--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Fri Nov 25 14:24:12 WET 2016
codeml.models=0 1 2 3 7 8
mrbayes.mpich=
mrbayes.ngen=1000000
tcoffee.alignMethod=CLUSTALW2
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/ADOPS/1/14-3-3zeta-PG/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/1/14-3-3zeta-PG/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/1/14-3-3zeta-PG/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/ADOPS/1/14-3-3zeta-PG/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -1544.40 -1562.83
2 -1544.56 -1563.72
--------------------------------------
TOTAL -1544.48 -1563.37
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS/1/14-3-3zeta-PG/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/1/14-3-3zeta-PG/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/ADOPS/1/14-3-3zeta-PG/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 0.247920 0.001647 0.176359 0.333366 0.244050 1307.41 1310.60 1.000
r(A<->C){all} 0.111095 0.001122 0.050567 0.178397 0.108058 747.82 835.04 1.000
r(A<->G){all} 0.152878 0.001703 0.077245 0.231470 0.148975 860.48 1021.09 1.000
r(A<->T){all} 0.067466 0.001098 0.012128 0.133970 0.062763 572.59 693.46 1.000
r(C<->G){all} 0.061421 0.000641 0.016602 0.111196 0.058105 899.13 1064.58 1.000
r(C<->T){all} 0.558366 0.004760 0.430141 0.696077 0.558382 677.21 764.72 1.000
r(G<->T){all} 0.048773 0.000730 0.003535 0.099599 0.044829 649.11 806.42 1.001
pi(A){all} 0.285953 0.000252 0.253916 0.314870 0.285640 1172.42 1266.40 1.003
pi(C){all} 0.261314 0.000221 0.232901 0.290045 0.260857 1057.87 1220.34 1.000
pi(G){all} 0.258237 0.000240 0.228554 0.287916 0.258559 1103.28 1177.05 1.001
pi(T){all} 0.194496 0.000198 0.166689 0.220516 0.194117 1180.49 1220.57 1.000
alpha{1,2} 0.056370 0.001757 0.000143 0.134510 0.048662 1226.83 1234.60 1.000
alpha{3} 2.077090 0.654349 0.694719 3.637498 1.954483 1378.89 1439.94 1.000
pinvar{all} 0.475676 0.008692 0.280469 0.634387 0.488729 1139.60 1235.02 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 -1451.36118
Model 2: PositiveSelection -1451.36118
Model 0: one-ratio -1458.92382
Model 3: discrete -1451.332921
Model 7: beta -1451.333746
Model 8: beta&w>1 -1451.361179
Model 0 vs 1 15.125279999999748
Model 2 vs 1 0.0
Model 8 vs 7 0.054865999999947235
