--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Fri Jun 01 07:20:21 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_A1/NS2A_5/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_A1/NS2A_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2A_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_A1/NS2A_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -7391.36 -7444.92
2 -7393.31 -7441.87
--------------------------------------
TOTAL -7391.92 -7444.27
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_A1/NS2A_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2A_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_A1/NS2A_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} 10.092795 0.466861 8.857748 11.514970 10.058750 676.48 731.57 1.000
r(A<->C){all} 0.046659 0.000058 0.031963 0.061885 0.046258 825.20 867.61 1.000
r(A<->G){all} 0.228051 0.000301 0.194344 0.260659 0.227717 457.60 568.59 1.000
r(A<->T){all} 0.045640 0.000052 0.031698 0.059737 0.045287 1034.50 1049.66 1.000
r(C<->G){all} 0.037415 0.000066 0.022376 0.053791 0.036915 579.10 704.21 1.000
r(C<->T){all} 0.606023 0.000440 0.564136 0.645262 0.606800 451.28 497.98 1.000
r(G<->T){all} 0.036212 0.000056 0.021875 0.050760 0.035860 758.44 833.82 1.000
pi(A){all} 0.301692 0.000122 0.279473 0.321937 0.301745 868.78 940.30 1.000
pi(C){all} 0.212124 0.000087 0.194092 0.230541 0.211868 941.71 1032.97 1.000
pi(G){all} 0.242848 0.000112 0.222660 0.263985 0.242578 814.21 896.14 1.000
pi(T){all} 0.243336 0.000107 0.224331 0.264536 0.242842 765.34 811.38 1.000
alpha{1,2} 0.415355 0.001774 0.337494 0.497128 0.412209 1179.98 1219.19 1.000
alpha{3} 4.134863 0.776552 2.612240 5.941047 4.029413 1423.41 1462.21 1.002
pinvar{all} 0.031415 0.000387 0.000176 0.068366 0.028327 1242.62 1244.27 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 -7174.131738
Model 2: PositiveSelection -7174.131738
Model 0: one-ratio -7187.8418
Model 3: discrete -7114.553347
Model 7: beta -7114.79184
Model 8: beta&w>1 -7114.793006
Model 0 vs 1 27.420124000000214
Model 2 vs 1 0.0
Model 8 vs 7 0.0023320000000239816
