Nothing
#
# 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: BivariateSaturated_MatrixRaw.R
# Author: Hermine Maes
# Date: 2009.08.01
#
# ModelType: Saturated
# DataType: Continuous
# Field: None
#
# Purpose:
# Bivariate Saturated model to estimate means and (co)variances
# Matrix style model input - Raw data input
#
# RevisionHistory:
# Hermine Maes -- 2009.10.08 updated & reformatted
# Ross Gore -- 2011.06.15 added Model, Data & Field metadata
# -----------------------------------------------------------------------------
require(OpenMx)
require(MASS)
# Load Libraries
# -----------------------------------------------------------------------------
set.seed(200)
rs=.5
xy <- mvtnorm::rmvnorm (1000, c(0,0), matrix(c(1,rs,rs,1),2,2))
testData <- xy
testData <- testData[, order(apply(testData, 2, var))[2:1]] #put the data columns in order from largest to smallest variance
# Note: Users do NOT have to re-order their data columns. This is only to make data generation the same on different operating systems: to fix an inconsistency with the mvtnorm::rmvnorm function in the MASS package.
selVars <- c("X","Y")
dimnames(testData) <- list(NULL, selVars)
summary(testData)
cov(testData)
# Simulate Data
# -----------------------------------------------------------------------------
bivSatModel4 <- mxModel("bivSat4",
mxMatrix(
type="Symm",
nrow=2,
ncol=2,
free = TRUE,
values=c(1,.5,1),
name="expCov"
),
mxMatrix(
type="Full",
nrow=1,
ncol=2,
free = TRUE,
values=c(0,0),
name="expMean"
),
mxData(
observed=testData,
type="raw",
),
mxFitFunctionML(),mxExpectationNormal(
covariance="expCov",
means="expMean",
dimnames=selVars
)
)
bivSatFit4 <- mxRun(bivSatModel4)
bivSatSummary4 <- summary(bivSatFit4)
EM4 <- mxEval(expMean, bivSatFit4)
EC4 <- mxEval(expCov, bivSatFit4)
LL4 <- mxEval(objective,bivSatFit4)
omxCheckEquals(bivSatSummary4$observedStatistics, 5)
# examples 4: Saturated Model with Raw Data and Matrix-Style Input
# -----------------------------------------------------------------------------
omxCheckCloseEnough(LL4,5407.037,.001)
omxCheckCloseEnough(c(EC4),c(1.066, 0.475, 0.475, 0.929),.001)
omxCheckCloseEnough(c(EM4),c(0.058, 0.006),.001)
# 4:RawMat
# -------------------------------------
# Compare OpenMx results to Mx results
# (LL: likelihood; EC: expected covariance, EM: expected means)
# -----------------------------------------------------------------------------
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