--- EXPERIMENT NOTES --- EXPERIMENT PROPERTIES #Tue Nov 22 07:19:46 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/3/acj6-PC/input.fasta input.names= mrbayes.params= codeml.params= --- PSRF SUMMARY Estimated marginal likelihoods for runs sampled in files "/opt/ADOPS/3/acj6-PC/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PC/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/3/acj6-PC/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat) Run Arithmetic mean Harmonic mean -------------------------------------- 1 -2154.58 -2173.84 2 -2154.94 -2172.30 -------------------------------------- TOTAL -2154.74 -2173.34 -------------------------------------- Model parameter summaries over the runs sampled in files "/opt/ADOPS/3/acj6-PC/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PC/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/3/acj6-PC/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat". 95% HPD Interval -------------------- Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+ ------------------------------------------------------------------------------------------------------ TL{all} 0.320561 0.003053 0.225983 0.431234 0.315063 1315.09 1408.05 1.000 r(A<->C){all} 0.079721 0.001015 0.018683 0.140059 0.076956 649.86 800.98 1.000 r(A<->G){all} 0.249985 0.003618 0.136891 0.367246 0.245688 431.54 465.39 1.000 r(A<->T){all} 0.173637 0.002758 0.078937 0.277183 0.169730 656.21 722.00 1.002 r(C<->G){all} 0.060758 0.000423 0.023813 0.101911 0.058704 615.91 792.67 1.002 r(C<->T){all} 0.424995 0.005111 0.284470 0.564363 0.421768 497.23 555.73 1.000 r(G<->T){all} 0.010904 0.000112 0.000004 0.031795 0.007700 969.00 1019.26 1.000 pi(A){all} 0.242957 0.000159 0.218109 0.267401 0.242974 1188.26 1263.76 1.000 pi(C){all} 0.306022 0.000171 0.281861 0.332303 0.305910 1313.42 1326.12 1.000 pi(G){all} 0.266052 0.000164 0.238660 0.288888 0.266027 1122.22 1132.10 1.000 pi(T){all} 0.184969 0.000121 0.164492 0.207358 0.184582 1225.57 1282.61 1.000 alpha{1,2} 0.054758 0.000876 0.000207 0.099587 0.057599 1067.17 1103.49 1.000 alpha{3} 2.202812 0.610383 0.859386 3.717442 2.091979 1296.74 1394.97 1.000 pinvar{all} 0.778867 0.000699 0.725369 0.826849 0.780434 1271.98 1386.49 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 -2066.605014 Model 2: PositiveSelection -2066.501271 Model 0: one-ratio -2071.635569 Model 3: discrete -2066.501271 Model 7: beta -2069.328302 Model 8: beta&w>1 -2066.500897 Model 0 vs 1 10.061109999999644 Model 2 vs 1 0.20748600000024453 Model 8 vs 7 5.654809999999998