--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Wed May 02 17:24:17 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_4/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS1/DNG_N1/E_4/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_4/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_4/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -12489.33 -12533.25 2 -12489.32 -12532.48 -------------------------------------- TOTAL -12489.33 -12532.94 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS1/DNG_N1/E_4/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_4/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_4/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 9.377637 0.305680 8.304878 10.455240 9.367509 565.09 622.74 1.000 r(A<->C){all} 0.036340 0.000023 0.027334 0.045985 0.036102 770.85 829.97 1.000 r(A<->G){all} 0.180672 0.000145 0.157676 0.204431 0.180585 522.38 560.57 1.000 r(A<->T){all} 0.047690 0.000031 0.037667 0.059188 0.047463 905.86 948.90 1.000 r(C<->G){all} 0.023142 0.000023 0.014076 0.032760 0.022939 716.08 767.06 1.000 r(C<->T){all} 0.690025 0.000241 0.660586 0.720244 0.689863 531.29 567.51 1.000 r(G<->T){all} 0.022130 0.000026 0.012244 0.031987 0.021993 649.60 657.59 1.000 pi(A){all} 0.346094 0.000072 0.330189 0.363424 0.346069 809.37 829.36 1.000 pi(C){all} 0.215244 0.000050 0.202405 0.228932 0.215026 611.90 732.06 1.000 pi(G){all} 0.240989 0.000060 0.226643 0.256652 0.240721 692.51 715.53 1.001 pi(T){all} 0.197673 0.000047 0.182875 0.210126 0.197525 720.06 758.54 1.000 alpha{1,2} 0.193925 0.000100 0.174806 0.213071 0.193206 1092.90 1142.00 1.000 alpha{3} 4.544400 0.618034 3.030927 5.974584 4.464177 1501.00 1501.00 1.000 pinvar{all} 0.077590 0.000282 0.046023 0.110009 0.076672 1277.18 1350.00 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 -11824.173141 Model 2: PositiveSelection -11824.173141 Model 0: one-ratio -11850.499226 Model 3: discrete -11677.499809 Model 7: beta -11678.227011 Model 8: beta&w>1 -11678.230481 Model 0 vs 1 52.652170000001206 Model 2 vs 1 0.0 Model 8 vs 7 0.006939999999303836