--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Tue Nov 22 08:58:41 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/3/acj6-PJ/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS/3/acj6-PJ/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PJ/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/3/acj6-PJ/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -2211.19 -2230.84 2 -2210.98 -2231.54 -------------------------------------- TOTAL -2211.08 -2231.25 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS/3/acj6-PJ/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PJ/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/3/acj6-PJ/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 0.396651 0.003701 0.295318 0.521331 0.390237 1276.35 1335.02 1.000 r(A<->C){all} 0.115081 0.001229 0.049035 0.183334 0.112285 745.12 876.80 1.000 r(A<->G){all} 0.235409 0.002933 0.131444 0.339849 0.230460 827.79 864.87 1.000 r(A<->T){all} 0.126386 0.001867 0.043650 0.210179 0.122094 720.64 722.87 1.000 r(C<->G){all} 0.062252 0.000377 0.028406 0.101082 0.060335 1061.09 1104.27 1.000 r(C<->T){all} 0.449642 0.004082 0.327147 0.575020 0.449460 528.17 648.74 1.000 r(G<->T){all} 0.011231 0.000110 0.000008 0.032123 0.008286 903.77 958.44 1.000 pi(A){all} 0.241380 0.000156 0.218427 0.266851 0.241174 1013.39 1183.39 1.000 pi(C){all} 0.306940 0.000179 0.281624 0.333857 0.306532 1178.50 1252.93 1.000 pi(G){all} 0.272821 0.000178 0.248136 0.300715 0.273184 1065.83 1176.18 1.000 pi(T){all} 0.178860 0.000120 0.157491 0.199860 0.178516 1225.33 1252.68 1.000 alpha{1,2} 0.050017 0.000677 0.000121 0.088401 0.054054 1027.61 1169.60 1.000 alpha{3} 2.324506 0.646379 1.014259 3.946140 2.202287 1461.39 1481.20 1.000 pinvar{all} 0.754197 0.000686 0.701980 0.804399 0.755113 1416.25 1458.62 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 -2116.486373 Model 2: PositiveSelection -2116.483814 Model 0: one-ratio -2116.532034 Model 3: discrete -2116.483814 Model 7: beta -2116.483433 Model 8: beta&w>1 -2116.485896 Model 0 vs 1 0.0913219999993089 Model 2 vs 1 0.005118000000038592 Model 8 vs 7 0.004926000000523345