--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Mon Jun 04 22:33:54 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_A2/NS3_5/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_A2/NS3_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A2/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_A2/NS3_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1     -15522.16        -15559.11
2     -15523.27        -15570.95
--------------------------------------
TOTAL   -15522.57        -15570.25
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_A2/NS3_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A2/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_A2/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}         8.724531    0.197061    7.852793    9.601222    8.712668    539.12    604.99    1.000
r(A<->C){all}   0.040531    0.000017    0.032142    0.048503    0.040402    599.40    705.49    1.001
r(A<->G){all}   0.212984    0.000147    0.189680    0.236200    0.212776    348.54    418.79    1.000
r(A<->T){all}   0.037396    0.000017    0.029599    0.045546    0.037411    747.77    779.13    1.001
r(C<->G){all}   0.016536    0.000012    0.009917    0.023272    0.016433    551.63    640.04    1.000
r(C<->T){all}   0.670936    0.000216    0.639848    0.696972    0.671376    352.95    439.71    1.000
r(G<->T){all}   0.021618    0.000016    0.014314    0.030010    0.021397    581.58    737.01    1.001
pi(A){all}      0.361480    0.000061    0.345417    0.376049    0.361628    641.81    708.02    1.001
pi(C){all}      0.218330    0.000041    0.205633    0.230285    0.218313    510.69    610.71    1.000
pi(G){all}      0.227567    0.000047    0.214083    0.240662    0.227555    837.53    870.23    1.000
pi(T){all}      0.192624    0.000034    0.181050    0.203796    0.192520    539.74    685.30    1.000
alpha{1,2}      0.154746    0.000039    0.142567    0.166845    0.154636   1042.77   1156.56    1.000
alpha{3}        6.326211    0.946948    4.517757    8.258949    6.238575   1204.80   1280.47    1.000
pinvar{all}     0.118206    0.000286    0.084584    0.148529    0.117533    756.19   1093.92    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	-14861.852585
Model 2: PositiveSelection	-14861.852599
Model 0: one-ratio	-14896.788378
Model 3: discrete	-14697.765167
Model 7: beta	-14701.481299
Model 8: beta&w>1	-14701.484465


Model 0 vs 1	69.87158599999748

Model 2 vs 1	2.7999998565064743E-5

Model 8 vs 7	0.006332000000838889