--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Wed Jun 06 20:23:35 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/NS4B_1/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_A2/NS4B_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A2/NS4B_1/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/NS4B_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -6813.63         -6854.34
2      -6814.03         -6858.11
--------------------------------------
TOTAL    -6813.81         -6857.44
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_A2/NS4B_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A2/NS4B_1/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/NS4B_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         7.406155    0.243577    6.470552    8.389127    7.378601    521.14    675.08    1.000
r(A<->C){all}   0.042358    0.000044    0.029682    0.055713    0.042102   1040.30   1055.44    1.000
r(A<->G){all}   0.229109    0.000409    0.191687    0.270525    0.228701    447.46    529.61    1.003
r(A<->T){all}   0.045964    0.000052    0.032257    0.060238    0.045681    844.06    936.99    1.000
r(C<->G){all}   0.024975    0.000044    0.011133    0.036755    0.024607    755.26    794.70    1.000
r(C<->T){all}   0.628463    0.000580    0.581426    0.676041    0.628574    433.06    496.66    1.003
r(G<->T){all}   0.029131    0.000049    0.016381    0.042947    0.028720    750.37    853.79    1.000
pi(A){all}      0.323952    0.000145    0.299246    0.346210    0.323831    917.93    966.09    1.000
pi(C){all}      0.234900    0.000113    0.214989    0.256182    0.234884    738.83    772.44    1.000
pi(G){all}      0.216447    0.000117    0.195068    0.237067    0.216488    710.28    739.26    1.001
pi(T){all}      0.224702    0.000104    0.205971    0.245145    0.224682    737.50    769.69    1.000
alpha{1,2}      0.219194    0.000223    0.191989    0.249530    0.218263   1108.28   1211.35    1.000
alpha{3}        5.023086    0.928869    3.255558    6.968522    4.913312   1190.47   1300.96    1.000
pinvar{all}     0.141693    0.000803    0.088722    0.196926    0.141794   1018.67   1184.57    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	-6355.813867
Model 2: PositiveSelection	-6355.813867
Model 0: one-ratio	-6396.472561
Model 3: discrete	-6288.205742
Model 7: beta	-6290.690906
Model 8: beta&w>1	-6290.692233


Model 0 vs 1	81.31738799999948

Model 2 vs 1	0.0

Model 8 vs 7	0.0026539999998931307