--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Mon May 14 04:45:16 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_2/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N2/NS3_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS3_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_N2/NS3_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -13660.45 -13709.89
2 -13659.66 -13707.72
--------------------------------------
TOTAL -13659.98 -13709.30
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N2/NS3_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS3_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_N2/NS3_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} 8.208054 0.192982 7.331877 9.055399 8.201090 545.44 621.96 1.000
r(A<->C){all} 0.041376 0.000023 0.032026 0.050851 0.041246 720.60 762.69 1.000
r(A<->G){all} 0.199229 0.000166 0.173687 0.223705 0.199033 501.24 515.46 1.000
r(A<->T){all} 0.040360 0.000025 0.030094 0.049865 0.040282 807.72 948.64 1.000
r(C<->G){all} 0.018738 0.000016 0.011174 0.026508 0.018515 654.23 741.66 1.000
r(C<->T){all} 0.680316 0.000250 0.649589 0.710048 0.680416 496.25 499.12 1.000
r(G<->T){all} 0.019980 0.000022 0.011280 0.029092 0.019600 381.76 558.85 1.000
pi(A){all} 0.360903 0.000064 0.345622 0.376486 0.360786 813.98 856.58 1.000
pi(C){all} 0.214613 0.000040 0.201741 0.226515 0.214405 715.01 780.53 1.000
pi(G){all} 0.228870 0.000051 0.215093 0.242714 0.228588 692.03 715.18 1.000
pi(T){all} 0.195614 0.000038 0.183886 0.207695 0.195619 701.24 759.73 1.000
alpha{1,2} 0.148950 0.000041 0.136246 0.161397 0.148650 1178.44 1225.41 1.000
alpha{3} 4.958261 0.643753 3.520045 6.583744 4.871818 1261.19 1381.09 1.000
pinvar{all} 0.109642 0.000281 0.077311 0.142453 0.109399 1200.24 1310.82 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 -11982.516811
Model 2: PositiveSelection -11982.516812
Model 0: one-ratio -12004.177286
Model 3: discrete -11843.511779
Model 7: beta -11843.965992
Model 8: beta&w>1 -11843.969252
Model 0 vs 1 43.320950000001176
Model 2 vs 1 2.0000006770715117E-6
Model 8 vs 7 0.006520000002637971
