--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Fri Dec 09 15:39:34 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/442/Zasp52-PO/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS/442/Zasp52-PO/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/442/Zasp52-PO/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/442/Zasp52-PO/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -4429.58 -4446.88 2 -4429.19 -4449.46 -------------------------------------- TOTAL -4429.36 -4448.84 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS/442/Zasp52-PO/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/442/Zasp52-PO/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/442/Zasp52-PO/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 0.490928 0.001681 0.409289 0.569859 0.488647 1397.38 1449.19 1.003 r(A<->C){all} 0.072325 0.000206 0.046225 0.101196 0.071326 1086.98 1141.22 1.000 r(A<->G){all} 0.191314 0.000709 0.139169 0.243381 0.189736 757.98 862.99 1.000 r(A<->T){all} 0.121191 0.000679 0.073249 0.173290 0.120338 863.77 932.14 1.000 r(C<->G){all} 0.081162 0.000180 0.055855 0.106875 0.080233 1149.07 1164.61 1.002 r(C<->T){all} 0.402499 0.001215 0.339444 0.470477 0.401971 688.38 823.29 1.000 r(G<->T){all} 0.131508 0.000509 0.088987 0.176486 0.130156 1093.12 1186.95 1.000 pi(A){all} 0.228705 0.000102 0.209213 0.248199 0.228809 965.54 987.69 1.000 pi(C){all} 0.330878 0.000128 0.309456 0.353419 0.330651 1018.58 1057.53 1.000 pi(G){all} 0.281176 0.000115 0.259907 0.301322 0.281029 1228.47 1288.53 1.000 pi(T){all} 0.159241 0.000075 0.142489 0.175641 0.159172 1153.99 1179.67 1.000 alpha{1,2} 0.172634 0.001204 0.105866 0.240656 0.169998 922.04 1051.61 1.000 alpha{3} 2.342114 0.643136 1.048182 3.976436 2.218016 1229.01 1299.43 1.000 pinvar{all} 0.533031 0.002406 0.439992 0.626908 0.535956 1147.23 1177.07 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 -3834.780134 Model 2: PositiveSelection -3834.780134 Model 0: one-ratio -3904.716809 Model 3: discrete -3833.849026 Model 7: beta -3835.214203 Model 8: beta&w>1 -3833.846982 Model 0 vs 1 139.87334999999985 Model 2 vs 1 0.0 Model 8 vs 7 2.7344419999999445