#
#   Copyright 2007-2014 The OpenMx Project
#
#   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 
# -----------------------------------------------------------------------

require(OpenMx)
# Load Libraries
# -----------------------------------------------------------------------

data(myLongitudinalData)
# Prepare Data
# -----------------------------------------------------------------------

growthCurveModel <- mxModel("Linear Growth Curve Model Matrix Specification", 
    mxData(
    	observed=myLongitudinalData, 
        type="raw"
    ),
    mxMatrix(
        type="Full",
        nrow=7, 
        ncol=7,
        free=F,
        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"
    ),
    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,  1,0.5,
                 0,0,0,0,0,0.5,  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"
    ),
    mxMatrix(
        type="Full",
        nrow=5,
        ncol=7,
        free=F,
        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=T,
        name="F"
    ),
    mxMatrix(
    	type="Full",
    	nrow=1, 
    	ncol=7,
        values=c(0,0,0,0,0,1,1),
        free=c(F,F,F,F,F,T,T),
        labels=c(NA,NA,NA,NA,NA,"meani","means"),
        name="M"
    ),
    mxFitFunctionML(),mxExpectationRAM("A","S","F","M",
		dimnames = c(names(myLongitudinalData), "intercept", "slope"))
)
# Create an MxModel object
# -----------------------------------------------------------------------
      
growthCurveFit<-mxRun(growthCurveModel, suppressWarnings=TRUE)

summary(growthCurveFit)
growthCurveFit$output$estimate


omxCheckCloseEnough(growthCurveFit$output$estimate[["meani"]], 9.930, 0.01)
omxCheckCloseEnough(growthCurveFit$output$estimate[["means"]], 1.813, 0.01)
omxCheckCloseEnough(growthCurveFit$output$estimate[["vari"]], 3.886, 0.01)
omxCheckCloseEnough(growthCurveFit$output$estimate[["vars"]], 0.258, 0.01)
omxCheckCloseEnough(growthCurveFit$output$estimate[["cov"]], 0.460, 0.01)
omxCheckCloseEnough(growthCurveFit$output$estimate[["residual"]], 2.316, 0.01)
# Compare OpenMx results to Mx results 
# -----------------------------------------------------------------------