--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Tue Nov 22 03:35:29 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/Acon-PB/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS/3/Acon-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/Acon-PB/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/Acon-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -7998.01 -8017.48 2 -7998.47 -8015.01 -------------------------------------- TOTAL -7998.21 -8016.87 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS/3/Acon-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/Acon-PB/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/Acon-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 0.996975 0.002714 0.901702 1.103123 0.994862 1168.44 1264.58 1.000 r(A<->C){all} 0.063677 0.000087 0.046344 0.082682 0.063289 996.95 1050.39 1.000 r(A<->G){all} 0.161645 0.000305 0.129517 0.197089 0.161357 944.25 964.01 1.000 r(A<->T){all} 0.106416 0.000301 0.071551 0.138749 0.106220 983.07 1041.18 1.001 r(C<->G){all} 0.050163 0.000044 0.037705 0.063625 0.049976 1152.00 1188.53 1.000 r(C<->T){all} 0.534763 0.000601 0.491987 0.585625 0.534856 713.77 795.37 1.000 r(G<->T){all} 0.083336 0.000124 0.061320 0.104595 0.082856 1215.74 1310.32 1.000 pi(A){all} 0.215627 0.000070 0.199796 0.232044 0.215598 1137.34 1144.67 1.000 pi(C){all} 0.324476 0.000079 0.306776 0.341259 0.324297 940.62 1063.18 1.000 pi(G){all} 0.272241 0.000077 0.256423 0.290243 0.272165 1142.70 1149.75 1.000 pi(T){all} 0.187656 0.000051 0.174913 0.202724 0.187545 1062.55 1134.29 1.000 alpha{1,2} 0.105143 0.000057 0.090836 0.120260 0.104921 1320.73 1406.14 1.000 alpha{3} 5.212324 1.260173 3.287058 7.506998 5.061816 1233.10 1367.05 1.000 pinvar{all} 0.346586 0.000700 0.292826 0.395863 0.347289 1130.66 1315.83 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 -7488.36443 Model 2: PositiveSelection -7488.36443 Model 0: one-ratio -7561.165264 Model 3: discrete -7461.165259 Model 7: beta -7461.607272 Model 8: beta&w>1 -7461.60783 Model 0 vs 1 145.60166800000115 Model 2 vs 1 0.0 Model 8 vs 7 0.0011159999994561076