--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Sat Jun 02 20:06:39 WEST 2018 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/DNG_A1/NS2B_5/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS1/DNG_A1/NS2B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2B_5/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/DNG_A1/NS2B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -4026.69 -4075.62 2 -4028.16 -4071.43 -------------------------------------- TOTAL -4027.18 -4074.94 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS1/DNG_A1/NS2B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2B_5/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/DNG_A1/NS2B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 7.124588 0.298460 6.127232 8.263139 7.093845 964.81 976.02 1.000 r(A<->C){all} 0.066252 0.000131 0.043401 0.088526 0.065766 655.28 818.10 1.000 r(A<->G){all} 0.250646 0.000679 0.202312 0.302209 0.249876 527.72 541.27 1.000 r(A<->T){all} 0.071268 0.000147 0.049149 0.095759 0.070423 811.15 833.53 1.000 r(C<->G){all} 0.038741 0.000108 0.018485 0.058716 0.038122 996.41 998.92 1.000 r(C<->T){all} 0.546956 0.000976 0.484762 0.605374 0.547237 488.75 551.29 1.000 r(G<->T){all} 0.026138 0.000080 0.010372 0.044096 0.025162 988.52 996.19 1.000 pi(A){all} 0.323125 0.000241 0.293787 0.353921 0.323430 948.31 1090.09 1.000 pi(C){all} 0.226402 0.000191 0.199964 0.253395 0.226094 768.16 832.58 1.000 pi(G){all} 0.235813 0.000211 0.209071 0.264508 0.235661 730.34 809.15 1.000 pi(T){all} 0.214660 0.000175 0.189871 0.241630 0.214228 810.96 1006.19 1.000 alpha{1,2} 0.292329 0.000970 0.239622 0.359700 0.289304 1448.51 1474.75 1.000 alpha{3} 3.690365 0.798533 2.028622 5.346438 3.602254 1431.34 1466.17 1.000 pinvar{all} 0.054811 0.000700 0.004618 0.104609 0.053790 1151.76 1266.43 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 -3832.589069 Model 2: PositiveSelection -3832.589049 Model 0: one-ratio -3844.501637 Model 3: discrete -3806.431394 Model 7: beta -3807.816542 Model 8: beta&w>1 -3807.817845 Model 0 vs 1 23.825135999999475 Model 2 vs 1 3.999999989900971E-5 Model 8 vs 7 0.002606000000014319