--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Fri Nov 25 13:09:30 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/1/14-3-3zeta-PA/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/1/14-3-3zeta-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/1/14-3-3zeta-PA/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/1/14-3-3zeta-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -1543.55         -1561.01
2      -1543.96         -1563.06
--------------------------------------
TOTAL    -1543.73         -1562.49
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/1/14-3-3zeta-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/1/14-3-3zeta-PA/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/1/14-3-3zeta-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         0.246522    0.001502    0.173765    0.323333    0.242426   1112.86   1306.93    1.000
r(A<->C){all}   0.111993    0.001174    0.051622    0.181044    0.108417    555.21    675.77    1.000
r(A<->G){all}   0.154391    0.001814    0.080132    0.240578    0.149644    749.36    783.88    1.000
r(A<->T){all}   0.066539    0.001037    0.014722    0.132432    0.062067    850.93    910.96    1.000
r(C<->G){all}   0.061977    0.000652    0.015878    0.110995    0.059227    879.61    936.93    1.000
r(C<->T){all}   0.555556    0.004706    0.422485    0.687568    0.559302    715.13    732.62    1.000
r(G<->T){all}   0.049543    0.000750    0.003571    0.102933    0.044281    836.64    901.39    1.000
pi(A){all}      0.285183    0.000249    0.256090    0.318082    0.285053   1159.97   1227.91    1.000
pi(C){all}      0.261794    0.000247    0.233084    0.294652    0.261387   1183.78   1342.39    1.001
pi(G){all}      0.257922    0.000255    0.226904    0.288827    0.257691    974.12   1011.49    1.001
pi(T){all}      0.195101    0.000198    0.167834    0.222710    0.194877   1205.51   1247.99    1.000
alpha{1,2}      0.057345    0.001727    0.000122    0.136091    0.050348   1331.52   1372.07    1.001
alpha{3}        2.109083    0.675405    0.745799    3.711802    1.979228   1296.79   1398.89    1.000
pinvar{all}     0.478803    0.007900    0.292310    0.633813    0.489065   1140.09   1320.54    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	-1451.36118
Model 2: PositiveSelection	-1451.36118
Model 0: one-ratio	-1458.92382
Model 3: discrete	-1451.332921
Model 7: beta	-1451.333746
Model 8: beta&w>1	-1451.361179


Model 0 vs 1	15.125279999999748

Model 2 vs 1	0.0

Model 8 vs 7	0.054865999999947235