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I suspect that perhaps the starting seed ensures reproducibility up to optimization of the "normal" tree inference, but then when FunDi optimization starts, some other random element causes two different runs to diverge?
Also, there is the numerical underflow warning, could that be related somehow?
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IQTREE version:
IQ-TREE multicore version 2.2.5 COVID-edition for Linux 64-bit built Sep 15 2023
Command line:
iqtree2 -s supermatrix_loci_183.fasta -te c60.treefile -seed 137888 -m LG+fundi_C10+G4 -mdef re-optimized-model.nexus -a 0.6250003068091398 --fundi $TAXA,estimate --prefix supermatrix_loci_183.fullTree.C10.out/fundi_full_tree -nt 20 -prec 10 --quiet
The model
fundi_C10
is specified in there-optimized-model.nexus
file, which states the per-class frequencies and weights.I ran this exact run twice, and it gave me two markedly different results with regards to FunDi results:
Run1:
Run2:
Note that these FunDi results are different, particularly the inferred central branch length and log likelihood:
Run1:
Run2:
The only difference between these runs is that they were run on different nodes of the same cluster.
Run1:
Run2:
I suspect that perhaps the starting seed ensures reproducibility up to optimization of the "normal" tree inference, but then when FunDi optimization starts, some other random element causes two different runs to diverge?
Also, there is the numerical underflow warning, could that be related somehow?
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