--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Tue May 08 03:17:50 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/NS2A_1/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N2/NS2A_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS2A_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_N2/NS2A_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -6539.54         -6590.94
2      -6540.57         -6590.81
--------------------------------------
TOTAL    -6539.92         -6590.88
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N2/NS2A_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS2A_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_N2/NS2A_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}        10.035575    0.518817    8.519632   11.338680   10.015700    535.07    620.36    1.001
r(A<->C){all}   0.040833    0.000074    0.023523    0.056574    0.040550    663.78    813.38    1.000
r(A<->G){all}   0.222407    0.000353    0.184121    0.257106    0.222003    559.63    591.89    1.000
r(A<->T){all}   0.056985    0.000077    0.040693    0.074357    0.056421    905.46    958.11    1.000
r(C<->G){all}   0.036676    0.000092    0.019354    0.055617    0.036136    792.54    808.49    1.000
r(C<->T){all}   0.615734    0.000523    0.570136    0.658323    0.616059    601.48    607.36    1.000
r(G<->T){all}   0.027363    0.000073    0.011832    0.044335    0.026995    742.66    750.60    1.000
pi(A){all}      0.300406    0.000123    0.278941    0.322175    0.300265    654.48    710.79    1.000
pi(C){all}      0.215637    0.000096    0.196564    0.234305    0.215464    820.72    917.01    1.002
pi(G){all}      0.243427    0.000109    0.223914    0.264082    0.243305    908.31    961.31    1.000
pi(T){all}      0.240530    0.000102    0.220850    0.261094    0.240318    766.31    794.89    1.000
alpha{1,2}      0.386116    0.001441    0.316925    0.464667    0.382003   1138.86   1146.35    1.001
alpha{3}        3.719204    0.709765    2.195823    5.363157    3.608738    867.77   1135.98    1.001
pinvar{all}     0.028333    0.000365    0.000009    0.065581    0.025310   1187.77   1256.50    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	-6178.818422
Model 2: PositiveSelection	-6178.818422
Model 0: one-ratio	-6184.005542
Model 3: discrete	-6108.11488
Model 7: beta	-6109.10482
Model 8: beta&w>1	-6109.106913


Model 0 vs 1	10.37423999999919

Model 2 vs 1	0.0

Model 8 vs 7	0.004186000000117929