I am using OpenMx in conjunction with SEM Trees. As a result of this, I have to include the whole dataset (30,000 x 1200). Right now, it takes about 30 seconds to run the current code:
hpc20 <- mxModel("1 Variable",type="RAM",
from= "A_AbsRea" ,
arrows=1,free= T,values= 8,labels = "mean1"))
system.time(hpc20Fit <- mxRun(hpc20,suppressWarnings=T,silent=T))
user system elapsed
30.723 2.092 32.806
I am trying to figure out a way to speed this up. Because the computation takes so long in using SEM Trees with such a large dataset, we are starting with the simplest possible model. I suspect the model takes so long to run because of the computation of the descriptives for all of the other variables. When the dataset is changed to just include the one variable, it takes a split second.
I have tried to change this option thus far:
hpc20 <- mxOption(hpc20,'No Sort Data',"A_AbsRea")
although it doesn't off change the computation time. Is there a different way to have OpenMx only calculate values for the 1 variable of interest? Sorry if I am not clear or am missing something quite obvious.