demo/BivariateSaturated_PathCov.R

In OpenMx: Extended Structural Equation Modelling

#
#   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.
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# 
#        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,
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# -----------------------------------------------------------------------------
# Program: BivariateSaturated_PathCov.R  
# Author: Hermine Maes
# Date: 2009.08.01 
#
# ModelType: Saturated
# DataType: Continuous
# Field: None
#
# Purpose:
#      Bivariate Saturated model to estimate means and (co)variances
#      Path 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)
cov(testData)
# Simulate Data
# -----------------------------------------------------------------------------

bivSatModel1 <- mxModel("bivSat1",
    manifestVars= selVars,
    mxPath(
        from=c("X", "Y"), 
        arrows=2, 
        free = TRUE, 
        values=1, 
        lbound=.01, 
        labels=c("varX","varY")
    ),
    mxPath(
        from="X", 
        to="Y", 
        arrows=2, 
        free = TRUE, 
        values=.2, 
        lbound=.01, 
        labels="covXY"
    ),
    mxData(
        observed=cov(testData), 
        type="cov", 
        numObs=1000 
    ),
    type="RAM"
)

bivSatFit1 <- mxRun(bivSatModel1)
EC1 <- mxEval(S, bivSatFit1)
LL1 <- mxEval(objective, bivSatFit1)
SL1 <- summary(bivSatFit1)$SaturatedLikelihood
Chi1 <- LL1-SL1
# example 1: Saturated Model with Cov Matrices and Path-Style Input
# -----------------------------------------------------------------------------

bivSatModel1m <- mxModel("bivSat1m",
    manifestVars= selVars,
    mxPath(
        from=c("X", "Y"), 
        arrows=2, 
        free = TRUE, 
        values=1, 
        lbound=.01, 
        labels=c("varX","varY")
    ),
    mxPath(
        from="X", 
        to="Y", 
        arrows=2, 
        free = TRUE, 
        values=.2, 
        lbound=.01, 
        labels="covXY"
    ),
    mxPath(
        from="one", 
        to=c("X", "Y"), 
        arrows=1, 
        free = TRUE, 
        values=.01, 
        labels=c("meanX","meanY")
    ),
    mxData(
        observed=cov(testData), 
        type="cov", 
        numObs=1000, 
        means=colMeans(testData)
    ),
    type="RAM"
)

bivSatFit1m <- mxRun(bivSatModel1m)
EM1m <- mxEval(M, bivSatFit1m)
EC1m <- mxEval(S, bivSatFit1m)
LL1m <- mxEval(objective, bivSatFit1m)
SL1m <- summary(bivSatFit1m)$SaturatedLikelihood
Chi1m <- LL1m-SL1m
# example 1m: Saturated Model with Cov Matrices & Means and Path-Style Input
# -----------------------------------------------------------------------------

omxCheckCloseEnough(Chi1, -0.001, .001)
omxCheckCloseEnough(c(EC1),c(1.065, 0.475, 0.475, 0.929),.001)
# 1:CovPat
# -------------------------------------

omxCheckCloseEnough(Chi1m, -0.001,.001)
omxCheckCloseEnough(c(EC1m),c(1.065, 0.475, 0.475, 0.929),.001)
omxCheckCloseEnough(c(EM1m),c(0.058, 0.006),.001)
# 1m:CovMPat 
# -------------------------------------
# Compare OpenMx results to Mx results 
# (LL: likelihood; EC: expected covariance, EM: expected means)
# -----------------------------------------------------------------------------