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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: LatentGrowthModel_PathRaw.R
# Author: Ryne Estabrook
# Date: 2010.09.17
#
# ModelType: Growth Mixture
# DataType: Continuous
# Field: None
#
# Purpose:
# Growth Mixture Model
# Path style model input - Raw data input
#
# RevisionHistory:
# Ross Gore -- 2011.06.16 added Model, Data & Field metadata
# Hermine Maes -- 2014.11.02 piecewise specification
# -----------------------------------------------------------------------------
require(OpenMx)
# Load Libraries
# -----------------------------------------------------------------------------
data(myGrowthMixtureData)
# Prepare Data
# -----------------------------------------------------------------------------
# residual variances
resVars <- mxPath( from=c("x1","x2","x3","x4","x5"), arrows=2,
free=TRUE, values = c(1,1,1,1,1),
labels=c("residual","residual","residual","residual","residual") )
# latent variances and covariance
latVars <- mxPath( from=c("intercept","slope"), arrows=2, connect="unique.pairs",
free=TRUE, values=c(1,.4,1), labels=c("vari1","cov1","vars1") )
# intercept loadings
intLoads <- mxPath( from="intercept", to=c("x1","x2","x3","x4","x5"), arrows=1,
free=FALSE, values=c(1,1,1,1,1) )
# slope loadings
sloLoads <- mxPath( from="slope", to=c("x1","x2","x3","x4","x5"), arrows=1,
free=FALSE, values=c(0,1,2,3,4) )
# manifest means
manMeans <- mxPath( from="one", to=c("x1","x2", "x3", "x4","x5"), arrows=1,
free=FALSE, values=c(0,0,0,0,0) )
# latent means
latMeans <- mxPath( from="one", to=c("intercept","slope"), arrows=1,
free=TRUE, values=c(0,-1), labels=c("meani1","means1") )
# enable the likelihood vector
funML <- mxFitFunctionML(vector=TRUE)
class1 <- mxModel("Class1", type="RAM",
manifestVars=c("x1","x2","x3","x4","x5"),
latentVars=c("intercept","slope"),
resVars, latVars, intLoads, sloLoads, manMeans, latMeans,
funML)
# latent variances and covariance
latVars2 <- mxPath( from=c("intercept","slope"), arrows=2, connect="unique.pairs",
free=TRUE, values=c(1,.5,1), labels=c("vari2","cov2","vars2") )
# latent means
latMeans2 <- mxPath( from="one", to=c("intercept", "slope"), arrows=1,
free=TRUE, values=c(5,1), labels=c("meani2","means2") )
class2 <- mxModel(class1, name="Class2", latVars2, latMeans2)
# Create an MxModel object
# -----------------------------------------------------------------------------
# request that individual likelihoods are used
# required for correct parameterization of class probabilities
classP <- mxMatrix( type="Full", nrow=2, ncol=1,
free=c(TRUE, FALSE), values=1, lbound=0.001,
labels = c("p1","p2"), name="Props" )
mixExp <- mxExpectationMixture(components=c('Class1', 'Class2'), weights='Props', scale='sum')
fit <- mxFitFunctionML()
dataRaw <- mxData( observed=myGrowthMixtureData, type="raw" )
gmm <- mxModel("Growth Mixture Model",
dataRaw, class1, class2, classP, mixExp, fit)
gmmFit <- mxRun(gmm, suppressWarnings=TRUE)
summary(gmmFit)
# Unscaled mixture proportions
mxEval(Props, gmmFit)
# Scaled mixture proportions
gmmFit$expectation$output$weights
omxCheckCloseEnough(-2*logLik(gmmFit), 8739.05, 0.01)
omxCheckCloseEnough(max(gmmFit$expectation$output$weights), 0.6009, 0.01)
omxCheckCloseEnough(min(gmmFit$expectation$output$weights), 0.3991, 0.01)
# Check to see if results match within the specified bounds
# -----------------------------------------------------------------------------
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