--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Tue Nov 22 08:02:25 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/3/acj6-PF/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/3/acj6-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PF/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/3/acj6-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -2257.37         -2282.05
2      -2256.73         -2276.21
--------------------------------------
TOTAL    -2257.00         -2281.36
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/3/acj6-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PF/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/3/acj6-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         0.389200    0.003138    0.287301    0.504191    0.384507   1405.33   1436.15    1.000
r(A<->C){all}   0.119417    0.001146    0.058768    0.190841    0.116106    795.84    908.99    1.000
r(A<->G){all}   0.251567    0.002956    0.147983    0.358029    0.247817    589.66    640.17    1.000
r(A<->T){all}   0.110706    0.001484    0.041559    0.187782    0.107179    757.43    817.06    1.000
r(C<->G){all}   0.065725    0.000407    0.030332    0.107925    0.063458    960.11   1006.60    1.000
r(C<->T){all}   0.441390    0.003780    0.323123    0.560051    0.441287    591.01    664.37    1.000
r(G<->T){all}   0.011194    0.000107    0.000002    0.031442    0.008227    921.04   1021.98    1.000
pi(A){all}      0.238453    0.000149    0.215876    0.262784    0.238250    986.36   1149.22    1.000
pi(C){all}      0.308173    0.000175    0.283215    0.334078    0.307752   1207.76   1210.44    1.000
pi(G){all}      0.270920    0.000168    0.245455    0.296428    0.271094   1117.11   1203.63    1.000
pi(T){all}      0.182454    0.000122    0.162519    0.205410    0.182323   1156.83   1195.96    1.000
alpha{1,2}      0.046441    0.000670    0.000113    0.086602    0.048221   1266.70   1325.95    1.000
alpha{3}        2.457735    0.659965    1.069537    4.040857    2.338692   1396.56   1448.78    1.000
pinvar{all}     0.746451    0.000694    0.695172    0.797072    0.747761   1101.42   1284.18    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	-2158.388457
Model 2: PositiveSelection	-2158.385862
Model 0: one-ratio	-2158.434656
Model 3: discrete	-2158.385862
Model 7: beta	-2158.385379
Model 8: beta&w>1	-2158.387974


Model 0 vs 1	0.09239799999977549

Model 2 vs 1	0.005189999999856809

Model 8 vs 7	0.005190000000766304