--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Wed Oct 31 18:42:30 GMT 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=/usr/bin/
input.sequences=
mrbayes.pburnin=2500
mrbayes.bin=mb
tcoffee.bin=t_coffee
mrbayes.dir=/opt/mrbayes_3.2.2/src
tcoffee.dir=
tcoffee.minScore=3
input.fasta=/data/res/NS3_3/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/data/res/NS3_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/data/res/NS3_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 /data/res/NS3_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -15815.71 -15853.66
2 -15814.62 -15855.37
--------------------------------------
TOTAL -15815.03 -15854.84
--------------------------------------
Model parameter summaries over the runs sampled in files
"/data/res/NS3_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/data/res/NS3_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 "/data/res/NS3_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} 8.425141 0.164025 7.646933 9.207501 8.407531 744.39 766.30 1.000
r(A<->C){all} 0.035718 0.000015 0.028219 0.042966 0.035718 775.16 884.98 1.002
r(A<->G){all} 0.208060 0.000141 0.184214 0.229804 0.207683 459.68 463.36 1.000
r(A<->T){all} 0.046224 0.000020 0.037373 0.054740 0.046128 861.91 913.43 1.000
r(C<->G){all} 0.016924 0.000014 0.009826 0.024158 0.016635 712.55 809.21 1.000
r(C<->T){all} 0.669157 0.000206 0.641284 0.697423 0.669348 436.61 437.49 1.001
r(G<->T){all} 0.023917 0.000020 0.015102 0.031945 0.023817 837.42 933.67 1.000
pi(A){all} 0.359492 0.000061 0.343688 0.373944 0.359424 740.06 758.48 1.004
pi(C){all} 0.215378 0.000041 0.202210 0.227201 0.215382 588.08 703.51 1.002
pi(G){all} 0.227439 0.000049 0.214465 0.241555 0.227227 597.84 675.02 1.000
pi(T){all} 0.197692 0.000037 0.186675 0.210501 0.197489 625.72 675.81 1.000
alpha{1,2} 0.158258 0.000043 0.146023 0.171408 0.157968 1366.34 1404.87 1.001
alpha{3} 5.668751 0.757254 4.170135 7.526887 5.599271 1501.00 1501.00 1.000
pinvar{all} 0.109799 0.000284 0.078068 0.142634 0.109073 985.58 1164.35 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 -15117.740977
Model 2: PositiveSelection -15117.740977
Model 0: one-ratio -15166.398429
Model 3: discrete -14954.584833
Model 7: beta -14953.852837
Model 8: beta&w>1 -14953.797678
Model 0 vs 1 97.31490400000257
Model 2 vs 1 0.0
Model 8 vs 7 0.11031799999909708
