--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Fri May 18 23:09:54 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_N2/NS3_5/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N2/NS3_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/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_N2/NS3_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -13110.61 -13150.77
2 -13106.58 -13152.08
--------------------------------------
TOTAL -13107.25 -13151.62
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N2/NS3_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/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_N2/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} 7.632280 0.194270 6.811406 8.507804 7.616064 379.71 510.03 1.000
r(A<->C){all} 0.041190 0.000025 0.031484 0.050798 0.041033 679.13 693.13 1.000
r(A<->G){all} 0.203134 0.000163 0.178955 0.228315 0.202445 610.99 617.85 1.000
r(A<->T){all} 0.041084 0.000027 0.031055 0.051736 0.041050 863.69 964.80 1.000
r(C<->G){all} 0.023046 0.000021 0.014347 0.032114 0.022838 867.74 900.39 1.001
r(C<->T){all} 0.670354 0.000244 0.638729 0.699630 0.670671 561.21 572.88 1.000
r(G<->T){all} 0.021193 0.000023 0.012484 0.031330 0.020995 707.93 791.05 1.000
pi(A){all} 0.354954 0.000063 0.339704 0.370710 0.354906 720.18 779.65 1.000
pi(C){all} 0.213999 0.000042 0.201380 0.226726 0.214192 782.30 845.15 1.000
pi(G){all} 0.232255 0.000053 0.217051 0.245360 0.232326 773.40 899.83 1.000
pi(T){all} 0.198792 0.000038 0.186191 0.210047 0.198683 729.90 752.82 1.000
alpha{1,2} 0.151181 0.000042 0.139032 0.164209 0.150781 1083.28 1195.60 1.000
alpha{3} 4.996657 0.683331 3.521265 6.639369 4.910608 1123.51 1255.35 1.000
pinvar{all} 0.118681 0.000313 0.085609 0.154113 0.118331 1033.98 1263.92 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 -11713.095707
Model 2: PositiveSelection -11713.095707
Model 0: one-ratio -11738.883263
Model 3: discrete -11578.064193
Model 7: beta -11579.774705
Model 8: beta&w>1 -11579.77772
Model 0 vs 1 51.575111999998626
Model 2 vs 1 0.0
Model 8 vs 7 0.006030000000464497
