Nothing
#
# 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_MatrixRaw.R
# Author: Ryne Estabrook
# Date: 2009.08.01
#
# ModelType: Growth Curve
# DataType: Longitudinal
# Field: None
#
# Purpose:
# Latent Growth model to estimate means and (co)variances of slope and intercept
# 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
# Hermine Maes -- 2014.11.02 piecewise specification
# -----------------------------------------------------------------------
require(OpenMx)
# Load Libraries
# -----------------------------------------------------------------------
data(myLongitudinalData)
# Prepare Data
# -----------------------------------------------------------------------
dataRaw <- mxData( observed=myLongitudinalData, type="raw" )
matrA <- mxMatrix( type="Full", nrow=7, ncol=7,
free = FALSE,
values=c(0,0,0,0,0,1,0,
0,0,0,0,0,1,1,
0,0,0,0,0,1,2,
0,0,0,0,0,1,3,
0,0,0,0,0,1,4,
0,0,0,0,0,0,0,
0,0,0,0,0,0,0),
byrow=TRUE, name="A" )
matrS <- mxMatrix( type="Symm", nrow=7, ncol=7,
free= c(T,F,F,F,F,F,F,
F,T,F,F,F,F,F,
F,F,T,F,F,F,F,
F,F,F,T,F,F,F,
F,F,F,F,T,F,F,
F,F,F,F,F,T,T,
F,F,F,F,F,T,T),
values=c(0,0,0,0,0, 0, 0,
0,0,0,0,0, 0, 0,
0,0,0,0,0, 0, 0,
0,0,0,0,0, 0, 0,
0,0,0,0,0, 0, 0,
0,0,0,0,0, 1,.5,
0,0,0,0,0,.5, 1),
labels=c("residual", NA, NA, NA, NA, NA, NA,
NA, "residual", NA, NA, NA, NA, NA,
NA, NA, "residual", NA, NA, NA, NA,
NA, NA, NA, "residual", NA, NA, NA,
NA, NA, NA, NA, "residual", NA, NA,
NA, NA, NA, NA, NA, "vari", "cov",
NA, NA, NA, NA, NA, "cov", "vars"),
byrow= TRUE, name="S" )
matrF <- mxMatrix( type="Full", nrow=5, ncol=7,
free = FALSE,
values=c(1,0,0,0,0,0,0,
0,1,0,0,0,0,0,
0,0,1,0,0,0,0,
0,0,0,1,0,0,0,
0,0,0,0,1,0,0),
byrow=TRUE, name="F" )
matrM <- mxMatrix( type="Full", nrow=1, ncol=7,
free=c(F,F,F,F,F,T,T), values=c(0,0,0,0,0,1,1),
labels=c(NA,NA,NA,NA,NA,"meani","means"), name="M" )
exp <- mxExpectationRAM("A","S","F","M",
dimnames=c(names(myLongitudinalData),"intercept","slope"))
funML <- mxFitFunctionML()
growthCurveModel <- mxModel("Linear Growth Curve Model Matrix Specification",
dataRaw, matrA, matrS, matrF, matrM, exp, funML)
# Create an MxModel object
# -----------------------------------------------------------------------
growthCurveFit<-mxRun(growthCurveModel, suppressWarnings=TRUE)
summary(growthCurveFit)
coef(growthCurveFit)
omxCheckCloseEnough(coef(growthCurveFit)[["meani"]], 9.930, 0.01)
omxCheckCloseEnough(coef(growthCurveFit)[["means"]], 1.813, 0.01)
omxCheckCloseEnough(coef(growthCurveFit)[["vari"]], 3.886, 0.01)
omxCheckCloseEnough(coef(growthCurveFit)[["vars"]], 0.258, 0.01)
omxCheckCloseEnough(coef(growthCurveFit)[["cov"]], 0.460, 0.01)
omxCheckCloseEnough(coef(growthCurveFit)[["residual"]], 2.316, 0.01)
# Compare OpenMx results to Mx results
# -----------------------------------------------------------------------
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