--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Sat Nov 12 07:09:22 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-PA/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

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

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -9662.08         -9676.88
2      -9662.13         -9677.89
--------------------------------------
TOTAL    -9662.10         -9677.51
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/2/ab-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/ab-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/2/ab-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}         1.127710    0.003883    1.016198    1.257378    1.125135   1222.71   1224.75    1.000
r(A<->C){all}   0.075381    0.000087    0.056560    0.093351    0.075124    821.50   1001.04    1.001
r(A<->G){all}   0.183892    0.000299    0.150037    0.217206    0.183261    746.28    802.18    1.001
r(A<->T){all}   0.135771    0.000319    0.101332    0.170846    0.135422    983.78   1001.37    1.001
r(C<->G){all}   0.043548    0.000032    0.033394    0.055208    0.043486   1228.93   1256.54    1.000
r(C<->T){all}   0.517653    0.000626    0.468078    0.564444    0.517111    746.31    783.44    1.000
r(G<->T){all}   0.043755    0.000089    0.026510    0.063752    0.043085   1010.28   1050.50    1.000
pi(A){all}      0.231152    0.000060    0.216016    0.245798    0.231012    980.96   1070.18    1.000
pi(C){all}      0.341327    0.000067    0.325451    0.356974    0.341130    940.71    942.55    1.000
pi(G){all}      0.286150    0.000063    0.271866    0.302763    0.285876   1005.94   1020.96    1.000
pi(T){all}      0.141371    0.000032    0.130914    0.152864    0.141212    900.79    990.02    1.000
alpha{1,2}      0.144060    0.000112    0.124045    0.164740    0.143552   1088.31   1122.53    1.000
alpha{3}        3.209153    0.501254    1.953241    4.542892    3.114192   1251.71   1376.36    1.000
pinvar{all}     0.348028    0.000760    0.290277    0.398083    0.348496   1144.25   1241.97    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	-8362.945549
Model 2: PositiveSelection	-8362.945564
Model 0: one-ratio	-8429.272407
Model 3: discrete	-8334.541998
Model 7: beta	-8334.810241
Model 8: beta&w>1	-8334.811974


Model 0 vs 1	132.6537160000007

Model 2 vs 1	2.9999999242136255E-5

Model 8 vs 7	0.003466000001935754