--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Thu Jun 07 20:01:12 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_A2/NS4B_3/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS1/DNG_A2/NS4B_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A2/NS4B_3/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_A2/NS4B_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -7742.29 -7788.73 2 -7741.85 -7784.54 -------------------------------------- TOTAL -7742.04 -7788.06 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS1/DNG_A2/NS4B_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A2/NS4B_3/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_A2/NS4B_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 8.021583 0.219675 7.116084 8.947767 8.023963 917.04 1015.19 1.000 r(A<->C){all} 0.032591 0.000028 0.022289 0.043030 0.032328 572.86 758.11 1.000 r(A<->G){all} 0.240391 0.000341 0.206115 0.279873 0.240201 405.58 443.68 1.000 r(A<->T){all} 0.055773 0.000049 0.042826 0.069782 0.055303 814.31 901.33 1.000 r(C<->G){all} 0.021403 0.000032 0.010063 0.031876 0.021164 825.21 861.01 1.000 r(C<->T){all} 0.604692 0.000482 0.559999 0.647217 0.604738 433.18 456.09 1.000 r(G<->T){all} 0.045149 0.000058 0.029885 0.059436 0.044863 803.00 830.90 1.000 pi(A){all} 0.341047 0.000144 0.315999 0.362375 0.341513 862.15 864.67 1.000 pi(C){all} 0.237030 0.000106 0.217276 0.256439 0.236859 674.69 726.25 1.000 pi(G){all} 0.211900 0.000104 0.190293 0.230706 0.211654 663.87 692.94 1.000 pi(T){all} 0.210022 0.000090 0.190071 0.226591 0.209886 686.60 702.70 1.000 alpha{1,2} 0.220068 0.000205 0.192477 0.249390 0.219273 1060.11 1213.98 1.000 alpha{3} 5.719110 1.052021 3.861267 7.748860 5.616145 1152.32 1229.67 1.000 pinvar{all} 0.136102 0.000664 0.087277 0.187880 0.135159 819.56 1035.85 1.001 ------------------------------------------------------------------------------------------------------ * 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 -7296.264532 Model 2: PositiveSelection -7296.264537 Model 0: one-ratio -7314.39087 Model 3: discrete -7217.962161 Model 7: beta -7219.24212 Model 8: beta&w>1 -7219.24457 Model 0 vs 1 36.252676000000065 Model 2 vs 1 9.999999747378752E-6 Model 8 vs 7 0.004899999999906868