--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Mon Nov 28 23:40:01 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/7/Alg10-PA/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/7/Alg10-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/7/Alg10-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/7/Alg10-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -5341.55         -5359.38
2      -5342.13         -5357.10
--------------------------------------
TOTAL    -5341.80         -5358.79
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/7/Alg10-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/7/Alg10-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/7/Alg10-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}         1.207548    0.006140    1.054870    1.359848    1.206157   1256.46   1294.66    1.000
r(A<->C){all}   0.120983    0.000247    0.089842    0.151121    0.120248    979.88   1037.02    1.001
r(A<->G){all}   0.326774    0.000806    0.274793    0.383296    0.326047   1034.86   1080.30    1.000
r(A<->T){all}   0.090421    0.000322    0.058619    0.127478    0.089191    980.93   1027.38    1.000
r(C<->G){all}   0.070628    0.000098    0.051137    0.089298    0.070311    832.72   1008.72    1.000
r(C<->T){all}   0.338222    0.000771    0.285275    0.391125    0.338129    896.57    908.58    1.000
r(G<->T){all}   0.052972    0.000104    0.034053    0.073235    0.052723    951.12   1086.27    1.000
pi(A){all}      0.177651    0.000094    0.158495    0.196266    0.177405    923.09   1069.63    1.000
pi(C){all}      0.300134    0.000125    0.277718    0.321212    0.300176   1122.27   1276.80    1.000
pi(G){all}      0.264259    0.000120    0.242935    0.285301    0.263855   1222.34   1228.72    1.000
pi(T){all}      0.257956    0.000122    0.236483    0.279252    0.258066   1161.15   1176.29    1.000
alpha{1,2}      0.160094    0.000217    0.133237    0.189073    0.158962   1093.61   1189.48    1.000
alpha{3}        3.700052    0.800830    2.132795    5.452389    3.586128   1230.51   1365.76    1.000
pinvar{all}     0.292967    0.001343    0.220204    0.359599    0.293573   1339.21   1420.10    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	-5067.242599
Model 2: PositiveSelection	-5067.242599
Model 0: one-ratio	-5110.730519
Model 3: discrete	-5060.295521
Model 7: beta	-5062.093895
Model 8: beta&w>1	-5061.205635


Model 0 vs 1	86.97583999999915

Model 2 vs 1	0.0

Model 8 vs 7	1.7765199999994365