--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Tue Nov 22 09:10:38 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-PK/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/3/acj6-PK/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PK/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-PK/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -2163.86 -2186.31
2 -2163.89 -2181.96
--------------------------------------
TOTAL -2163.87 -2185.63
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS/3/acj6-PK/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PK/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-PK/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 0.318417 0.002898 0.218979 0.430674 0.314010 930.08 1177.20 1.000
r(A<->C){all} 0.081192 0.001023 0.022479 0.144900 0.078688 830.83 922.44 1.000
r(A<->G){all} 0.248432 0.003789 0.129989 0.368342 0.244852 556.63 588.20 1.000
r(A<->T){all} 0.172115 0.002893 0.068418 0.274738 0.167887 699.42 725.58 1.000
r(C<->G){all} 0.059393 0.000415 0.024329 0.100519 0.057335 858.53 924.07 1.000
r(C<->T){all} 0.427720 0.004985 0.284940 0.555764 0.424244 603.37 717.45 1.000
r(G<->T){all} 0.011147 0.000131 0.000001 0.034025 0.007483 926.03 964.16 1.000
pi(A){all} 0.245664 0.000165 0.219292 0.269241 0.245351 1250.58 1269.21 1.000
pi(C){all} 0.304552 0.000180 0.279486 0.332543 0.304513 1274.57 1278.42 1.000
pi(G){all} 0.265579 0.000166 0.242424 0.292737 0.264790 1069.89 1128.59 1.001
pi(T){all} 0.184205 0.000120 0.163615 0.206490 0.184061 1193.97 1214.33 1.000
alpha{1,2} 0.054752 0.000851 0.000102 0.099357 0.058052 975.17 1105.37 1.000
alpha{3} 2.197450 0.588341 0.947330 3.794628 2.084394 1163.78 1300.12 1.000
pinvar{all} 0.779979 0.000660 0.730139 0.830188 0.781148 1293.69 1367.36 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 -2076.210814
Model 2: PositiveSelection -2076.10255
Model 0: one-ratio -2081.247505
Model 3: discrete -2076.10255
Model 7: beta -2078.940124
Model 8: beta&w>1 -2076.102176
Model 0 vs 1 10.073381999999583
Model 2 vs 1 0.21652799999992567
Model 8 vs 7 5.675896000000648
