--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Fri Dec 09 19:09:35 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/439/Wnt2-PA/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/439/Wnt2-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/439/Wnt2-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/439/Wnt2-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -3199.69         -3214.97
2      -3199.32         -3216.07
--------------------------------------
TOTAL    -3199.49         -3215.67
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/439/Wnt2-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/439/Wnt2-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/439/Wnt2-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}         0.743549    0.006198    0.595570    0.900638    0.740252    891.71   1116.87    1.000
r(A<->C){all}   0.122289    0.000461    0.078390    0.160928    0.121234   1006.91   1055.82    1.000
r(A<->G){all}   0.241021    0.001219    0.176093    0.310992    0.239454    724.90    733.69    1.000
r(A<->T){all}   0.111395    0.000915    0.054572    0.170777    0.109484    813.85    872.02    1.000
r(C<->G){all}   0.049044    0.000121    0.029718    0.072999    0.048094    985.85   1030.53    1.000
r(C<->T){all}   0.419369    0.001854    0.339461    0.504459    0.417453    615.23    723.38    1.000
r(G<->T){all}   0.056883    0.000266    0.026754    0.089139    0.055945    926.31   1010.97    1.000
pi(A){all}      0.212398    0.000158    0.188264    0.237172    0.212105    928.56    954.75    1.000
pi(C){all}      0.314347    0.000190    0.288020    0.340774    0.314431    905.68   1057.21    1.000
pi(G){all}      0.311758    0.000196    0.283642    0.337729    0.311813    975.70   1094.62    1.001
pi(T){all}      0.161496    0.000110    0.140399    0.182245    0.161356   1098.94   1130.76    1.000
alpha{1,2}      0.166115    0.000536    0.124260    0.213543    0.163444   1182.55   1206.28    1.000
alpha{3}        2.890917    0.789887    1.366468    4.673697    2.768291   1487.72   1494.36    1.000
pinvar{all}     0.531197    0.001478    0.452431    0.602085    0.533092   1353.51   1399.58    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	-2940.374583
Model 2: PositiveSelection	-2940.374587
Model 0: one-ratio	-2969.136327
Model 3: discrete	-2936.864527
Model 7: beta	-2939.941339
Model 8: beta&w>1	-2937.016945


Model 0 vs 1	57.523488000000725

Model 2 vs 1	7.999999979801942E-6

Model 8 vs 7	5.848788000000241