--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Thu Dec 08 05:42:17 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/418/Tlk-PM/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/418/Tlk-PM/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/418/Tlk-PM/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/418/Tlk-PM/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1     -10828.02        -10845.83
2     -10827.95        -10842.04
--------------------------------------
TOTAL   -10827.98        -10845.16
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/418/Tlk-PM/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/418/Tlk-PM/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/418/Tlk-PM/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         0.722888    0.001295    0.653268    0.793567    0.722249   1494.82   1497.91    1.000
r(A<->C){all}   0.093031    0.000111    0.073592    0.114841    0.092958    981.66   1054.38    1.000
r(A<->G){all}   0.258413    0.000329    0.224969    0.294667    0.258008    868.40    897.52    1.000
r(A<->T){all}   0.102044    0.000236    0.072978    0.132252    0.101438    891.05    970.92    1.000
r(C<->G){all}   0.033196    0.000027    0.023421    0.043600    0.032969   1023.51   1074.17    1.000
r(C<->T){all}   0.447895    0.000501    0.406836    0.494059    0.448218    490.09    654.06    1.000
r(G<->T){all}   0.065421    0.000085    0.047761    0.083543    0.064841   1050.17   1065.50    1.000
pi(A){all}      0.244760    0.000045    0.232278    0.258626    0.244882   1206.78   1248.81    1.000
pi(C){all}      0.291733    0.000045    0.279475    0.305132    0.291667   1040.02   1122.15    1.001
pi(G){all}      0.305979    0.000051    0.291774    0.319300    0.305838    890.03   1048.80    1.000
pi(T){all}      0.157527    0.000028    0.147016    0.167049    0.157404   1015.64   1071.21    1.000
alpha{1,2}      0.097461    0.000077    0.080143    0.114111    0.097448   1443.80   1472.40    1.000
alpha{3}        6.026622    1.527686    3.816240    8.452252    5.907479   1282.08   1359.86    1.000
pinvar{all}     0.425855    0.000516    0.379555    0.469019    0.426383   1136.52   1283.70    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	-9565.631407
Model 2: PositiveSelection	-9565.631407
Model 0: one-ratio	-9645.56154
Model 3: discrete	-9561.890912
Model 7: beta	-9562.664216
Model 8: beta&w>1	-9562.004177


Model 0 vs 1	159.86026599999968

Model 2 vs 1	0.0

Model 8 vs 7	1.320077999997011