# # 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: SimpleRegression_MatrixCov.R # Author: Ryne Estabrook # Date: 2009.08.01 # # ModelType: Regression # DataType: Continuous # Field: None # # Purpose: # Simple Regression model to estimate effect of independent on dependent variables # Matrix style model input - Covariance matrix data input # # RevisionHistory: # Hermine Maes -- 2009.10.08 updated & reformatted # Ross Gore -- 2011.06.06 added Model, Data & Field metadata # ----------------------------------------------------------------------------- require(OpenMx) # Load Library # ----------------------------------------------------------------------------- myRegDataCov <- matrix( c(0.808,-0.110, 0.089, 0.361, -0.110, 1.116, 0.539, 0.289, 0.089, 0.539, 0.933, 0.312, 0.361, 0.289, 0.312, 0.836), nrow=4, dimnames=list( c("w","x","y","z"), c("w","x","y","z")) ) SimpleDataCov <- myRegDataCov[c("x","y"),c("x","y")] myRegDataMeans <- c(2.582, 0.054, 2.574, 4.061) names(myRegDataMeans) <- c("w","x","y","z") SimpleDataMeans <- myRegDataMeans[c(2,3)] # Prepare Data # ----------------------------------------------------------------------------- uniRegModel <- mxModel("Simple Regression Matrix Specification", mxData( observed=SimpleDataCov, type="cov", numObs=100, means=SimpleDataMeans ), mxMatrix( type="Full", nrow=2, ncol=2, free=c(F, F, T, F), values=c(0, 0, 1, 0), labels=c(NA, NA, "beta1", NA), byrow=TRUE, name="A" ), mxMatrix( type="Symm", nrow=2, ncol=2, values=c(1, 0, 0, 1), free=c(T, F, F, T), labels=c("varx", NA, NA, "residual"), byrow=TRUE, name="S" ), mxMatrix( type="Iden", nrow=2, ncol=2, name="F" ), mxMatrix( type="Full", nrow=1, ncol=2, free=c(T, T), values=c(0, 0), labels=c("meanx", "beta0"), name="M"), mxFitFunctionML(),mxExpectationRAM("A", "S", "F", "M", dimnames = c("x", "y")) ) # Create an MxModel object # ----------------------------------------------------------------------------- uniRegFit <- mxRun(uniRegModel) summary(uniRegFit) uniRegFit$output omxCheckCloseEnough(uniRegFit$output$estimate[["beta0"]], 2.54776, 0.001) omxCheckCloseEnough(uniRegFit$output$estimate[["beta1"]], 0.48312, 0.001) omxCheckCloseEnough(uniRegFit$output$estimate[["residual"]], 0.672, 0.01) omxCheckCloseEnough(uniRegFit$output$estimate[["meanx"]], 0.05412, 0.001) omxCheckCloseEnough(uniRegFit$output$estimate[["varx"]], 1.11654, 0.001) # Compare OpenMx results to Mx results # -----------------------------------------------------------------------------