--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Sat Nov 12 07:43:51 WET 2016
codeml.models=0 1 2 3 7 8
mrbayes.mpich=
mrbayes.ngen=1000000
tcoffee.alignMethod=CLUSTALW2
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/ADOPS/2/ab-PB/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/2/ab-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/ab-PB/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/ADOPS/2/ab-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -9549.98         -9563.03
2      -9549.92         -9564.86
--------------------------------------
TOTAL    -9549.95         -9564.31
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/2/ab-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/ab-PB/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/ADOPS/2/ab-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         1.133752    0.003677    1.023109    1.258663    1.130396   1274.35   1384.32    1.000
r(A<->C){all}   0.075860    0.000095    0.057817    0.095435    0.075650    657.00    825.04    1.000
r(A<->G){all}   0.181743    0.000280    0.146550    0.211669    0.181628    781.63    903.92    1.000
r(A<->T){all}   0.134193    0.000353    0.098609    0.170831    0.133839    954.65    966.12    1.001
r(C<->G){all}   0.043275    0.000031    0.032990    0.054633    0.042947   1134.95   1151.10    1.000
r(C<->T){all}   0.521741    0.000624    0.470845    0.567580    0.521880    632.20    884.50    1.000
r(G<->T){all}   0.043188    0.000091    0.025416    0.062649    0.042711   1037.33   1141.77    1.000
pi(A){all}      0.229089    0.000055    0.214575    0.243589    0.228994   1146.79   1223.28    1.000
pi(C){all}      0.340816    0.000065    0.325638    0.357512    0.340773   1148.80   1193.29    1.000
pi(G){all}      0.288579    0.000066    0.272014    0.303426    0.288577   1011.28   1039.36    1.000
pi(T){all}      0.141516    0.000032    0.130096    0.152489    0.141541    752.20    910.00    1.000
alpha{1,2}      0.142435    0.000111    0.122575    0.163218    0.141961   1315.71   1343.87    1.000
alpha{3}        3.186952    0.491472    1.957350    4.580550    3.098432   1248.13   1374.56    1.000
pinvar{all}     0.340083    0.000757    0.286432    0.393686    0.341067   1104.18   1196.40    1.001
------------------------------------------------------------------------------------------------------
* 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	-8333.050317
Model 2: PositiveSelection	-8333.05045
Model 0: one-ratio	-8403.270713
Model 3: discrete	-8304.51193
Model 7: beta	-8304.768658
Model 8: beta&w>1	-8304.770348


Model 0 vs 1	140.44079200000124

Model 2 vs 1	2.660000027390197E-4

Model 8 vs 7	0.0033799999982875306