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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_ModelRec.R
# Author: Michael Hunter
# Date: 2011.07.22
#
# 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
# Recursive
#
# RevisionHistory:
# Michael Hunter -- 2011.07.22 Took template from Ryne Estabrook's LatentGrowthModel_PathRaw.R
#
# -----------------------------------------------------------------------------
require(OpenMx)
# Load Library
# -----------------------------------------------------------------------------
data(myLongitudinalData)
# Prepare Data
# -----------------------------------------------------------------------------
growthCurveModel <- mxModel(
name="Linear Growth Curve Model Path Specification",
type="RAM",
mxData(
observed=myLongitudinalData,
type="raw"
),
manifestVars=c("x1","x2","x3","x4","x5"),
latentVars=c("intercept","slope")
)
growthCurveModel <- mxModel(
model = growthCurveModel,
# residual variances
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")
)
)
growthCurveModel <- mxModel(
model = growthCurveModel,
# latent variances and covariance
mxPath(
from=c("intercept","slope"),
arrows=2,
connect="unique.pairs",
free=TRUE,
values=c(1, 1, 1),
labels=c("vari", "cov", "vars")
)
)
growthCurveModel <- mxModel(
model = growthCurveModel,
# intercept loadings
mxPath(
from="intercept",
to=c("x1","x2","x3","x4","x5"),
arrows=1,
free=FALSE,
values=c(1, 1, 1, 1, 1)
)
)
growthCurveModel <- mxModel(
model= growthCurveModel,
# slope loadings
mxPath(
from="slope",
to=c("x1","x2","x3","x4","x5"),
arrows=1,
free=FALSE,
values=c(0, 1, 2, 3, 4)
)
)
growthCurveModel <- mxModel(
model = growthCurveModel,
# manifest means
mxPath(from="one",
to=c("x1", "x2", "x3", "x4", "x5"),
arrows=1,
free=FALSE,
values=c(0, 0, 0, 0, 0)
)
)
growthCurveModel <- mxModel(
model = growthCurveModel,
# latent means
mxPath(from="one",
to=c("intercept", "slope"),
arrows=1,
free=TRUE,
values=c(1, 1),
labels=c("meani", "means")
)
)
# -----------------------------------------------------------------------------
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
# -----------------------------------------------------------------------------
|
