Factor scores following WLS estimation

#require(devtools)
#install_bitbucket('mhunter/easymx')
# need a bleeding edge version of EasyMx
# to get the deviation=FALSE argument
# However, if your ordinal variables all have the
# same number of categories this isn't needed.
# Then you can use
# install.packages('EasyMx')

require(EasyMx)

# Load data
data(jointdata)
jointdata[, c(2, 4, 5)] <- mxFactor(jointdata[,c(2, 4, 5)],
levels=sapply(jointdata[,c(2, 4, 5)], function(x){sort(unique(x))}))

# Build a basic factor model
m0 <- emxFactorModel(list(F1=names(jointdata)), jointdata)
m0$Loadings$lbound <- 0

# Make this a WLS factor model
m1 <- mxModel("WLSFactorModel",
emxThresholds(jointdata, ordinal=c(2, 4, 5), deviation=FALSE),
m0$Loadings, m0$Residuals, m0$LatentMeans, m0$Means, m0$LatentVariances,
m0$expectation,
mxDataWLS(jointdata),
mxFitFunctionWLS())

# Run it
m1r <- mxRun(m1)

# Try factor scores. Error.
mxFactorScores(m1r, 'ML')
# Error in mxFactorScores(m1r, "ML") :
# The 'model' argument must have raw (not summary) data.

# Swap in raw data for WLS, and ML fit function for WLS
m1rd <- mxModel(m1r,
mxData(jointdata, 'raw'),
mxFitFunctionML())