--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Fri Nov 25 15:26:45 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/1/14-3-3zeta-PL/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/1/14-3-3zeta-PL/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/1/14-3-3zeta-PL/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/1/14-3-3zeta-PL/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -1341.76         -1381.22
2      -1344.31         -1389.71
--------------------------------------
TOTAL    -1342.38         -1389.02
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/1/14-3-3zeta-PL/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/1/14-3-3zeta-PL/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/1/14-3-3zeta-PL/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         0.485009    0.063287    0.146257    1.001772    0.416285    891.72    895.63    1.000
r(A<->C){all}   0.068007    0.001572    0.000256    0.141216    0.060526    324.93    460.01    1.000
r(A<->G){all}   0.230654    0.015458    0.036083    0.482491    0.212445    201.41    266.37    1.001
r(A<->T){all}   0.064841    0.001531    0.005098    0.139618    0.057952    476.41    508.85    1.001
r(C<->G){all}   0.044280    0.000779    0.000058    0.096720    0.039213    333.93    394.36    1.001
r(C<->T){all}   0.574188    0.022134    0.288488    0.857889    0.572071    284.64    326.10    1.001
r(G<->T){all}   0.018030    0.000336    0.000004    0.056166    0.012076    613.81    728.16    1.000
pi(A){all}      0.280672    0.000267    0.249103    0.310808    0.280443   1083.40   1122.88    1.001
pi(C){all}      0.259480    0.000251    0.228249    0.289560    0.259411   1215.23   1292.76    1.000
pi(G){all}      0.259909    0.000255    0.230279    0.292257    0.259780    978.81   1082.22    1.002
pi(T){all}      0.199939    0.000212    0.173770    0.229793    0.199269   1087.76   1126.84    1.000
alpha{1,2}      0.093722    0.000843    0.035796    0.156225    0.091761    980.31    994.48    1.001
alpha{3}        1.191803    0.399384    0.299157    2.482751    1.059195    737.12    851.18    1.000
pinvar{all}     0.826061    0.001154    0.761418    0.890943    0.829741   1073.12   1097.42    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	-1263.066771
Model 2: PositiveSelection	-1263.066771
Model 0: one-ratio	-1264.026193
Model 3: discrete	-1263.063085
Model 7: beta	-1263.3924
Model 8: beta&w>1	-1263.066768


Model 0 vs 1	1.9188439999998081

Model 2 vs 1	0.0

Model 8 vs 7	0.6512640000000829