#
# 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: 2009.08.01
#
# ModelType: Growth Curve
# DataType: Longitudinal
# Field: None
#
# Purpose:
# Latent Growth model to estimate means and
# (co)variances of slope and intercept
# Path style model input - Raw data input
#
# RevisionHistory:
# Hermine Maes -- 2009.10.08 updated & reformatted
# Ross Gore -- 2011.06.15 added Model, Data & Field
# Hermine Maes -- 2014.11.02 piecewise specification
# -----------------------------------------------------------------------------
require(OpenMx)
# Load Library
# -----------------------------------------------------------------------------
data(myLongitudinalData)
# Prepare Data
# -----------------------------------------------------------------------------
dataRaw <- mxData( observed=myLongitudinalData, type="raw" )
# 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(1,1,1), labels=c("vari","cov","vars") )
# 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(1,1), labels=c("meani","means") )
growthCurveModel <- mxModel("Linear Growth Curve Model Path Specification",
type="RAM",
manifestVars=c("x1","x2","x3","x4","x5"),
latentVars=c("intercept","slope"),
dataRaw, resVars, latVars, intLoads, sloLoads,
manMeans, latMeans)
#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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