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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.
# ---------------------------------------------------------------------
# Program: OneFactorCov-OpenMx100221.R
# Author: Steven M. Boker
# Date: Sun Feb 21 13:39:29 EST 2010
#
# This program fits a covariance single factor model to the
# factorExample1.csv simulated data.
#
#
# ---------------------------------------------------------------------
# Revision History
# -- Sun Feb 21 13:39:32 EST 2010
# Created OneFactorCov-OpenMx100221.R.
#
# ---------------------------------------------------------------------
# ----------------------------------
# Read libraries and set options.
require(OpenMx)
library(testthat)
# ----------------------------------
# Read the data and print descriptive statistics.
data(factorExample1)
# ----------------------------------
# Build an OpenMx single factor covariance model with fixed variance
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)
)
l1 <- omxLocateParameters(oneFactorCov1, "Var_F1", free=FALSE)
expect_equal(nrow(l1), 1)
expect_equal(l1$matrix, "S")
expect_equal(l1$row, 10)
expect_equal(l1$col, 10)
l2 <- omxLocateParameters(oneFactorCov1, "Var_F1", free=NA)
expect_equal(l1, l2)
expect_equal(nrow(omxLocateParameters(oneFactorCov1, "Var_F1")), 0)
oneFactorCov1Out <- mxRun(oneFactorCov1, suppressWarnings=TRUE)
summary(oneFactorCov1Out)
omxCheckError(mxBootstrap(oneFactorCov1Out),
"MxComputeBootstrap: data 'Single Factor Covariance Model with Fixed Variance.data' of type 'cov' cannot have row weights")
# ----------------------------------
# check for correct values
expectVal <- c(0.68396, 0.32482, 0.10887, 0.47441, 0.60181, 1.12064,
1.25934, 0.64739, 0.71873, 0.3528, 0.17619, 0.19354, 0.79988, 0.63306,
0.36763, 0.34024, 0.23404, 0.85441)
expectSE <- c(0.035244, 0.022431, 0.020808, 0.044662, 0.042295, 0.045789,
0.048931, 0.03064, 0.049368, 0.02492, 0.01197, 0.01234, 0.052165,
0.042853, 0.032175, 0.034939, 0.017353, 0.057948)
# cat(deparse(round(oneFactorCov1Out$output$estimate, 5)))
omxCheckCloseEnough(expectVal, oneFactorCov1Out$output$estimate, 0.001)
omxCheckCloseEnough(expectSE,
as.vector(oneFactorCov1Out$output[['standardErrors']]), 0.001)
omxCheckCloseEnough(1435.94, oneFactorCov1Out$output$minimum, 0.001)
# ----------------------------------
# Build an OpenMx single factor covariance model with fixed loading
oneFactorCov2 <- mxModel("Single Factor Covariance Model with Fixed Loading",
type="RAM",
manifestVars=indicators,
latentVars=latents,
mxPath(from=latents, to=indicators,
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=TRUE, values=1,
labels=factorVarLabels),
mxPath(from=latents, to=c("x1"),
arrows=1,
free=FALSE, values=1),
mxData(observed=cov(factorExample1), type="cov", numObs=500)
)
oneFactorCov2Out <- mxRun(oneFactorCov2, suppressWarnings=TRUE)
summary(oneFactorCov2Out)
# ----------------------------------
# check for correct values
expectVal <- c(0.47491, 0.15917, 0.69363, 0.87989, 1.63847, 1.84125,
0.94654, 1.05084, 0.3528, 0.17619, 0.19354, 0.79988, 0.63306, 0.36763,
0.34024, 0.23404, 0.85441, 0.4678)
expectSE <- c(0.035182, 0.030686, 0.067917, 0.066228, 0.078835, 0.085033,
0.051178, 0.077348, 0.02492, 0.01197, 0.01234, 0.052166, 0.042854,
0.032175, 0.034939, 0.017353, 0.057948, 0.048261)
# cat(deparse(round(oneFactorCov2Out$output$estimate, 5)))
omxCheckCloseEnough(expectVal, oneFactorCov2Out$output$estimate, 0.001)
omxCheckCloseEnough(expectSE,
as.vector(oneFactorCov2Out$output[['standardErrors']]), 0.001)
omxCheckCloseEnough(1435.9409, oneFactorCov2Out$output$minimum, 0.001)
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