--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Fri Jun 01 07:20:21 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_A1/NS2A_5/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_A1/NS2A_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2A_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_A1/NS2A_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -7391.36         -7444.92
2      -7393.31         -7441.87
--------------------------------------
TOTAL    -7391.92         -7444.27
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_A1/NS2A_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2A_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_A1/NS2A_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}        10.092795    0.466861    8.857748   11.514970   10.058750    676.48    731.57    1.000
r(A<->C){all}   0.046659    0.000058    0.031963    0.061885    0.046258    825.20    867.61    1.000
r(A<->G){all}   0.228051    0.000301    0.194344    0.260659    0.227717    457.60    568.59    1.000
r(A<->T){all}   0.045640    0.000052    0.031698    0.059737    0.045287   1034.50   1049.66    1.000
r(C<->G){all}   0.037415    0.000066    0.022376    0.053791    0.036915    579.10    704.21    1.000
r(C<->T){all}   0.606023    0.000440    0.564136    0.645262    0.606800    451.28    497.98    1.000
r(G<->T){all}   0.036212    0.000056    0.021875    0.050760    0.035860    758.44    833.82    1.000
pi(A){all}      0.301692    0.000122    0.279473    0.321937    0.301745    868.78    940.30    1.000
pi(C){all}      0.212124    0.000087    0.194092    0.230541    0.211868    941.71   1032.97    1.000
pi(G){all}      0.242848    0.000112    0.222660    0.263985    0.242578    814.21    896.14    1.000
pi(T){all}      0.243336    0.000107    0.224331    0.264536    0.242842    765.34    811.38    1.000
alpha{1,2}      0.415355    0.001774    0.337494    0.497128    0.412209   1179.98   1219.19    1.000
alpha{3}        4.134863    0.776552    2.612240    5.941047    4.029413   1423.41   1462.21    1.002
pinvar{all}     0.031415    0.000387    0.000176    0.068366    0.028327   1242.62   1244.27    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	-7174.131738
Model 2: PositiveSelection	-7174.131738
Model 0: one-ratio	-7187.8418
Model 3: discrete	-7114.553347
Model 7: beta	-7114.79184
Model 8: beta&w>1	-7114.793006


Model 0 vs 1	27.420124000000214

Model 2 vs 1	0.0

Model 8 vs 7	0.0023320000000239816