--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Mon Jun 04 22:33:54 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_A2/NS3_5/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_A2/NS3_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A2/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_A2/NS3_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -15522.16 -15559.11
2 -15523.27 -15570.95
--------------------------------------
TOTAL -15522.57 -15570.25
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_A2/NS3_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A2/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_A2/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} 8.724531 0.197061 7.852793 9.601222 8.712668 539.12 604.99 1.000
r(A<->C){all} 0.040531 0.000017 0.032142 0.048503 0.040402 599.40 705.49 1.001
r(A<->G){all} 0.212984 0.000147 0.189680 0.236200 0.212776 348.54 418.79 1.000
r(A<->T){all} 0.037396 0.000017 0.029599 0.045546 0.037411 747.77 779.13 1.001
r(C<->G){all} 0.016536 0.000012 0.009917 0.023272 0.016433 551.63 640.04 1.000
r(C<->T){all} 0.670936 0.000216 0.639848 0.696972 0.671376 352.95 439.71 1.000
r(G<->T){all} 0.021618 0.000016 0.014314 0.030010 0.021397 581.58 737.01 1.001
pi(A){all} 0.361480 0.000061 0.345417 0.376049 0.361628 641.81 708.02 1.001
pi(C){all} 0.218330 0.000041 0.205633 0.230285 0.218313 510.69 610.71 1.000
pi(G){all} 0.227567 0.000047 0.214083 0.240662 0.227555 837.53 870.23 1.000
pi(T){all} 0.192624 0.000034 0.181050 0.203796 0.192520 539.74 685.30 1.000
alpha{1,2} 0.154746 0.000039 0.142567 0.166845 0.154636 1042.77 1156.56 1.000
alpha{3} 6.326211 0.946948 4.517757 8.258949 6.238575 1204.80 1280.47 1.000
pinvar{all} 0.118206 0.000286 0.084584 0.148529 0.117533 756.19 1093.92 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 -14861.852585
Model 2: PositiveSelection -14861.852599
Model 0: one-ratio -14896.788378
Model 3: discrete -14697.765167
Model 7: beta -14701.481299
Model 8: beta&w>1 -14701.484465
Model 0 vs 1 69.87158599999748
Model 2 vs 1 2.7999998565064743E-5
Model 8 vs 7 0.006332000000838889
