Based on the two-factor model and joint ordinal-continuous model examples in the documentation, I tried creating two single-factor models (one continuous and one ordinal), which I tried to combine in a new model. In the new model essentially just tried to add a covariance path between the latent variables. This would useful when I want to expand existing models, without completely respecifying them, however I seem unable to combine them appropriately. First I got the prompt to add a `joinKey`

, so I created a column with a unique entry for each row. Now I get the error:

*"Error in mxPath(from = c("oneFactorContinuousModel.F1", "oneFactorOrdinalModel.F2"), : between level join mappings can only use single headed arrows"*

Seemingly, OpenMx thinks I am trying to create a multilevel model, which I, in this case, am not. Is there a way I can add existing models in a new model, and expand upon them?

Here's my code example:

require(OpenMx) require(dplyr) oneFactorJoint <- myFADataRaw[,c("x1","x2","x3","z1","z2","z3")] %>% mutate(key = 1:nrow(.)) 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)) dataRaw <- mxData( observed=oneFactorJoint, type="raw", primaryKey = "key" ) # Continuous factor model # residual variances resVarsX <- mxPath( from=c("x1","x2","x3"), arrows=2, free=c(TRUE, TRUE, TRUE), values=1, labels=c("e1","e2","e3") ) # latent variance latVarF1 <- mxPath( from=c("F1"), arrows=2, connect="unique.pairs", free=TRUE, values=1, labels = c("varF1") ) # factor loadings facLoadsX <- mxPath( from="F1", to=c("x1","x2","x3"), arrows=1, free=c(FALSE, TRUE, TRUE), values=1, labels=c("l1","l2","l3") ) # means meansX <- mxPath( from="one", to=c("x1","x2","x3","F1"), arrows=1, free=c(TRUE,TRUE,TRUE,FALSE), values=0, labels=c("meanx1","meanx2","meanx3","meanF1") ) oneFactorContinuousModel <- mxModel("Continuous Factor Model Path Specification", type="RAM", manifestVars=c("x1","x2","x3"), latentVars=c("F1"), dataRaw, resVarsX, latVarF1, facLoadsX, meansX) oneFactorContinuousResults <- mxRun(oneFactorContinuousModel) # Ordinal factor model # residual variances resVarsZ <- mxPath( from=c("z1","z2","z3"), arrows=2, free=c(FALSE, FALSE, FALSE), values=1, labels=c("e4","e5","e6") ) # latent variance latVarZ <- mxPath( from=c("F2"), arrows=2, connect="unique.pairs", free=TRUE, values=1, labels = c("varF2") ) # factor loadings facLoadsZ <- mxPath( from="F2", to=c("z1","z2","z3"), arrows=1, free=c(FALSE, TRUE, TRUE), values=1, labels=c("l4","l5","l6") ) # means meansZ <- mxPath( from="one", to=c("z1","z2","z3","F2"), arrows=1, free=c(FALSE,FALSE,FALSE,FALSE), values=0, labels=c("meanz1","meanz2","meanz3","meanF2") ) # thresholds thresholdsZ <- mxThreshold(vars=c("z1","z2","z3"), nThresh=c(1,1,2), free=TRUE, values=c(-1,0,-.5,1.2) ) oneFactorOrdinalModel <- mxModel("Ordinal Factor Model Path Specification", type="RAM", manifestVars=c("z1","z2","z3"), latentVars=c("F2"), dataRaw, resVarsZ, latVarZ, facLoadsZ, meansZ, thresholdsZ) oneFactorOrdinalResults <- mxTryHardOrdinal(oneFactorOrdinalModel) %>% suppressMessages() # [THIS IS WHERE I AM STUCK:] # Combine the two single-factor models combined into a two-factor model # latent covariance latCovar <- mxPath( from=c("oneFactorContinuousModel.F1", "oneFactorOrdinalModel.F2"), arrows=2, connect="unique.bivariate", free=TRUE, values=1, labels = c("cov"), joinKey = "key" ) twoFactorJointModel1 <- mxModel("Two-Factor Model Path Specification", type="RAM", oneFactorContinuousModel, oneFactorOrdinalModel, dataRaw, latCovar)