--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Wed Nov 02 14:53:29 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/14/Arl5-PA/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/14/Arl5-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/14/Arl5-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/14/Arl5-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -1790.96         -1809.44
2      -1790.54         -1807.98
--------------------------------------
TOTAL    -1790.73         -1808.95
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/14/Arl5-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/14/Arl5-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/14/Arl5-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}         1.360324    0.022172    1.097790    1.672114    1.353018   1202.77   1276.11    1.000
r(A<->C){all}   0.099697    0.000529    0.057888    0.145736    0.097994    801.80    909.01    1.000
r(A<->G){all}   0.220806    0.001570    0.147453    0.300013    0.217351    743.81    774.80    1.001
r(A<->T){all}   0.017110    0.000296    0.000013    0.051406    0.011960    522.26    757.58    1.000
r(C<->G){all}   0.055933    0.000190    0.030834    0.084527    0.054713   1046.00   1050.16    1.000
r(C<->T){all}   0.572883    0.002536    0.472851    0.666363    0.573493    650.77    746.29    1.001
r(G<->T){all}   0.033571    0.000206    0.007509    0.061226    0.032221    995.13   1033.17    1.000
pi(A){all}      0.232541    0.000313    0.199698    0.268381    0.232665    933.09    993.88    1.000
pi(C){all}      0.286523    0.000303    0.250996    0.318808    0.286229   1212.98   1296.45    1.000
pi(G){all}      0.299230    0.000334    0.264494    0.336610    0.298622   1122.53   1160.52    1.000
pi(T){all}      0.181706    0.000218    0.153801    0.210788    0.181481    970.80   1099.16    1.000
alpha{1,2}      0.072469    0.000241    0.045571    0.104129    0.073167    958.46   1019.05    1.001
alpha{3}        3.430087    0.932108    1.761111    5.327696    3.287892   1203.73   1302.82    1.000
pinvar{all}     0.303055    0.002824    0.195675    0.400741    0.303969   1166.90   1331.58    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	-1592.168673
Model 2: PositiveSelection	-1592.166905
Model 0: one-ratio	-1592.281347
Model 3: discrete	-1592.166905
Model 7: beta	-1592.165772
Model 8: beta&w>1	-1592.200271


Model 0 vs 1	0.22534800000039468

Model 2 vs 1	0.003535999999712658

Model 8 vs 7	0.06899799999973766