Also, it is a bad idea to make your randomized analyses dependent on bit-for-bit reproducibility... first, different computer architectures handle floating point intermediate calculations differently, and second the whole point of a randomized trial is that it converges to some quantifiable mean result regardless of the path taken to get there.
On October 3, 2025 2:57:46 AM PDT, Jeanne Moreau <[email protected]> wrote: >Good Morning, > >I am working with LDA models in R (using both topicmodels::LDA and >quanteda::textmodel_lda) and noticed that the results differ slightly >across different machines, even when I use set.seed(1234) and the same >dataset. > >So, I have a few questions: >Is this expected due to BLAS/LAPACK or low-level random number generation >differences? >Is there a recommended way to enforce bit-for-bit reproducibility of LDA >results across machines in R? >Would you recommend always saving fitted models with saveRDS() to ensure >reproducible outputs instead of re-fitting? > >Thanks a lot for your guidance. > >Best regards, > >Jeanne Moreau > > [[alternative HTML version deleted]] > >______________________________________________ >[email protected] mailing list -- To UNSUBSCRIBE and more, see >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide https://www.R-project.org/posting-guide.html >and provide commented, minimal, self-contained, reproducible code. -- Sent from my phone. Please excuse my brevity. ______________________________________________ [email protected] mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide https://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
