--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Tue Nov 22 09:25:22 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-PL/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS/3/acj6-PL/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PL/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-PL/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -2298.26 -2318.69 2 -2298.43 -2317.24 -------------------------------------- TOTAL -2298.34 -2318.21 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS/3/acj6-PL/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PL/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-PL/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 0.385422 0.003186 0.285139 0.501020 0.382228 1256.91 1270.69 1.000 r(A<->C){all} 0.117348 0.001195 0.053253 0.186696 0.114094 777.46 817.87 1.000 r(A<->G){all} 0.260672 0.003090 0.155466 0.363654 0.256585 551.21 719.48 1.000 r(A<->T){all} 0.122000 0.001662 0.051686 0.206977 0.118424 748.03 786.59 1.000 r(C<->G){all} 0.063051 0.000408 0.024987 0.103635 0.060645 843.36 965.71 1.002 r(C<->T){all} 0.426503 0.003724 0.309816 0.545263 0.424279 605.28 705.98 1.000 r(G<->T){all} 0.010426 0.000095 0.000007 0.029784 0.007550 897.68 1025.79 1.001 pi(A){all} 0.239497 0.000153 0.215039 0.262443 0.239234 1234.44 1241.30 1.000 pi(C){all} 0.303275 0.000174 0.278360 0.329285 0.303265 1166.91 1250.79 1.000 pi(G){all} 0.270450 0.000176 0.243600 0.295813 0.270446 1157.68 1165.09 1.000 pi(T){all} 0.186778 0.000117 0.166299 0.208001 0.186486 1178.36 1245.65 1.000 alpha{1,2} 0.046951 0.000666 0.000101 0.087423 0.049117 1057.99 1154.03 1.000 alpha{3} 2.367067 0.660360 1.003678 3.947117 2.222249 1408.85 1454.92 1.000 pinvar{all} 0.765000 0.000593 0.717879 0.811266 0.765715 1366.61 1411.82 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 -2207.775789 Model 2: PositiveSelection -2207.773192 Model 0: one-ratio -2207.820215 Model 3: discrete -2207.773192 Model 7: beta -2207.772726 Model 8: beta&w>1 -2207.775324 Model 0 vs 1 0.0888520000007702 Model 2 vs 1 0.005193999999391963 Model 8 vs 7 0.005196000000069034