--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Sat May 05 01:01:32 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/NS1_2/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS1/DNG_N1/NS1_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/NS1_2/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/NS1_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -8960.72 -9009.30 2 -8961.78 -9005.03 -------------------------------------- TOTAL -8961.12 -9008.62 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS1/DNG_N1/NS1_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/NS1_2/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/NS1_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 7.792185 0.242197 6.835030 8.748124 7.762955 654.84 673.73 1.001 r(A<->C){all} 0.028577 0.000026 0.019043 0.038783 0.028395 843.15 871.76 1.002 r(A<->G){all} 0.220253 0.000252 0.189133 0.250636 0.220001 519.42 590.57 1.005 r(A<->T){all} 0.055693 0.000047 0.042811 0.069122 0.055311 889.61 894.53 1.000 r(C<->G){all} 0.023263 0.000032 0.013112 0.034483 0.022897 775.46 831.79 1.000 r(C<->T){all} 0.653654 0.000349 0.619141 0.690421 0.653577 561.56 585.36 1.007 r(G<->T){all} 0.018559 0.000036 0.007671 0.031189 0.018216 630.06 766.25 1.000 pi(A){all} 0.344289 0.000107 0.325413 0.365957 0.344333 800.27 880.45 1.001 pi(C){all} 0.231198 0.000075 0.214244 0.248421 0.231148 504.15 741.29 1.003 pi(G){all} 0.230409 0.000083 0.212691 0.247952 0.230384 845.47 853.79 1.000 pi(T){all} 0.194104 0.000061 0.179200 0.209735 0.194227 845.09 964.00 1.000 alpha{1,2} 0.193714 0.000138 0.170727 0.216343 0.193170 1230.80 1242.77 1.000 alpha{3} 5.803558 1.152172 3.896037 7.963801 5.661483 1501.00 1501.00 1.000 pinvar{all} 0.135554 0.000507 0.093593 0.180917 0.134787 987.77 1152.01 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 -8598.078479 Model 2: PositiveSelection -8598.078479 Model 0: one-ratio -8702.716886 Model 3: discrete -8491.209645 Model 7: beta -8494.419795 Model 8: beta&w>1 -8492.756939 Model 0 vs 1 209.27681400000074 Model 2 vs 1 0.0 Model 8 vs 7 3.3257119999980205