--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Sat Nov 12 09:13:54 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/2/ab-PF/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/2/ab-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/ab-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/2/ab-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -9550.27         -9564.63
2      -9550.33         -9564.45
--------------------------------------
TOTAL    -9550.30         -9564.54
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/2/ab-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/ab-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/2/ab-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}         1.133528    0.003943    1.007033    1.253249    1.130120   1036.72   1268.86    1.000
r(A<->C){all}   0.075632    0.000092    0.055634    0.093940    0.075321    891.53    954.66    1.002
r(A<->G){all}   0.180961    0.000282    0.148015    0.212395    0.180614    706.56    715.16    1.001
r(A<->T){all}   0.134029    0.000322    0.101443    0.168899    0.133404   1041.34   1061.22    1.001
r(C<->G){all}   0.043106    0.000031    0.032758    0.054420    0.042920   1074.55   1192.05    1.000
r(C<->T){all}   0.523047    0.000623    0.472464    0.569185    0.522883    683.67    763.48    1.000
r(G<->T){all}   0.043225    0.000088    0.025319    0.061649    0.042579    975.43   1048.50    1.000
pi(A){all}      0.229296    0.000056    0.215766    0.244921    0.229113    990.22   1013.60    1.001
pi(C){all}      0.340521    0.000067    0.325138    0.357279    0.340520    787.90    877.71    1.000
pi(G){all}      0.288791    0.000062    0.274011    0.304492    0.288829    879.53   1100.25    1.000
pi(T){all}      0.141392    0.000033    0.130235    0.152459    0.141373    971.13   1073.46    1.003
alpha{1,2}      0.142276    0.000113    0.122072    0.163455    0.141735   1484.13   1489.55    1.000
alpha{3}        3.176430    0.490580    1.982382    4.609904    3.081406   1363.07   1411.36    1.001
pinvar{all}     0.339863    0.000788    0.282087    0.391741    0.340916   1305.30   1388.63    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	-8333.050317
Model 2: PositiveSelection	-8333.050317
Model 0: one-ratio	-8403.270713
Model 3: discrete	-8304.51193
Model 7: beta	-8304.768658
Model 8: beta&w>1	-8304.770348


Model 0 vs 1	140.44079200000124

Model 2 vs 1	0.0

Model 8 vs 7	0.0033799999982875306