# # 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_PathCov.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 # Path style model input - Covariance matrix data input # # RevisionHistory: # Hermine Maes -- 2009.10.08 updated & reformatted # Ross Gore -- 2011.06.15 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")) ) myRegDataMeans <- c(2.582, 0.054, 2.574, 4.061) names(myRegDataMeans) <- c("w","x","y","z") # Prepare Data # ----------------------------------------------------------------------------- multivariateRegModel <- mxModel("MultiVariate Regression Path Specification", type="RAM", mxData( observed=myRegDataCov, type="cov", numObs=100, means=myRegDataMeans ), manifestVars=c("w", "x", "y", "z"), mxPath( from=c("w", "x", "y", "z"), arrows=2, free=TRUE, values = c(1, 1, 1, 1), labels=c("residualw", "varx", "residualy", "varz") ), # variance paths # ------------------------------------- mxPath( from="x", to="z", arrows=2, free=TRUE, values=0.5, labels="covxz" ), # covariance of x and z # ------------------------------------- mxPath( from=c("x","z"), to="y", arrows=1, free=TRUE, values=1, labels=c("betayx","betayz") ), # regression weights for y # ------------------------------------- mxPath( from=c("x","z"), to="w", arrows=1, free=TRUE, values=1, labels=c("betawx","betawz") ), # regression weights for w # ------------------------------------- mxPath( from="one", to=c("w", "x", "y", "z"), arrows=1, free=TRUE, values=c(1, 1), labels=c("betaw", "meanx", "betay", "meanz") ) # means and intercepts # ------------------------------------- ) # close model # Create an MxModel object # ----------------------------------------------------------------------------- multivariateRegFit <- mxRun(multivariateRegModel) summary(multivariateRegFit) multivariateRegFit$output omxCheckCloseEnough(multivariateRegFit$output$estimate[["betay"]], 1.6312, 0.001) omxCheckCloseEnough(multivariateRegFit$output$estimate[["betayx"]], 0.4243, 0.001) omxCheckCloseEnough(multivariateRegFit$output$estimate[["betayz"]], 0.2265, 0.001) omxCheckCloseEnough(multivariateRegFit$output$estimate[["residualy"]], 0.6336, 0.001) omxCheckCloseEnough(multivariateRegFit$output$estimate[["betaw"]], 0.5165, 0.001) omxCheckCloseEnough(multivariateRegFit$output$estimate[["betawx"]], -0.2311, 0.001) omxCheckCloseEnough(multivariateRegFit$output$estimate[["betawz"]], 0.5117, 0.001) omxCheckCloseEnough(multivariateRegFit$output$estimate[["residualw"]], 0.5979, 0.001) omxCheckCloseEnough(multivariateRegFit$output$estimate[["varx"]], 1.1160, 0.001) omxCheckCloseEnough(multivariateRegFit$output$estimate[["varz"]], 0.8360, 0.001) omxCheckCloseEnough(multivariateRegFit$output$estimate[["covxz"]], 0.2890, 0.001) omxCheckCloseEnough(multivariateRegFit$output$estimate[["meanx"]], 0.0540, 0.001) omxCheckCloseEnough(multivariateRegFit$output$estimate[["meanz"]], 4.0610, 0.001) # Compare OpenMx results to Mx results # -----------------------------------------------------------------------------