Hello, To start off, I am using the full version of SAS not the UE. I have reasonable processing resources i.e. 32g ram, a modern processor, and I have updated the config file to maximize the available ram for analysis. In spite of these efforts, I continue to experience 'integer overflow' and am unable to process this proc mixed code due to insufficient memory. I have 2 questions for the esteemed experts who frequent this forum. 1) Is there anything in my code that strikes you as particularly resource demanding that I might be able to modify? 2) Is it possible that elements of how I have structured the 2 time parameters are problematic? This is part of a large, longitudinal analysis. Participants have from 1 to 14 assessment times. In the proc mixed equation both independent variables are time parameters. The first, age_curve, is the subject age at time of assessment. The second, time_discharge, is the time in years, to one decimal place, before a critical event i.e. discharge from one program to another. I am running this analysis as a Change Point Model. The age_curve acts as a sort of control group and the time_discharge is for only those who fall out of the main program and are discharged. The dependent variable is a measure of functional independence (for those who are interested), I'll call that IND. One thing that has me curious is if the decision to leave measurement occasions prior to the change point in with the age_curve variable is causing excessive memory demands. For example, subject x has an assessment at 1,2,3,4,5,6 years. I set the change point at 3.5 years and in so doing assessments 4,5 & 6 are assigned to the time_discharge variable while assessments 1,2 & 3 remain in the age_curve variable. Would splitting the subjects across these 2 variables be problematic in terms of processing demands? I have data from about 150,000 participants, so roughly 4 times that many total observations as the average number of assessments is about 4. proc mixed data=have noclprint noitprint covtest method=ml; class IDVar; model IND= age_curve time_discharge /solution notest; random intercept age_curve time_discharge /type=un subject=IDVar gcorr; run;
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