--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Mon Nov 21 14:18:48 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/295/Lmpt-PE/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/295/Lmpt-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/295/Lmpt-PE/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/295/Lmpt-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -1995.72         -2013.26
2      -1995.78         -2016.93
--------------------------------------
TOTAL    -1995.75         -2016.26
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/295/Lmpt-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/295/Lmpt-PE/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/295/Lmpt-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         0.834238    0.012002    0.627644    1.051122    0.825141   1419.53   1460.27    1.000
r(A<->C){all}   0.080960    0.000492    0.040268    0.124935    0.079031    985.86   1102.20    1.000
r(A<->G){all}   0.220212    0.002522    0.128312    0.320676    0.217389    539.12    646.88    1.000
r(A<->T){all}   0.010102    0.000094    0.000005    0.029522    0.007173    761.61    928.49    1.000
r(C<->G){all}   0.097730    0.000470    0.057155    0.140022    0.095920    773.93    880.28    1.000
r(C<->T){all}   0.562495    0.003140    0.458799    0.674044    0.562662    660.73    706.22    1.000
r(G<->T){all}   0.028500    0.000275    0.000097    0.060267    0.026316    950.92   1043.47    1.000
pi(A){all}      0.225826    0.000218    0.196206    0.253302    0.225463   1225.58   1339.66    1.000
pi(C){all}      0.311483    0.000271    0.278221    0.341525    0.311604   1254.54   1261.33    1.000
pi(G){all}      0.275871    0.000265    0.245869    0.308100    0.275597   1196.40   1260.28    1.000
pi(T){all}      0.186820    0.000176    0.162500    0.214740    0.186298   1094.16   1181.14    1.000
alpha{1,2}      0.085992    0.000275    0.057500    0.119788    0.085828    877.60   1024.87    1.000
alpha{3}        2.224142    0.488473    1.061426    3.648855    2.111553   1329.23   1376.04    1.000
pinvar{all}     0.600376    0.001409    0.527285    0.669611    0.602321   1256.34   1372.71    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	-1818.876561
Model 2: PositiveSelection	-1818.874206
Model 0: one-ratio	-1818.988065
Model 3: discrete	-1818.874206
Model 7: beta	-1818.873078
Model 8: beta&w>1	-1818.875434


Model 0 vs 1	0.22300799999993615

Model 2 vs 1	0.004710000000159198

Model 8 vs 7	0.004711999999926775