--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Wed May 30 13:43:40 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/NS2A_1/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS1/DNG_A1/NS2A_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2A_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_A1/NS2A_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -7580.43 -7634.99 2 -7585.83 -7633.43 -------------------------------------- TOTAL -7581.12 -7634.49 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS1/DNG_A1/NS2A_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2A_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_A1/NS2A_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} 10.275873 0.484566 8.951379 11.665340 10.256450 549.80 730.51 1.002 r(A<->C){all} 0.046232 0.000067 0.029964 0.061376 0.045887 691.07 744.71 1.000 r(A<->G){all} 0.241327 0.000319 0.205915 0.276539 0.241358 549.70 553.44 1.000 r(A<->T){all} 0.056932 0.000061 0.041280 0.071485 0.056639 683.07 835.83 1.000 r(C<->G){all} 0.033750 0.000058 0.020306 0.049952 0.033351 836.19 889.30 1.000 r(C<->T){all} 0.584958 0.000450 0.541341 0.625048 0.584039 569.80 581.38 1.000 r(G<->T){all} 0.036800 0.000056 0.022125 0.051236 0.036376 981.56 1041.03 1.000 pi(A){all} 0.300289 0.000113 0.280601 0.321731 0.299964 640.69 868.50 1.000 pi(C){all} 0.216282 0.000089 0.198416 0.234749 0.216265 679.26 805.10 1.002 pi(G){all} 0.246808 0.000108 0.227433 0.267562 0.246704 815.72 866.35 1.001 pi(T){all} 0.236621 0.000095 0.217929 0.255749 0.236795 738.45 898.58 1.000 alpha{1,2} 0.418408 0.001734 0.343737 0.504083 0.414891 1164.32 1197.36 1.000 alpha{3} 4.607867 0.961182 2.826767 6.546296 4.498651 1272.24 1384.16 1.000 pinvar{all} 0.030873 0.000338 0.000049 0.065160 0.028076 1223.28 1272.37 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 -7286.010144 Model 2: PositiveSelection -7286.010144 Model 0: one-ratio -7293.64689 Model 3: discrete -7228.709293 Model 7: beta -7230.250979 Model 8: beta&w>1 -7230.253144 Model 0 vs 1 15.27349200000026 Model 2 vs 1 0.0 Model 8 vs 7 0.004329999999754364