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#
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# -----------------------------------------------------------------------
# Program: BivariateSaturated_MatrixCovCholesky.R  
# Author: Hermine Maes
# Date: 2009.08.01 
#
# ModelType: Saturated
# DataType: Continuous
# Field: None
#
# Purpose: 
#      Bivariate Saturated model to estimate means and (co)variances 
#      using Cholesky Decomposition
#      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
# -----------------------------------------------------------------------

#options(error = utils::recover)
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
# -----------------------------------------------------------------------------


bivSatModel5 <- mxModel("bivSat5",
    mxMatrix(
        type="Lower", 
        nrow=2, 
        ncol=2, 
        free=T, 
        values=.5, 
        name="Chol"
    ),
    mxAlgebra(
        expression=Chol %*% t(Chol), 
        name="expCov" 
    ),
    mxData(
        observed=cov(testData), 
        type="cov", 
        numObs=1000 
    ),
    mxFitFunctionML(),mxExpectationNormal(
        covariance="expCov",
        dimnames=selVars
    )
)

bivSatFit5 <- mxRun(bivSatModel5)
EC5 <- mxEval(expCov, bivSatFit5)
LL5 <- mxEval(objective,bivSatFit5)
SL5 <- summary(bivSatFit5)$SaturatedLikelihood
Chi5 <- LL5-SL5
# example 5: Saturated Model with Cov Matrices and Matrix-Style Input
# -----------------------------------------------------------------------------


bivSatModel5m <- mxModel("bivSat5m",
    mxMatrix(
        type="Lower", 
        nrow=2, 
        ncol=2, 
        free=T, 
        values=.5, 
        name="Chol"
    ),
    mxAlgebra(
        expression=Chol %*% t(Chol), 
        name="expCov" 
    ),
    mxMatrix(
        type="Full", 
        nrow=1, 
        ncol=2, 
        free=T, 
        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
    )
)

bivSatFit5m <- mxRun(bivSatModel5m)
EM5m <- mxEval(expMean, bivSatFit5m)
EC5m <- mxEval(expCov, bivSatFit5m)
LL5m <- mxEval(objective,bivSatFit5m);
SL5m <- summary(bivSatFit5m)$SaturatedLikelihood
Chi5m <- LL5m-SL5m
# example 5m: Saturated Model with Cov Matrices & Means and Matrix-Style Input
# -----------------------------------------------------------------------------


omxCheckCloseEnough(Chi5,-0.001,.001)
omxCheckCloseEnough(c(EC5),c(1.065, 0.475, 0.475, 0.929),.001)
# 5:CovMat Cholesky
# -------------------------------------

omxCheckCloseEnough(Chi5m,-0.001,.001)
omxCheckCloseEnough(c(EC5m),c(1.065, 0.475, 0.475, 0.929),.001)
omxCheckCloseEnough(c(EM5m),c(0.058, 0.006),.001)
# 5m:CovMPat Cholesky
# -------------------------------------
# Compare OpenMx results to Mx results 
# (LL: likelihood; EC: expected covariance, EM: expected means)
# -----------------------------------------------------------------------------
