--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Tue Dec 06 14:34:16 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/398/Snr1-PA/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/398/Snr1-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/398/Snr1-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/398/Snr1-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -3915.80         -3932.67
2      -3915.01         -3934.92
--------------------------------------
TOTAL    -3915.33         -3934.33
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/398/Snr1-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/398/Snr1-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/398/Snr1-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}         1.521540    0.012600    1.300875    1.741680    1.519128   1361.15   1431.08    1.000
r(A<->C){all}   0.092973    0.000244    0.062300    0.123825    0.091755   1026.57   1031.84    1.001
r(A<->G){all}   0.282576    0.000915    0.226149    0.343229    0.282244    732.61    799.03    1.001
r(A<->T){all}   0.081228    0.000637    0.036217    0.132752    0.079598    751.12    830.59    1.000
r(C<->G){all}   0.027221    0.000045    0.014868    0.040402    0.026887    937.75    977.65    1.000
r(C<->T){all}   0.430241    0.001262    0.363098    0.499345    0.430170    731.96    841.11    1.000
r(G<->T){all}   0.085761    0.000246    0.056248    0.116897    0.084894    807.64    880.40    1.001
pi(A){all}      0.212137    0.000138    0.188992    0.234845    0.212444    783.63    987.43    1.001
pi(C){all}      0.334413    0.000171    0.309296    0.360938    0.334221    992.80   1072.73    1.001
pi(G){all}      0.288570    0.000162    0.265838    0.315657    0.288238   1044.02   1101.56    1.000
pi(T){all}      0.164881    0.000092    0.145679    0.183041    0.164589   1077.93   1101.53    1.000
alpha{1,2}      0.037695    0.000467    0.000266    0.069867    0.039342    791.46    894.72    1.000
alpha{3}        4.521970    1.049778    2.698380    6.564698    4.413021   1108.47   1267.60    1.001
pinvar{all}     0.250813    0.001370    0.175195    0.319640    0.251034   1361.65   1419.79    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	-3543.510495
Model 2: PositiveSelection	-3543.506831
Model 0: one-ratio	-3543.506828
Model 3: discrete	-3543.506876
Model 7: beta	-3543.522495
Model 8: beta&w>1	-3543.522844


Model 0 vs 1	0.007333999999900698

Model 2 vs 1	0.007327999999688473

Model 8 vs 7	6.979999998293351E-4