227447 0.107
## 128 itemModel.item[2,64] item p2 afqt64n 1.988108 0.107
## 129 itemModel.item[1,65] item afqt afqt65n 0.715183 0.095
## 130 itemModel.item[2,65] item p2 afqt65n 2.030909 0.096
## 131 itemModel.item[1,66] item afqt afqt66n 1.465276 0.177
## 132 itemModel.item[2,66] item p2 afqt66n 3.890972 0.238
## 133 itemModel.item[1,67] item afqt afqt67n 1.367252 0.118
## 134 itemModel.item[2,67] item p2 afqt67n 2.280497 0.123
## 135 itemModel.item[1,68] item afqt afqt68n 0.315321 0.113
## 136 itemModel.item[2,68] item p2 afqt68n 2.571237 0.111
## 137 itemModel.item[1,69] item afqt afqt69n 0.871627 0.081
## 138 itemModel.item[2,69] item p2 afqt69n 0.997913 0.073
## 139 itemModel.item[1,70] item afqt afqt70n 0.934470 0.081
## 140 itemModel.item[2,70] item p2 afqt70n 0.723456 0.070
## 141 itemModel.item[1,71] item afqt afqt71n 1.351254 0.104
## 142 itemModel.item[2,71] item p2 afqt71n 1.403380 0.092
## 143 itemModel.item[1,72] item afqt afqt72n 0.852137 0.077
## 144 itemModel.item[2,72] item p2 afqt72n 0.539187 0.067
## 145 itemModel.item[1,73] item afqt afqt73n 0.607448 0.084
## 146 itemModel.item[2,73] item p2 afqt73n 1.656535 0.082
## 147 itemModel.item[1,74] item afqt afqt74n 0.598837 0.078
## 148 itemModel.item[2,74] item p2 afqt74n 1.337091 0.074
## 149 itemModel.item[1,75] item afqt afqt75n 0.658518 0.075
## 150 itemModel.item[2,75] item p2 afqt75n 1.037030 0.069
## 151 itemModel.item[1,76] item afqt afqt76n 0.843053 0.101
## 152 itemModel.item[2,76] item p2 afqt76n 2.198408 0.106
## 153 itemModel.item[1,77] item afqt afqt77n 0.819065 0.075
## 154 itemModel.item[2,77] item p2 afqt77n 0.059446 0.064
## 155 itemModel.item[1,78] item afqt afqt78n 1.010964 0.087
## 156 itemModel.item[2,78] item p2 afqt78n 1.096308 0.077
## 157 itemModel.item[1,79] item afqt afqt79n 0.920054 0.080
## 158 itemModel.item[2,79] item p2 afqt79n 0.580324 0.068
## 159 itemModel.item[1,80] item afqt afqt80n 1.856432 0.129
## 160 itemModel.item[2,80] item p2 afqt80n 1.063101 0.098
## 161 itemModel.item[1,81] item afqt afqt81n 0.646501 0.075
## 162 itemModel.item[2,81] item p2 afqt81n 1.109496 0.070
## 163 itemModel.item[1,82] item afqt afqt82n 1.086728 0.114
## 164 itemModel.item[2,82] item p2 afqt82n 2.489232 0.125
## 165 itemModel.item[1,83] item afqt afqt83n 0.595484 0.069
## 166 itemModel.item[2,83] item p2 afqt83n 0.632312 0.063
## 167 itemModel.item[1,84] item afqt afqt84n 0.912612 0.094
## 168 itemModel.item[2,84] item p2 afqt84n 1.822357 0.094
## 169 itemModel.item[1,85] item afqt afqt85n 1.169904 0.090
## 170 itemModel.item[2,85] item p2 afqt85n 0.534801 0.073
## 171 itemModel.item[1,86] item afqt afqt86n 1.004961 0.082
## 172 itemModel.item[2,86] item p2 afqt86n 0.405005 0.069
## 173 itemModel.item[1,87] item afqt afqt87n 0.547156 0.067
## 174 itemModel.item[2,87] item p2 afqt87n -0.135326 0.059
## 175 itemModel.item[1,88] item afqt afqt88n 0.930041 0.081
## 176 itemModel.item[2,88] item p2 afqt88n -0.621989 0.069
## 177 itemModel.item[1,89] item afqt afqt89n 0.494449 0.066
## 178 itemModel.item[2,89] item p2 afqt89n 0.398215 0.060
## 179 itemModel.item[1,90] item afqt afqt90n 0.443010 0.065
## 180 itemModel.item[2,90] item p2 afqt90n 0.377516 0.059
## 181 itemModel.item[1,91] item afqt afqt91n 0.463393 0.064
## 182 itemModel.item[2,91] item p2 afqt91n -0.000077 0.058
## 183 itemModel.item[1,92] item afqt afqt92n 0.449527 0.065
## 184 itemModel.item[2,92] item p2 afqt92n -0.311895 0.059
## 185 itemModel.item[1,93] item