--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Mon Nov 28 15:36:36 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/6/AGO1-PA/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/6/AGO1-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/6/AGO1-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/6/AGO1-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -9130.50         -9148.15
2      -9129.84         -9148.54
--------------------------------------
TOTAL    -9130.12         -9148.37
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/6/AGO1-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/6/AGO1-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/6/AGO1-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}         1.082724    0.003020    0.973985    1.189983    1.080582   1264.35   1382.68    1.000
r(A<->C){all}   0.076628    0.000104    0.057280    0.097536    0.076225   1076.80   1103.24    1.000
r(A<->G){all}   0.334556    0.000493    0.291188    0.377702    0.333983    943.64    971.63    1.000
r(A<->T){all}   0.132450    0.000319    0.098365    0.167671    0.131602    932.96    944.36    1.000
r(C<->G){all}   0.037657    0.000026    0.028283    0.047999    0.037464   1191.86   1250.81    1.000
r(C<->T){all}   0.373332    0.000488    0.330429    0.415789    0.373067    742.22    832.52    1.000
r(G<->T){all}   0.045377    0.000068    0.030112    0.062681    0.045004   1037.38   1106.89    1.000
pi(A){all}      0.198979    0.000049    0.185596    0.212905    0.199073    953.05   1060.78    1.000
pi(C){all}      0.318418    0.000062    0.303666    0.334495    0.318476   1183.70   1184.65    1.001
pi(G){all}      0.278726    0.000063    0.263470    0.294722    0.278890   1055.25   1207.39    1.000
pi(T){all}      0.203878    0.000044    0.191147    0.216882    0.203887    872.37    992.69    1.000
alpha{1,2}      0.056340    0.000195    0.020471    0.077177    0.059502   1057.83   1073.69    1.000
alpha{3}        5.584025    1.308599    3.506649    7.841655    5.475720   1381.60   1441.30    1.000
pinvar{all}     0.429794    0.000453    0.387788    0.470177    0.430381   1212.64   1356.82    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	-8464.310743
Model 2: PositiveSelection	-8464.302554
Model 0: one-ratio	-8464.302554
Model 3: discrete	-8464.302554
Model 7: beta	-8464.383754
Model 8: beta&w>1	-8464.391933


Model 0 vs 1	0.016378000000258908

Model 2 vs 1	0.016378000000258908

Model 8 vs 7	0.01635800000076415