# # Copyright 2007-2012 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: OneFactorJoint_PathRaw.R # Author: Ryne Estabrook # Date: 2014.05.09 # # ModelType: Factor # DataType: Ordinal # Field: None # # Purpose: # One Factor model to estimate factor loadings, residual variances, means and thresholds # Path style model input - Raw data input # # RevisionHistory: # Hermine Maes -- 2014.11.02 piecewise specification # ----------------------------------------------------------------------------- require(OpenMx) # Load Library # ----------------------------------------------------------------------------- data(myFADataRaw) oneFactorJoint <- myFADataRaw[,c("x1","x2","x3","z1","z2","z3")] oneFactorJoint$z1 <- mxFactor(oneFactorJoint$z1, levels=c(0, 1)) oneFactorJoint$z2 <- mxFactor(oneFactorJoint$z2, levels=c(0, 1)) oneFactorJoint$z3 <- mxFactor(oneFactorJoint$z3, levels=c(0, 1, 2)) # Prepare Data # ----------------------------------------------------------------------------- dataRaw <- mxData( observed=oneFactorJoint, type="raw" ) # residual variances resVars <- mxPath( from=c("x1","x2","x3","z1","z2","z3"), arrows=2, free=c(TRUE, TRUE, TRUE, FALSE, FALSE, FALSE), values=1, labels=c("e1","e2","e3","e4","e5","e6") ) # latent variance latVar <- mxPath( from="F1", arrows=2, free=FALSE, values=1, labels ="varF1" ) # factor loadings facLoads <- mxPath( from="F1", to=c("x1","x2","x3","z1","z2","z3"), arrows=1, free=TRUE, values=1, labels=c("l1","l2","l3","l4","l5","l6") ) # means means <- mxPath( from="one", to=c("x1","x2","x3","z1","z2","z3","F1"), arrows=1, free=c(TRUE,TRUE,TRUE,FALSE,FALSE,FALSE,FALSE), values=0, labels=c("meanx1","meanx2","meanx3","meanz1","meanz2","meanz3","meanF") ) # thresholds thresholds <- mxThreshold(vars=c("z1","z2","z3"), nThresh=c(1,1,2), free=TRUE, values=c(-1, 0, -.5, 1.2), labels=c("var1_th1","var2_th1","var3_th1","var3_th2") ) oneFactorJointModel <- mxModel("Common Factor Model Path Specification", type="RAM", manifestVars=c("x1","x2","x3","z1","z2","z3"), latentVars="F1", dataRaw, resVars, latVar, means, thresholds) # Create an MxModel object # ----------------------------------------------------------------------------- oneFactorJointFit <- mxRun(oneFactorJointModel) # Fit the model with mxRun # ----------------------------------------------------------------------------- summary(oneFactorJointFit) oneFactorJointFit$output$estimate # Print a summary of the results # -----------------------------------------------------------------------------