afqt afqt93n 0.766742 0.073
## 186 itemModel.item[2,93] item p2 afqt93n -0.185288 0.063
## 187 itemModel.item[1,94] item afqt afqt94n 1.014241 0.082
## 188 itemModel.item[2,94] item p2 afqt94n 0.367859 0.068
## 189 itemModel.item[1,95] item afqt afqt95n 1.055006 0.084
## 190 itemModel.item[2,95] item p2 afqt95n -0.183177 0.068
## 191 itemModel.item[1,96] item afqt afqt96n 0.289724 0.070
## 192 itemModel.item[2,96] item p2 afqt96n -1.077998 0.065
## 193 itemModel.item[1,97] item afqt afqt97n 0.196293 0.076
## 194 itemModel.item[2,97] item p2 afqt97n -1.446843 0.071
## 195 itemModel.item[1,98] item afqt afqt98n 0.805098 0.075
## 196 itemModel.item[2,98] item p2 afqt98n -0.261834 0.064
## 197 itemModel.item[1,99] item afqt afqt99n 0.492514 0.068
## 198 itemModel.item[2,99] item p2 afqt99n -0.597199 0.061
## 199 itemModel.item[1,100] item afqt afqt100n 0.575464 0.069
## 200 itemModel.item[2,100] item p2 afqt100n -0.519842 0.062
##
## Model Statistics:
## | Parameters | Degrees of Freedom | Fit (-2lnL units)
## Model: 200 NA 99982
## Saturated: NA 0 18642
## Independence: NA NA 109349
## Number of observations/statistics: 1300/NA
##
## chi-square: χ² ( df=NA ) = 81339, p = NA
## Information Criteria:
## | df Penalty | Parameters Penalty | Sample-Size Adjusted
## AIC: NA 100382 100455
## BIC: NA 101416 100780
## CFI: NA
## TLI: 1 (also known as NNFI)
## RMSEA: 0 [95% CI (NA, NA)]
## Prob(RMSEA <= 0.05): NA
## To get additional fit indices, see help(mxRefModels)
## timestamp: 2019-03-08 11:05:03
## Wall clock time: 2.6 secs
## OpenMx version number: 2.12.1
## Need help? See help(mxSummary)citation('OpenMx')To cite package ‘OpenMx’ in publications use:
Michael C. Neale, Michael D. Hunter, Joshua N. Pritikin, Mahsa Zahery, Timothy R. Brick Robert M. Kirkpatrick, Ryne Estabrook, Timothy C. Bates, Hermine H. Maes, Steven M. Boker. (2016). OpenMx 2.0: Extended structural equation and statistical modeling. Psychometrika, 81(2), 535-549. doi:10.1007/s11336-014-9435-8
Pritikin, J. N., Hunter, M. D., & Boker, S. M. (2015). Modular open-source software for Item Factor Analysis. Educational and Psychological Measurement, 75(3), 458-474
Hunter, M. D. (in press). State space modeling in an open source, modular, structural equation modeling environment. Structural Equation Modeling, 1-18. doi: 10.1080/10705511.2017.1369354
Steven M. Boker [aut], Michael C. Neale [aut], Hermine H. Maes [aut], Michael J. Wilde [ctb], Michael Spiegel [aut], Timothy R. Brick [aut], Ryne Estabrook [aut], Timothy C. Bates [aut], Paras Mehta [ctb], Timo von Oertzen [ctb], Ross J. Gore [aut], Michael D. Hunter [aut], Daniel C. Hackett [ctb], Julian Karch [ctb], Andreas M. Brandmaier [ctb], Joshua N. Pritikin jpritikin@pobox.com [aut, cre], Mahsa Zahery [aut], Robert M. Kirkpatrick [aut], Yang Wang [ctb], Charles Driver driver@mpib-berlin.mpg.de [ctb], Massachusetts Institute of Technology [cph], S. G. Johnson [cph], Association for Computing Machinery [cph], Dieter Kraft [cph], Stefan Wilhelm [cph], Sarah Medland [cph], Carl F. Falk [cph], Matt Keller [cph], Manjunath B G [cph], The Regents of the University of California [cph], Lester Ingber [cph] and Wong Shao Voon [cph]. (2019) OpenMx 2.12.1 User Guide.
BibTeX entries for LaTeX users are obtained by:
toBibtex(citation(‘OpenMx’))
OpenMx was developed with support from NIH/NIDA grants:
R37-DA018673, R25-DA026119, R21-DA024304 To see these entries in BibTeX format, use ‘print(