--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Sat Nov 12 08:20:57 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/2/ab-PD/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/2/ab-PD/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/ab-PD/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/2/ab-PD/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -9550.38         -9564.03
2      -9550.42         -9564.12
--------------------------------------
TOTAL    -9550.40         -9564.07
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/2/ab-PD/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/ab-PD/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/2/ab-PD/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         1.135076    0.003754    1.025178    1.262076    1.132208   1278.63   1389.81    1.000
r(A<->C){all}   0.075882    0.000093    0.057170    0.095335    0.075335    889.47    946.99    1.000
r(A<->G){all}   0.181152    0.000288    0.150074    0.216044    0.180355    597.03    745.33    1.001
r(A<->T){all}   0.133943    0.000342    0.098902    0.170701    0.133579    825.83    885.42    1.002
r(C<->G){all}   0.042911    0.000033    0.032084    0.054233    0.042602   1164.57   1227.91    1.000
r(C<->T){all}   0.522874    0.000642    0.471691    0.569673    0.522509    606.37    700.16    1.003
r(G<->T){all}   0.043237    0.000088    0.025369    0.061152    0.042750   1106.48   1166.34    1.000
pi(A){all}      0.228810    0.000058    0.213356    0.242825    0.228840    920.83   1040.40    1.004
pi(C){all}      0.341048    0.000068    0.324480    0.356152    0.341250    904.31   1079.38    1.001
pi(G){all}      0.288792    0.000066    0.273802    0.305765    0.288740   1271.35   1271.89    1.000
pi(T){all}      0.141351    0.000033    0.130445    0.153219    0.141162    829.03    972.75    1.000
alpha{1,2}      0.142338    0.000111    0.121751    0.163260    0.141998   1142.97   1292.97    1.000
alpha{3}        3.169330    0.494633    1.940352    4.547084    3.079945   1047.80   1169.89    1.003
pinvar{all}     0.339833    0.000796    0.284244    0.393986    0.340849   1248.42   1358.93    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	-8333.050317
Model 2: PositiveSelection	-8333.050317
Model 0: one-ratio	-8403.270713
Model 3: discrete	-8304.51193
Model 7: beta	-8304.768658
Model 8: beta&w>1	-8304.770348


Model 0 vs 1	140.44079200000124

Model 2 vs 1	0.0

Model 8 vs 7	0.0033799999982875306