--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Mon May 07 05:25:36 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_N1/NS1_5/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N1/NS1_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/NS1_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_N1/NS1_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -8323.17         -8367.37
2      -8321.88         -8358.12
--------------------------------------
TOTAL    -8322.33         -8366.68
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N1/NS1_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/NS1_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_N1/NS1_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}         6.875514    0.199414    5.987630    7.715891    6.848400    844.92    861.34    1.000
r(A<->C){all}   0.034880    0.000037    0.023991    0.047127    0.034626    853.66    868.72    1.000
r(A<->G){all}   0.220645    0.000276    0.188579    0.253584    0.220073    547.05    640.91    1.001
r(A<->T){all}   0.057913    0.000057    0.044553    0.073573    0.057600    830.47    853.13    1.000
r(C<->G){all}   0.034923    0.000051    0.021411    0.048708    0.034654    582.01    641.48    1.000
r(C<->T){all}   0.625892    0.000424    0.584300    0.665766    0.626134    554.34    607.21    1.002
r(G<->T){all}   0.025747    0.000052    0.011637    0.039265    0.025486    717.69    852.47    1.001
pi(A){all}      0.347555    0.000115    0.327626    0.369497    0.347666    967.98    991.60    1.000
pi(C){all}      0.227782    0.000080    0.210055    0.245008    0.227707    837.02    902.20    1.000
pi(G){all}      0.227277    0.000092    0.208586    0.246876    0.227177    639.13    743.80    1.000
pi(T){all}      0.197386    0.000063    0.182631    0.213383    0.197413    952.69   1028.54    1.000
alpha{1,2}      0.199346    0.000167    0.174935    0.224723    0.198747   1086.90   1163.98    1.001
alpha{3}        3.853339    0.530644    2.585800    5.341212    3.768005   1229.48   1365.24    1.002
pinvar{all}     0.119924    0.000515    0.076245    0.165723    0.119508   1033.46   1034.10    1.002
------------------------------------------------------------------------------------------------------
* 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	-7798.012387
Model 2: PositiveSelection	-7798.012387
Model 0: one-ratio	-7877.409743
Model 3: discrete	-7713.753213
Model 7: beta	-7716.017746
Model 8: beta&w>1	-7716.02078


Model 0 vs 1	158.7947120000008

Model 2 vs 1	0.0

Model 8 vs 7	0.006067999998776941