--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Sat Nov 12 06:00:54 WET 2016 codeml.models=0 1 2 3 7 8 mrbayes.mpich= mrbayes.ngen=1000000 tcoffee.alignMethod=CLUSTALW2 tcoffee.params= tcoffee.maxSeqs=0 codeml.bin=codeml mrbayes.tburnin=2500 codeml.dir= input.sequences= mrbayes.pburnin=2500 mrbayes.bin=mb_adops tcoffee.bin=t_coffee_ADOPS mrbayes.dir=/usr/bin/ tcoffee.dir= tcoffee.minScore=3 input.fasta=/opt/ADOPS/200/CG9485-PE/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS/200/CG9485-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/200/CG9485-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run2.p": (Use the harmonic mean for Bayes factor comparisons of models) (Values are saved to the file /opt/ADOPS/200/CG9485-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -18442.35 -18458.82 2 -18442.08 -18461.12 -------------------------------------- TOTAL -18442.20 -18460.52 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS/200/CG9485-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/200/CG9485-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run2.p": Summaries are based on a total of 3002 samples from 2 runs. Each run produced 2001 samples of which 1501 samples were included. Parameter summaries saved to file "/opt/ADOPS/200/CG9485-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 1.443493 0.002399 1.347477 1.536372 1.443360 1355.73 1406.92 1.001 r(A<->C){all} 0.096105 0.000059 0.081506 0.111075 0.095845 779.75 806.47 1.000 r(A<->G){all} 0.278755 0.000185 0.253583 0.305420 0.278557 820.63 854.57 1.000 r(A<->T){all} 0.119692 0.000112 0.099913 0.141759 0.119314 885.79 960.53 1.000 r(C<->G){all} 0.044835 0.000019 0.037216 0.054293 0.044761 865.02 885.52 1.001 r(C<->T){all} 0.383447 0.000218 0.356730 0.413327 0.383443 779.95 786.80 1.000 r(G<->T){all} 0.077166 0.000047 0.064335 0.090396 0.077073 964.18 1107.37 1.000 pi(A){all} 0.217635 0.000035 0.206537 0.229389 0.217504 877.20 970.07 1.000 pi(C){all} 0.290079 0.000037 0.279033 0.302627 0.289904 808.61 922.94 1.000 pi(G){all} 0.277126 0.000037 0.264609 0.288545 0.277074 1175.07 1207.30 1.001 pi(T){all} 0.215159 0.000029 0.204851 0.225547 0.215108 881.22 953.46 1.003 alpha{1,2} 0.129043 0.000033 0.117349 0.140089 0.128760 1436.82 1452.52 1.000 alpha{3} 6.304459 1.210395 4.360364 8.443409 6.191276 1491.93 1496.46 1.000 pinvar{all} 0.313402 0.000318 0.278929 0.348552 0.313513 1135.10 1191.35 1.000 ------------------------------------------------------------------------------------------------------ * Convergence diagnostic (ESS = Estimated Sample Size); min and avg values correspond to minimal and average ESS among runs. ESS value below 100 may indicate that the parameter is undersampled. + Convergence diagnostic (PSRF = Potential Scale Reduction Factor; Gelman and Rubin, 1992) should approach 1.0 as runs converge. Setting sumt conformat to Simple --- CODEML SUMMARY Model 1: NearlyNeutral -17318.601925 Model 2: PositiveSelection -17318.602128 Model 0: one-ratio -17455.212255 Model 3: discrete -17250.559313 Model 7: beta -17252.27484 Model 8: beta&w>1 -17251.474405 Model 0 vs 1 273.22065999999904 Model 2 vs 1 4.059999992023222E-4 Model 8 vs 7 1.6008699999947567