--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Tue Nov 22 08:02:25 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/3/acj6-PF/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/3/acj6-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PF/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/3/acj6-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -2257.37 -2282.05
2 -2256.73 -2276.21
--------------------------------------
TOTAL -2257.00 -2281.36
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS/3/acj6-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PF/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/3/acj6-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 0.389200 0.003138 0.287301 0.504191 0.384507 1405.33 1436.15 1.000
r(A<->C){all} 0.119417 0.001146 0.058768 0.190841 0.116106 795.84 908.99 1.000
r(A<->G){all} 0.251567 0.002956 0.147983 0.358029 0.247817 589.66 640.17 1.000
r(A<->T){all} 0.110706 0.001484 0.041559 0.187782 0.107179 757.43 817.06 1.000
r(C<->G){all} 0.065725 0.000407 0.030332 0.107925 0.063458 960.11 1006.60 1.000
r(C<->T){all} 0.441390 0.003780 0.323123 0.560051 0.441287 591.01 664.37 1.000
r(G<->T){all} 0.011194 0.000107 0.000002 0.031442 0.008227 921.04 1021.98 1.000
pi(A){all} 0.238453 0.000149 0.215876 0.262784 0.238250 986.36 1149.22 1.000
pi(C){all} 0.308173 0.000175 0.283215 0.334078 0.307752 1207.76 1210.44 1.000
pi(G){all} 0.270920 0.000168 0.245455 0.296428 0.271094 1117.11 1203.63 1.000
pi(T){all} 0.182454 0.000122 0.162519 0.205410 0.182323 1156.83 1195.96 1.000
alpha{1,2} 0.046441 0.000670 0.000113 0.086602 0.048221 1266.70 1325.95 1.000
alpha{3} 2.457735 0.659965 1.069537 4.040857 2.338692 1396.56 1448.78 1.000
pinvar{all} 0.746451 0.000694 0.695172 0.797072 0.747761 1101.42 1284.18 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 -2158.388457
Model 2: PositiveSelection -2158.385862
Model 0: one-ratio -2158.434656
Model 3: discrete -2158.385862
Model 7: beta -2158.385379
Model 8: beta&w>1 -2158.387974
Model 0 vs 1 0.09239799999977549
Model 2 vs 1 0.005189999999856809
Model 8 vs 7 0.005190000000766304
