--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Fri May 04 08:39:17 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_N1/NS1_1/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS1/DNG_N1/NS1_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/NS1_1/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_N1/NS1_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -8288.60 -8339.00 2 -8289.73 -8335.05 -------------------------------------- TOTAL -8289.01 -8338.33 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS1/DNG_N1/NS1_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/NS1_1/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_N1/NS1_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 7.629462 0.264793 6.664328 8.648123 7.615478 626.90 630.16 1.000 r(A<->C){all} 0.027256 0.000031 0.016680 0.038603 0.027068 915.24 920.38 1.000 r(A<->G){all} 0.214614 0.000290 0.180679 0.246830 0.214410 431.54 540.16 1.000 r(A<->T){all} 0.054603 0.000054 0.040500 0.069083 0.054372 677.09 782.05 1.000 r(C<->G){all} 0.026701 0.000044 0.014042 0.039669 0.026263 542.31 740.79 1.000 r(C<->T){all} 0.657788 0.000427 0.619472 0.700583 0.657409 402.98 504.74 1.000 r(G<->T){all} 0.019037 0.000043 0.006703 0.031868 0.018747 750.48 751.18 1.000 pi(A){all} 0.347989 0.000109 0.328535 0.369582 0.347952 678.08 788.42 1.001 pi(C){all} 0.230371 0.000083 0.213430 0.248975 0.230040 938.95 1006.28 1.001 pi(G){all} 0.224151 0.000089 0.207534 0.243785 0.223809 672.00 819.42 1.000 pi(T){all} 0.197489 0.000063 0.182538 0.213198 0.197507 464.53 617.41 1.000 alpha{1,2} 0.194319 0.000141 0.172632 0.218273 0.193720 1146.54 1259.02 1.000 alpha{3} 4.908844 0.902357 3.132158 6.668747 4.812321 1128.50 1314.75 1.000 pinvar{all} 0.142613 0.000575 0.097070 0.187134 0.141572 923.34 1029.30 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 -7921.44414 Model 2: PositiveSelection -7921.44414 Model 0: one-ratio -8031.533019 Model 3: discrete -7827.202985 Model 7: beta -7831.309872 Model 8: beta&w>1 -7829.697256 Model 0 vs 1 220.17775800000163 Model 2 vs 1 0.0 Model 8 vs 7 3.2252319999988686