--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Fri May 18 23:09:54 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_N2/NS3_5/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS1/DNG_N2/NS3_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS3_5/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_N2/NS3_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -13110.61 -13150.77 2 -13106.58 -13152.08 -------------------------------------- TOTAL -13107.25 -13151.62 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS1/DNG_N2/NS3_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS3_5/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_N2/NS3_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 7.632280 0.194270 6.811406 8.507804 7.616064 379.71 510.03 1.000 r(A<->C){all} 0.041190 0.000025 0.031484 0.050798 0.041033 679.13 693.13 1.000 r(A<->G){all} 0.203134 0.000163 0.178955 0.228315 0.202445 610.99 617.85 1.000 r(A<->T){all} 0.041084 0.000027 0.031055 0.051736 0.041050 863.69 964.80 1.000 r(C<->G){all} 0.023046 0.000021 0.014347 0.032114 0.022838 867.74 900.39 1.001 r(C<->T){all} 0.670354 0.000244 0.638729 0.699630 0.670671 561.21 572.88 1.000 r(G<->T){all} 0.021193 0.000023 0.012484 0.031330 0.020995 707.93 791.05 1.000 pi(A){all} 0.354954 0.000063 0.339704 0.370710 0.354906 720.18 779.65 1.000 pi(C){all} 0.213999 0.000042 0.201380 0.226726 0.214192 782.30 845.15 1.000 pi(G){all} 0.232255 0.000053 0.217051 0.245360 0.232326 773.40 899.83 1.000 pi(T){all} 0.198792 0.000038 0.186191 0.210047 0.198683 729.90 752.82 1.000 alpha{1,2} 0.151181 0.000042 0.139032 0.164209 0.150781 1083.28 1195.60 1.000 alpha{3} 4.996657 0.683331 3.521265 6.639369 4.910608 1123.51 1255.35 1.000 pinvar{all} 0.118681 0.000313 0.085609 0.154113 0.118331 1033.98 1263.92 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 -11713.095707 Model 2: PositiveSelection -11713.095707 Model 0: one-ratio -11738.883263 Model 3: discrete -11578.064193 Model 7: beta -11579.774705 Model 8: beta&w>1 -11579.77772 Model 0 vs 1 51.575111999998626 Model 2 vs 1 0.0 Model 8 vs 7 0.006030000000464497