--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Fri Dec 09 19:09:35 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/439/Wnt2-PA/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS/439/Wnt2-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/439/Wnt2-PA/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/439/Wnt2-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -3199.69 -3214.97 2 -3199.32 -3216.07 -------------------------------------- TOTAL -3199.49 -3215.67 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS/439/Wnt2-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/439/Wnt2-PA/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/439/Wnt2-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 0.743549 0.006198 0.595570 0.900638 0.740252 891.71 1116.87 1.000 r(A<->C){all} 0.122289 0.000461 0.078390 0.160928 0.121234 1006.91 1055.82 1.000 r(A<->G){all} 0.241021 0.001219 0.176093 0.310992 0.239454 724.90 733.69 1.000 r(A<->T){all} 0.111395 0.000915 0.054572 0.170777 0.109484 813.85 872.02 1.000 r(C<->G){all} 0.049044 0.000121 0.029718 0.072999 0.048094 985.85 1030.53 1.000 r(C<->T){all} 0.419369 0.001854 0.339461 0.504459 0.417453 615.23 723.38 1.000 r(G<->T){all} 0.056883 0.000266 0.026754 0.089139 0.055945 926.31 1010.97 1.000 pi(A){all} 0.212398 0.000158 0.188264 0.237172 0.212105 928.56 954.75 1.000 pi(C){all} 0.314347 0.000190 0.288020 0.340774 0.314431 905.68 1057.21 1.000 pi(G){all} 0.311758 0.000196 0.283642 0.337729 0.311813 975.70 1094.62 1.001 pi(T){all} 0.161496 0.000110 0.140399 0.182245 0.161356 1098.94 1130.76 1.000 alpha{1,2} 0.166115 0.000536 0.124260 0.213543 0.163444 1182.55 1206.28 1.000 alpha{3} 2.890917 0.789887 1.366468 4.673697 2.768291 1487.72 1494.36 1.000 pinvar{all} 0.531197 0.001478 0.452431 0.602085 0.533092 1353.51 1399.58 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 -2940.374583 Model 2: PositiveSelection -2940.374587 Model 0: one-ratio -2969.136327 Model 3: discrete -2936.864527 Model 7: beta -2939.941339 Model 8: beta&w>1 -2937.016945 Model 0 vs 1 57.523488000000725 Model 2 vs 1 7.999999979801942E-6 Model 8 vs 7 5.848788000000241