--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Mon Apr 30 19:04:11 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/E_2/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS1/DNG_N1/E_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_2/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/E_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -12469.67 -12516.65 2 -12468.79 -12509.57 -------------------------------------- TOTAL -12469.14 -12515.96 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS1/DNG_N1/E_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_2/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/E_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 9.399429 0.302178 8.284445 10.476200 9.384797 538.51 565.01 1.000 r(A<->C){all} 0.034689 0.000025 0.025598 0.045019 0.034601 803.04 873.14 1.001 r(A<->G){all} 0.176774 0.000152 0.151480 0.199359 0.176845 554.52 574.07 1.000 r(A<->T){all} 0.050497 0.000034 0.039501 0.062356 0.050272 615.75 655.71 1.001 r(C<->G){all} 0.017198 0.000020 0.008373 0.025501 0.016938 693.06 806.78 1.001 r(C<->T){all} 0.696792 0.000243 0.668569 0.728087 0.697019 549.55 562.10 1.000 r(G<->T){all} 0.024051 0.000024 0.014532 0.033421 0.023871 852.02 933.77 1.000 pi(A){all} 0.350061 0.000072 0.334498 0.367593 0.349939 648.59 747.13 1.000 pi(C){all} 0.213442 0.000049 0.199398 0.226402 0.213687 456.09 613.65 1.000 pi(G){all} 0.242822 0.000061 0.227250 0.257754 0.242826 743.92 794.44 1.000 pi(T){all} 0.193676 0.000045 0.179503 0.205815 0.193549 625.52 727.58 1.000 alpha{1,2} 0.197264 0.000098 0.177429 0.216286 0.196838 871.13 1121.80 1.000 alpha{3} 4.842499 0.690217 3.356065 6.466790 4.770707 1034.34 1201.16 1.000 pinvar{all} 0.093876 0.000301 0.061314 0.127653 0.093513 788.23 914.69 1.001 ------------------------------------------------------------------------------------------------------ * 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 -11942.430876 Model 2: PositiveSelection -11942.430881 Model 0: one-ratio -11981.93516 Model 3: discrete -11801.936554 Model 7: beta -11802.700904 Model 8: beta&w>1 -11802.662628 Model 0 vs 1 79.0085680000011 Model 2 vs 1 9.999999747378752E-6 Model 8 vs 7 0.07655199999862816