--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Tue Nov 22 08:30:28 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-PH/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS/3/acj6-PH/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PH/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-PH/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -2202.33 -2226.49 2 -2202.26 -2222.74 -------------------------------------- TOTAL -2202.30 -2225.82 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS/3/acj6-PH/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PH/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-PH/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 0.398948 0.003586 0.291985 0.522766 0.394293 1187.85 1344.42 1.000 r(A<->C){all} 0.116702 0.001314 0.047389 0.185891 0.113409 539.30 694.28 1.000 r(A<->G){all} 0.240539 0.003111 0.140886 0.356073 0.235726 607.17 636.80 1.002 r(A<->T){all} 0.128960 0.001939 0.051691 0.215578 0.123400 858.59 864.40 1.000 r(C<->G){all} 0.061557 0.000391 0.025350 0.100529 0.059699 776.74 869.03 1.000 r(C<->T){all} 0.440501 0.004222 0.319848 0.571056 0.441155 552.44 611.49 1.001 r(G<->T){all} 0.011741 0.000119 0.000001 0.035259 0.008666 919.40 1037.66 1.001 pi(A){all} 0.238666 0.000159 0.213234 0.263546 0.238715 1112.06 1220.97 1.000 pi(C){all} 0.308803 0.000172 0.281245 0.333079 0.308930 1286.69 1343.28 1.000 pi(G){all} 0.272981 0.000175 0.246631 0.297647 0.272874 1129.15 1155.12 1.000 pi(T){all} 0.179549 0.000118 0.157869 0.199807 0.179360 1135.79 1182.40 1.000 alpha{1,2} 0.048430 0.000662 0.000106 0.087386 0.051533 1196.38 1264.40 1.000 alpha{3} 2.346881 0.620205 1.033658 3.967005 2.224011 1321.58 1411.29 1.000 pinvar{all} 0.753122 0.000680 0.702684 0.803493 0.754077 1335.67 1338.28 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 -2106.513824 Model 2: PositiveSelection -2106.511272 Model 0: one-ratio -2106.559576 Model 3: discrete -2106.511272 Model 7: beta -2106.510882 Model 8: beta&w>1 -2106.513346 Model 0 vs 1 0.09150399999998626 Model 2 vs 1 0.005103999999846565 Model 8 vs 7 0.004928000000290922