--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Tue Nov 22 08:45:27 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-PI/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS/3/acj6-PI/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PI/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-PI/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -2266.17 -2284.86 2 -2265.95 -2284.74 -------------------------------------- TOTAL -2266.06 -2284.80 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS/3/acj6-PI/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PI/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-PI/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 0.388850 0.003261 0.282135 0.497652 0.384365 1312.29 1397.93 1.000 r(A<->C){all} 0.118751 0.001152 0.060245 0.190239 0.115789 848.97 932.21 1.000 r(A<->G){all} 0.247701 0.002851 0.148755 0.355528 0.245181 471.46 516.19 1.000 r(A<->T){all} 0.111127 0.001423 0.041648 0.184680 0.107305 777.09 799.93 1.000 r(C<->G){all} 0.065652 0.000415 0.029946 0.107426 0.063244 844.44 944.36 1.000 r(C<->T){all} 0.445810 0.003740 0.322958 0.563454 0.446044 672.17 746.40 1.000 r(G<->T){all} 0.010958 0.000105 0.000022 0.031156 0.007902 782.15 813.64 1.000 pi(A){all} 0.241819 0.000149 0.217424 0.265722 0.241292 1266.11 1281.69 1.000 pi(C){all} 0.306077 0.000175 0.280298 0.331841 0.306253 1162.05 1280.55 1.000 pi(G){all} 0.270149 0.000173 0.244127 0.295796 0.270271 1262.34 1311.24 1.000 pi(T){all} 0.181955 0.000121 0.160300 0.202828 0.181911 1185.48 1279.24 1.000 alpha{1,2} 0.045390 0.000683 0.000112 0.086819 0.047038 1048.01 1140.40 1.000 alpha{3} 2.455511 0.675468 1.067352 4.100930 2.337238 1389.62 1445.31 1.000 pinvar{all} 0.746716 0.000690 0.694248 0.797080 0.747571 1285.60 1323.85 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 -2168.428845 Model 2: PositiveSelection -2168.426242 Model 0: one-ratio -2168.474962 Model 3: discrete -2168.426242 Model 7: beta -2168.42576 Model 8: beta&w>1 -2168.428362 Model 0 vs 1 0.0922339999997348 Model 2 vs 1 0.005205999999816413 Model 8 vs 7 0.005204000000048836