--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Wed Nov 08 10:32:55 WET 2017 codeml.models=0 1 2 3 7 8 mrbayes.mpich= mrbayes.ngen=1000000 tcoffee.alignMethod=MUSCLE 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/ADOPS1/ZikaADOPSresults/pr/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS1/ZikaADOPSresults/pr/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/ZikaADOPSresults/pr/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/ADOPS1/ZikaADOPSresults/pr/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -941.72 -979.63 2 -941.91 -984.24 -------------------------------------- TOTAL -941.81 -983.56 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS1/ZikaADOPSresults/pr/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/ZikaADOPSresults/pr/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/ADOPS1/ZikaADOPSresults/pr/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 1.284241 0.071519 0.819832 1.798684 1.243649 533.06 719.34 1.002 r(A<->C){all} 0.052870 0.000491 0.014432 0.096465 0.050331 651.63 694.94 1.003 r(A<->G){all} 0.182709 0.003526 0.076589 0.300048 0.176887 362.76 393.55 1.000 r(A<->T){all} 0.016335 0.000170 0.000001 0.040703 0.013252 439.45 587.58 1.000 r(C<->G){all} 0.009389 0.000078 0.000003 0.026448 0.006890 744.40 819.67 1.000 r(C<->T){all} 0.716030 0.005516 0.565985 0.850447 0.720446 333.78 384.20 1.001 r(G<->T){all} 0.022666 0.000152 0.002607 0.047007 0.020660 362.65 614.03 1.000 pi(A){all} 0.261494 0.000570 0.216056 0.308144 0.260412 1076.43 1094.60 1.000 pi(C){all} 0.215484 0.000519 0.173447 0.261379 0.215106 513.09 822.57 1.000 pi(G){all} 0.290855 0.000629 0.239508 0.336884 0.291105 1099.73 1165.36 1.000 pi(T){all} 0.232167 0.000543 0.189275 0.279048 0.231033 968.25 1088.37 1.000 alpha{1,2} 0.160170 0.001649 0.091637 0.245280 0.155157 1114.69 1175.96 1.000 alpha{3} 1.655989 0.479472 0.530707 3.007114 1.530987 996.14 998.82 1.001 pinvar{all} 0.210514 0.009180 0.009376 0.365474 0.216725 893.98 927.94 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 -913.421485 Model 2: PositiveSelection -913.421485 Model 0: one-ratio -915.624986 Model 3: discrete -911.681233 Model 7: beta -912.031149 Model 8: beta&w>1 -912.031209 Model 0 vs 1 4.407002000000148 Model 2 vs 1 0.0 Model 8 vs 7 1.199999999244028E-4