Hi everyone,

I am trying to fit an LGC model with individually-varying time points. I've added definition variables as labels, and the model itself works well (the script is pasted below). I am just a little confused about the estimations. As my understanding, in my case, I should have full information maximum likelihood (FIML) rather than ML since individual contributions to the likelihood depends on the individual-specific time; but I am not sure if I can get the FIML through my codes. Do I have to use mxFitFunctionML() or some other statement? Thank you in advance.

library(OpenMx) lg.math.age.omx <- mxModel('LGC with individually varying time points', type='RAM', mxData(observed = data, type='raw'), manifestVars=c('y1','y2','y3','y4'), latentVars=c('eta_1','eta_2'), # residual variance paths mxPath(from=c('y1','y2','y3','y4'), arrows=2, free=TRUE, values=40, labels='th'), # latent variable variances and covariance mxPath(from=c('eta1','eta2'), arrows=2, connect='unique.pairs', free=TRUE, values=c(1,0,1), labels=c('psi11','psi21','psi22')), # regression weights mxPath(from='eta1', to=c('y1','y2','y3','y4'), arrows=1, free=FALSE, values=1), mxPath(from='eta2', to=c('y1','y2','y3','y4'), arrows=1, free=FALSE, labels=c('data.t1','data.t2','data.t3','data.t4')), # means and intercepts mxPath(from='one', to=c('eta1','eta2'), arrows=1, free=TRUE, values=c(40, 4), labels=c('alpha1','alpha2')) ) # close model lg.math.age. t <- mxRun(lg.math.age.omx) summary(lg.math.age. t)