Can OpenMx plot predicted y hat of LGC automatically?

Hi Veronica

Can you clarify? Do you simply want the expected mean for each row? Or do you also want a function of the estimated factor scores, a la fitted.lme?

For the former, the mxGetExpected() will deliver the model expected means, and its defvar row argument could be apply'd to obtain the predicted means when there are definition variables involved that make the expected means differ between data rows.

Thanks
Mike

If you start with something like the demo called LatentGrowthCurveModel_PathRaw which can be run with

demo(package='OpenMx', 'LatentGrowthCurveModel_PathRaw')

Then you only need a few extra lines to get what you want. The real code here is just four lines, but there's some extra inspection and comments.

fs <- mxFactorScores(growthCurveFit, type='regression')
# There's a message about the space in the model name, but everything is fine.
# See ?mxFactorScores for the structure returned
head(fs[,,1]) # these are the factor scores
#  intercept    slope
#1  7.304843 1.102765
#2  9.494752 2.357813
#3  9.343139 1.688075
#4  9.335320 1.904806
#5 10.397774 1.942586
#6 10.128553 1.350930
map <- mxEval(A, growthCurveFit)[growthCurveFit$manifestVars, growthCurveFit$latentVars]
# Note that this is the mapping for defvar.row=1. If you have different times of measurement
#  for different people, you'll need a different 'map' for each person.
obs <- t(map %*% t(fs[,,1]))
dim(obs) # structured like original data set
#[1] 500   5
matplot(t(obs), type='l')
# quick plot

The general idea here is to (1) get the regression factor scores, (2) get the latent-to-observed variable map (i.e. factor loadings), and then (3) compute the predicted observed scores based on the factor scores and the mapping. Finally, a simple plot is made with matplot.