--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Thu May 03 15:07:15 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_5/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS1/DNG_N1/E_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_5/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_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -12239.74 -12286.21 2 -12238.53 -12291.36 -------------------------------------- TOTAL -12238.96 -12290.67 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS1/DNG_N1/E_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_5/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_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 8.945854 0.287377 7.991090 10.034280 8.917087 515.01 651.12 1.000 r(A<->C){all} 0.041307 0.000027 0.030601 0.050829 0.041020 612.15 692.28 1.000 r(A<->G){all} 0.182715 0.000146 0.159813 0.205367 0.182464 320.64 508.08 1.001 r(A<->T){all} 0.050040 0.000035 0.038216 0.061534 0.049986 473.69 686.10 1.000 r(C<->G){all} 0.015137 0.000016 0.007830 0.023260 0.014929 969.73 971.29 1.006 r(C<->T){all} 0.681954 0.000243 0.653149 0.712402 0.682130 237.91 464.81 1.002 r(G<->T){all} 0.028848 0.000029 0.018325 0.039302 0.028618 563.16 731.43 1.000 pi(A){all} 0.347795 0.000072 0.332011 0.365015 0.347649 619.81 726.76 1.001 pi(C){all} 0.219274 0.000051 0.204842 0.233093 0.219387 735.50 777.57 1.000 pi(G){all} 0.240934 0.000058 0.225859 0.254923 0.240957 814.69 876.80 1.000 pi(T){all} 0.191997 0.000044 0.178945 0.204599 0.191875 500.92 709.56 1.000 alpha{1,2} 0.203233 0.000118 0.181688 0.223570 0.202619 994.21 1122.13 1.000 alpha{3} 4.540831 0.600213 3.153307 6.128505 4.454810 1209.16 1355.08 1.000 pinvar{all} 0.099068 0.000342 0.064354 0.136244 0.097965 1180.10 1214.44 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 -11431.863105 Model 2: PositiveSelection -11431.863109 Model 0: one-ratio -11466.23619 Model 3: discrete -11281.208712 Model 7: beta -11283.169663 Model 8: beta&w>1 -11283.172841 Model 0 vs 1 68.74616999999853 Model 2 vs 1 7.99999907030724E-6 Model 8 vs 7 0.0063559999980498105