--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Fri Nov 18 01:42:48 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/105/CG30271-PF/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS/105/CG30271-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/105/CG30271-PF/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/105/CG30271-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -2826.99 -2847.09 2 -2826.03 -2842.39 -------------------------------------- TOTAL -2826.40 -2846.40 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS/105/CG30271-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/105/CG30271-PF/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/105/CG30271-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 1.006019 0.009274 0.811624 1.189277 1.004319 1476.02 1488.51 1.000 r(A<->C){all} 0.092153 0.000337 0.058252 0.128712 0.091092 747.90 922.65 1.000 r(A<->G){all} 0.171612 0.000763 0.117693 0.224880 0.170491 950.21 980.21 1.000 r(A<->T){all} 0.081748 0.000654 0.036707 0.131569 0.079744 703.63 764.41 1.000 r(C<->G){all} 0.071254 0.000184 0.047356 0.099208 0.070426 1128.76 1201.33 1.000 r(C<->T){all} 0.488384 0.001558 0.416168 0.567437 0.487702 911.30 941.30 1.001 r(G<->T){all} 0.094849 0.000416 0.055688 0.134208 0.093584 937.17 1033.83 1.000 pi(A){all} 0.240563 0.000217 0.210593 0.268612 0.239931 1046.64 1090.30 1.001 pi(C){all} 0.290600 0.000209 0.263007 0.318780 0.290298 1040.86 1182.79 1.000 pi(G){all} 0.306362 0.000230 0.277052 0.336315 0.306136 1366.16 1408.92 1.001 pi(T){all} 0.162474 0.000132 0.139735 0.183871 0.162357 976.29 1075.19 1.000 alpha{1,2} 0.140327 0.000315 0.108565 0.175374 0.138831 1060.91 1237.79 1.000 alpha{3} 2.913458 0.694472 1.573391 4.715483 2.786817 1140.77 1213.26 1.000 pinvar{all} 0.422813 0.001944 0.330373 0.505072 0.423858 1324.85 1382.94 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 -2660.988257 Model 2: PositiveSelection -2660.988257 Model 0: one-ratio -2666.629381 Model 3: discrete -2644.34978 Model 7: beta -2645.430377 Model 8: beta&w>1 -2645.431403 Model 0 vs 1 11.282248000000436 Model 2 vs 1 0.0 Model 8 vs 7 0.002051999999821419