--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Sat Jun 02 20:06:39 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/NS2B_5/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_A1/NS2B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2B_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/NS2B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -4026.69         -4075.62
2      -4028.16         -4071.43
--------------------------------------
TOTAL    -4027.18         -4074.94
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_A1/NS2B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2B_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/NS2B_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}         7.124588    0.298460    6.127232    8.263139    7.093845    964.81    976.02    1.000
r(A<->C){all}   0.066252    0.000131    0.043401    0.088526    0.065766    655.28    818.10    1.000
r(A<->G){all}   0.250646    0.000679    0.202312    0.302209    0.249876    527.72    541.27    1.000
r(A<->T){all}   0.071268    0.000147    0.049149    0.095759    0.070423    811.15    833.53    1.000
r(C<->G){all}   0.038741    0.000108    0.018485    0.058716    0.038122    996.41    998.92    1.000
r(C<->T){all}   0.546956    0.000976    0.484762    0.605374    0.547237    488.75    551.29    1.000
r(G<->T){all}   0.026138    0.000080    0.010372    0.044096    0.025162    988.52    996.19    1.000
pi(A){all}      0.323125    0.000241    0.293787    0.353921    0.323430    948.31   1090.09    1.000
pi(C){all}      0.226402    0.000191    0.199964    0.253395    0.226094    768.16    832.58    1.000
pi(G){all}      0.235813    0.000211    0.209071    0.264508    0.235661    730.34    809.15    1.000
pi(T){all}      0.214660    0.000175    0.189871    0.241630    0.214228    810.96   1006.19    1.000
alpha{1,2}      0.292329    0.000970    0.239622    0.359700    0.289304   1448.51   1474.75    1.000
alpha{3}        3.690365    0.798533    2.028622    5.346438    3.602254   1431.34   1466.17    1.000
pinvar{all}     0.054811    0.000700    0.004618    0.104609    0.053790   1151.76   1266.43    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	-3832.589069
Model 2: PositiveSelection	-3832.589049
Model 0: one-ratio	-3844.501637
Model 3: discrete	-3806.431394
Model 7: beta	-3807.816542
Model 8: beta&w>1	-3807.817845


Model 0 vs 1	23.825135999999475

Model 2 vs 1	3.999999989900971E-5

Model 8 vs 7	0.002606000000014319