[R-meta] Handling dependencies among multiple independent and dependent variables
Viechtbauer Wolfgang (SP)
wolfgang.viechtbauer at maastrichtuniversity.nl
Tue Apr 3 23:04:02 CEST 2018
Glad you found that page -- I would have direct you there anyway.
How many parameters are you actually trying to estimate?
From: Jens Schüler [mailto:jens.schueler at wiwi.uni-kl.de]
Sent: Tuesday, 03 April, 2018 3:45
To: Viechtbauer Wolfgang (SP); r-sig-meta-analysis at r-project.org
Subject: AW: Handling dependencies among multiple independent and dependent variables
Nevermind, I just went through your info on speeding up model fitting:
Even though I am using an AMD R5 processor with 6 physical cores, I decided
to give the MKL avenue a shot and well, it cranked the CPU usage up from
about 7% to 55% - hopefully this speeds things up.
Von: R-sig-meta-analysis <r-sig-meta-analysis-bounces at r-project.org> Im
Auftrag von Jens Schüler
Gesendet: Montag, 2. April 2018 22:17
An: Viechtbauer Wolfgang (SP)
<wolfgang.viechtbauer at maastrichtuniversity.nl>;
r-sig-meta-analysis at r-project.org
Betreff: Re: [R-meta] Handling dependencies among multiple independent and
after rearranging my coding sheet, the rmat function worked like a charm (of
course I screwed up here and there before I got it right).
However, currently I am wondering about the computational performance of
matrix calculations in R.
My data consists of ~ 1700 observations drawn from 422 samples and the
rma.mv function is currently up and running for over 5 hours.
I use the latest base version of R, together with R Studio, and have a
potent CPU in my desktop - of which R only uses about 7%.
Thus, are the calculations really that lengthy/tedious or is it more likely
that I still screwed something up?
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