# # 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: MultivariateRegression_MatrixRaw.R # Author: Ryne Estabrook # Date: 2009.08.01 # # ModelType: Regression # DataType: Continuous # Field: None # # Purpose: # Multivariate Regression model to estimate effect of # independent on dependent variables # 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 Library # ----------------------------------------------------------------------------- data(myRegDataRaw) # Prepare Data # ----------------------------------------------------------------------------- dataRaw <- mxData( observed=myRegDataRaw, type="raw" ) matrA <- mxMatrix( type="Full", nrow=4, ncol=4, free= c(F,T,F,T, F,F,F,F, F,T,F,T, F,F,F,F), values=c(0,1,0,1, 0,0,0,0, 0,1,0,1, 0,0,0,0), labels=c(NA,"betawx",NA,"betawz", NA, NA, NA, NA, NA,"betayx",NA,"betayz", NA, NA, NA, NA), byrow=TRUE, name="A" ) matrS <- mxMatrix( type="Symm", nrow=4, ncol=4, free=c(T,F,F,F, F,T,F,T, F,F,T,F, F,T,F,T), values=c(1, 0,0, 0, 0, 1,0,.5, 0, 0,1, 0, 0,.5,0, 1), labels=c("residualw", NA, NA, NA, NA, "varx", NA, "covxz", NA, NA, "residualy", NA, NA, "covxz", NA, "varz"), byrow=TRUE, name="S" ) matrF <- mxMatrix( type="Iden", nrow=4, ncol=4, name="F" ) matrM <- mxMatrix( type="Full", nrow=1, ncol=4, free=c(T,T,T,T), values=c(0,0,0,0), labels=c("betaw","meanx","betay","meanz"), name="M" ) exp <- mxExpectationRAM("A","S","F","M", dimnames=c("w","x","y","z") ) funML <- mxFitFunctionML() multivariateRegModel <- mxModel("Multiple Regression Matrix Specification", dataRaw, matrA, matrS, matrF, matrM, exp, funML) # Create an MxModel object # ----------------------------------------------------------------------------- multivariateRegFit<-mxRun(multivariateRegModel) summary(multivariateRegFit) multivariateRegFit\$output omxCheckCloseEnough(multivariateRegFit\$output\$estimate[["betay"]], 1.6332, 0.001) omxCheckCloseEnough(multivariateRegFit\$output\$estimate[["betayx"]], 0.4246, 0.001) omxCheckCloseEnough(multivariateRegFit\$output\$estimate[["betayz"]], 0.2260, 0.001) omxCheckCloseEnough(multivariateRegFit\$output\$estimate[["residualy"]], 0.6267, 0.001) omxCheckCloseEnough(multivariateRegFit\$output\$estimate[["betaw"]], 0.5139, 0.001) omxCheckCloseEnough(multivariateRegFit\$output\$estimate[["betawx"]], -0.2310, 0.001) omxCheckCloseEnough(multivariateRegFit\$output\$estimate[["betawz"]], 0.5122, 0.001) omxCheckCloseEnough(multivariateRegFit\$output\$estimate[["residualw"]], 0.5914, 0.001) omxCheckCloseEnough(multivariateRegFit\$output\$estimate[["varx"]], 1.1053, 0.001) omxCheckCloseEnough(multivariateRegFit\$output\$estimate[["varz"]], 0.8275, 0.001) omxCheckCloseEnough(multivariateRegFit\$output\$estimate[["covxz"]], 0.2862, 0.001) omxCheckCloseEnough(multivariateRegFit\$output\$estimate[["meanx"]], 0.0542, 0.001) omxCheckCloseEnough(multivariateRegFit\$output\$estimate[["meanz"]], 4.0611, 0.001) # Compare OpenMx results to Mx results # -----------------------------------------------------------------------------