--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Sun Oct 28 02:28:18 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_1/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/data/res/NS3_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/data/res/NS3_1/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_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -15329.36 -15373.58
2 -15329.73 -15376.38
--------------------------------------
TOTAL -15329.53 -15375.74
--------------------------------------
Model parameter summaries over the runs sampled in files
"/data/res/NS3_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/data/res/NS3_1/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_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 8.410911 0.186214 7.601417 9.274114 8.392660 435.22 483.62 1.000
r(A<->C){all} 0.036342 0.000016 0.028374 0.043874 0.036246 579.71 712.87 1.000
r(A<->G){all} 0.198526 0.000124 0.177482 0.220228 0.198371 349.50 384.85 1.001
r(A<->T){all} 0.039138 0.000018 0.031361 0.047800 0.038980 577.10 669.73 1.000
r(C<->G){all} 0.017187 0.000012 0.010556 0.024205 0.017076 696.07 749.56 1.004
r(C<->T){all} 0.688017 0.000190 0.662993 0.717290 0.688240 274.10 328.56 1.003
r(G<->T){all} 0.020790 0.000016 0.013057 0.028635 0.020591 682.09 744.04 1.000
pi(A){all} 0.360404 0.000060 0.344110 0.374796 0.360246 811.17 820.48 1.001
pi(C){all} 0.215973 0.000041 0.203936 0.228390 0.215955 692.99 717.90 1.000
pi(G){all} 0.229875 0.000046 0.216893 0.243446 0.229823 493.80 672.33 1.000
pi(T){all} 0.193748 0.000034 0.182645 0.204920 0.193843 801.53 802.63 1.001
alpha{1,2} 0.164246 0.000047 0.151390 0.178539 0.163955 1176.86 1242.32 1.000
alpha{3} 6.235556 0.965629 4.525527 8.321555 6.156852 1407.84 1418.31 1.000
pinvar{all} 0.130268 0.000320 0.097066 0.166054 0.129908 1025.20 1086.61 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 -14756.824397
Model 2: PositiveSelection -14756.824397
Model 0: one-ratio -14782.633196
Model 3: discrete -14582.276698
Model 7: beta -14583.680656
Model 8: beta&w>1 -14583.685229
Model 0 vs 1 51.617598000000726
Model 2 vs 1 0.0
Model 8 vs 7 0.009146000000328058
