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#
# 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.
# -----------------------------------------------------------------------------
# Program: BivariateSaturated_MatrixCov.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 - Covariance matrix 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)
covData <- cov(testData)
# Simulate Data
# -----------------------------------------------------------------------------
bivSatModel3 <- mxModel("bivSat3",
mxMatrix(
type="Symm",
nrow=2,
ncol=2,
free = TRUE,
values=c(1,.5,1),
name="expCov"
),
mxData(
observed=covData,
type="cov",
numObs=1000
),
mxFitFunctionML(),mxExpectationNormal(
covariance="expCov",
dimnames=selVars
)
)
bivSatFit3 <- mxRun(bivSatModel3)
EC3 <- mxEval(expCov, bivSatFit3)
LL3 <- mxEval(objective,bivSatFit3)
bivSatSummary3 <- summary(bivSatFit3)
SL3 <- bivSatSummary3$SaturatedLikelihood
omxCheckEquals(bivSatSummary3$observedStatistics, nrow(covData) * (ncol(covData) + 1) / 2)
Chi3 <- LL3-SL3
# example 3: Saturated Model with Cov Matrices and Matrix-Style Input
# -----------------------------------------------------------------------------
bivSatModel3m <- mxModel("bivSat3m",
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=cov(testData),
type="cov",
numObs=1000,
means=colMeans(testData)
),
mxFitFunctionML(),mxExpectationNormal(
covariance="expCov",
means="expMean",
dimnames=selVars
)
)
bivSatFit3m <- mxRun(bivSatModel3m)
EM3m <- mxEval(expMean, bivSatFit3m)
EC3m <- mxEval(expCov, bivSatFit3m)
LL3m <- mxEval(objective,bivSatFit3m)
SL3m <- summary(bivSatFit3m)$SaturatedLikelihood
Chi3m <- LL3m-SL3m
# example 3m: Saturated Model with Cov Matrices & Means and Matrix-Style Input
# -----------------------------------------------------------------------------
omxCheckCloseEnough(Chi3, -0.001, .001)
omxCheckCloseEnough(c(EC3),c(1.065, 0.475, 0.475, 0.929),.001)
# 3:CovMat
# -------------------------------------
omxCheckCloseEnough(Chi3m, -0.001, .001)
omxCheckCloseEnough(c(EC3m),c(1.065, 0.475, 0.475, 0.929),.001)
omxCheckCloseEnough(c(EM3m),c(0.058, 0.006),.001)
# 3m:CovMPat
# -------------------------------------
# Compare OpenMx results to Mx results
# (LL: likelihood; EC: expected covariance, EM: expected means)
# -----------------------------------------------------------------------------
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