--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Tue May 15 04:28:29 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_N2/NS3_3/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N2/NS3_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS3_3/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_N2/NS3_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1     -13963.60        -14007.63
2     -13963.03        -14003.88
--------------------------------------
TOTAL   -13963.27        -14006.96
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N2/NS3_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS3_3/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_N2/NS3_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         7.915451    0.177839    7.112767    8.783453    7.902768    387.72    546.63    1.000
r(A<->C){all}   0.042879    0.000022    0.034653    0.052631    0.042604    846.07    943.55    1.000
r(A<->G){all}   0.211449    0.000165    0.187306    0.237299    0.210895    561.89    582.90    1.001
r(A<->T){all}   0.041145    0.000024    0.031915    0.050937    0.041000    689.87    750.01    1.000
r(C<->G){all}   0.022031    0.000017    0.014158    0.030052    0.021846    706.13    788.67    1.000
r(C<->T){all}   0.659385    0.000253    0.626848    0.688277    0.659577    472.83    480.73    1.001
r(G<->T){all}   0.023110    0.000020    0.014572    0.031634    0.022960    697.32    815.76    1.000
pi(A){all}      0.359954    0.000063    0.344559    0.375825    0.359695    664.77    828.59    1.000
pi(C){all}      0.215894    0.000042    0.204451    0.229312    0.215817    678.14    764.84    1.001
pi(G){all}      0.226913    0.000047    0.213313    0.240387    0.226825    869.05    875.47    1.000
pi(T){all}      0.197238    0.000038    0.185296    0.209494    0.197065    869.95    905.43    1.000
alpha{1,2}      0.150550    0.000044    0.137829    0.163307    0.150116    878.24   1147.95    1.000
alpha{3}        5.257324    0.725829    3.729928    6.970870    5.149294   1223.76   1362.38    1.000
pinvar{all}     0.112892    0.000280    0.081158    0.146115    0.112711   1143.17   1216.98    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	-13155.381783
Model 2: PositiveSelection	-13155.381796
Model 0: one-ratio	-13178.981682
Model 3: discrete	-13005.746284
Model 7: beta	-13007.685573
Model 8: beta&w>1	-13007.690944


Model 0 vs 1	47.19979799999783

Model 2 vs 1	2.599999788799323E-5

Model 8 vs 7	0.010741999998572282