Pulling specific parameters from models to prevent exhausting memory.

-----Original Message-----
From: R-sig-mixed-models <r-sig-mixed-models-bounces at r-project.org> On
Behalf Of Ades, James
Sent: Sunday, October 18, 2020 2:01 AM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Pulling specific parameters from models to prevent
exhausting memory.

Hi all,

I'm modeling fMRI imaging data using lme4. There are 4 time points and
roughly 550 subjects with 27,730 regions of interest (these are the variables).
Since I have access to a super computer, my thought was to create a long
dataset with a repeated measures of regions of interest per time point and
then subjects over the 4 time points. I'm using the model below. I gather the
regions of interest using the super computer because it ends up being
roughly 70 million something observations. Timepoint is discrete and
timepoint.nu is just numerical time point.

lmer(connectivity ~ roi * timepoint + (timepoint.nu|subjectID) +
(timepoint.nu|subjectID:roi), na.action = 'na.exclude', control =
lmerControl(optimizer = "nloptwrap", calc.derivs = FALSE), REML = FALSE,
data)

I received back the following error: "cannot allocate vector of size 30206.2
GbExecution halted"

So I'm wondering how I can only pull the essential parameters I need (group
means vs individual fixed effects) while modeling, such that the super
computer can finish the job without exhausting the memory. I say group
means because I will eventually be adding in covariates.

Also, the super computer rules are that the job must finish within two days.
I'm not sure that this would, so I'm wondering whether there is any way to
parallel code in lme4 such that I could make access of multiple cores and
nodes.

I've included a slice of data here:
https://drive.google.com/file/d/1mhTj6qZZ2nT35fXUuYG_ThQ-QtWbb-
8L/view?usp=sharing

Thanks much,

James



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