--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Mon Nov 21 14:18:48 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/295/Lmpt-PE/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS/295/Lmpt-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/295/Lmpt-PE/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/295/Lmpt-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -1995.72 -2013.26 2 -1995.78 -2016.93 -------------------------------------- TOTAL -1995.75 -2016.26 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS/295/Lmpt-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/295/Lmpt-PE/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/295/Lmpt-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 0.834238 0.012002 0.627644 1.051122 0.825141 1419.53 1460.27 1.000 r(A<->C){all} 0.080960 0.000492 0.040268 0.124935 0.079031 985.86 1102.20 1.000 r(A<->G){all} 0.220212 0.002522 0.128312 0.320676 0.217389 539.12 646.88 1.000 r(A<->T){all} 0.010102 0.000094 0.000005 0.029522 0.007173 761.61 928.49 1.000 r(C<->G){all} 0.097730 0.000470 0.057155 0.140022 0.095920 773.93 880.28 1.000 r(C<->T){all} 0.562495 0.003140 0.458799 0.674044 0.562662 660.73 706.22 1.000 r(G<->T){all} 0.028500 0.000275 0.000097 0.060267 0.026316 950.92 1043.47 1.000 pi(A){all} 0.225826 0.000218 0.196206 0.253302 0.225463 1225.58 1339.66 1.000 pi(C){all} 0.311483 0.000271 0.278221 0.341525 0.311604 1254.54 1261.33 1.000 pi(G){all} 0.275871 0.000265 0.245869 0.308100 0.275597 1196.40 1260.28 1.000 pi(T){all} 0.186820 0.000176 0.162500 0.214740 0.186298 1094.16 1181.14 1.000 alpha{1,2} 0.085992 0.000275 0.057500 0.119788 0.085828 877.60 1024.87 1.000 alpha{3} 2.224142 0.488473 1.061426 3.648855 2.111553 1329.23 1376.04 1.000 pinvar{all} 0.600376 0.001409 0.527285 0.669611 0.602321 1256.34 1372.71 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 -1818.876561 Model 2: PositiveSelection -1818.874206 Model 0: one-ratio -1818.988065 Model 3: discrete -1818.874206 Model 7: beta -1818.873078 Model 8: beta&w>1 -1818.875434 Model 0 vs 1 0.22300799999993615 Model 2 vs 1 0.004710000000159198 Model 8 vs 7 0.004711999999926775