--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Wed May 02 17:24:17 WEST 2018
codeml.models=0 1 2 3 7 8
mrbayes.mpich=
mrbayes.ngen=1000000
tcoffee.alignMethod=MUSCLE
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/ADOPS1/DNG_N1/E_4/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N1/E_4/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_4/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/ADOPS1/DNG_N1/E_4/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1     -12489.33        -12533.25
2     -12489.32        -12532.48
--------------------------------------
TOTAL   -12489.33        -12532.94
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N1/E_4/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_4/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/ADOPS1/DNG_N1/E_4/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         9.377637    0.305680    8.304878   10.455240    9.367509    565.09    622.74    1.000
r(A<->C){all}   0.036340    0.000023    0.027334    0.045985    0.036102    770.85    829.97    1.000
r(A<->G){all}   0.180672    0.000145    0.157676    0.204431    0.180585    522.38    560.57    1.000
r(A<->T){all}   0.047690    0.000031    0.037667    0.059188    0.047463    905.86    948.90    1.000
r(C<->G){all}   0.023142    0.000023    0.014076    0.032760    0.022939    716.08    767.06    1.000
r(C<->T){all}   0.690025    0.000241    0.660586    0.720244    0.689863    531.29    567.51    1.000
r(G<->T){all}   0.022130    0.000026    0.012244    0.031987    0.021993    649.60    657.59    1.000
pi(A){all}      0.346094    0.000072    0.330189    0.363424    0.346069    809.37    829.36    1.000
pi(C){all}      0.215244    0.000050    0.202405    0.228932    0.215026    611.90    732.06    1.000
pi(G){all}      0.240989    0.000060    0.226643    0.256652    0.240721    692.51    715.53    1.001
pi(T){all}      0.197673    0.000047    0.182875    0.210126    0.197525    720.06    758.54    1.000
alpha{1,2}      0.193925    0.000100    0.174806    0.213071    0.193206   1092.90   1142.00    1.000
alpha{3}        4.544400    0.618034    3.030927    5.974584    4.464177   1501.00   1501.00    1.000
pinvar{all}     0.077590    0.000282    0.046023    0.110009    0.076672   1277.18   1350.00    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	-11824.173141
Model 2: PositiveSelection	-11824.173141
Model 0: one-ratio	-11850.499226
Model 3: discrete	-11677.499809
Model 7: beta	-11678.227011
Model 8: beta&w>1	-11678.230481


Model 0 vs 1	52.652170000001206

Model 2 vs 1	0.0

Model 8 vs 7	0.006939999999303836