Hi fellow community members,
I'm working with quite large data sets ~500000 lines of 4-28 variables (number of covariate patterns >500). I'm trying to simplify the input data into OpenMx by aggregating covariate patterns so that I can specify one line per unique pattern, and then multiply the objective function by how many observations there are of the specific pattern. The reason for me to do this is that I want to save computing time.
I currently specify a sub-model for each pattern and simply set the numObs to the number of observations, however this takes longer time than just using the raw data. It seems to me that there should be a number of much simpler ways to do this, but being a not-so-great programmer I don't know how to find out which functions to alter to achieve this. Preferably I would like to be able to input the data as "raw", but only one row per covariate pattern and the supply an array of values to numObs, each value representing the number of observations of the specific pattern in the corresponding row.
My questions are 2:
1. Will I gain any computing time by doing this, or is it redundant since the optimizer already does this, or something similar to it?
2. How to do it, or perhaps some explanation of which functions to alter. (e.g. mxData, mxFIMLObjective and something with the NPSOL optimizer?)