--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Thu Jul 12 12:02:52 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_N3/NS4B_2/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N3/NS4B_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N3/NS4B_2/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_N3/NS4B_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -5934.26         -5985.98
2      -5934.13         -5983.21
--------------------------------------
TOTAL    -5934.19         -5985.35
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N3/NS4B_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N3/NS4B_2/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_N3/NS4B_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         7.130853    0.242613    6.226398    8.133440    7.117942    684.86    709.90    1.000
r(A<->C){all}   0.043035    0.000058    0.028465    0.058338    0.042501    831.72    878.21    1.000
r(A<->G){all}   0.210947    0.000372    0.174128    0.246675    0.210451    510.74    567.24    1.002
r(A<->T){all}   0.053843    0.000076    0.037199    0.070980    0.053501    715.51    754.31    1.000
r(C<->G){all}   0.017848    0.000050    0.005264    0.032536    0.017337    667.13    695.22    1.002
r(C<->T){all}   0.641040    0.000553    0.594108    0.685189    0.641833    593.59    631.39    1.002
r(G<->T){all}   0.033286    0.000071    0.017579    0.049779    0.032963    835.99    859.96    1.000
pi(A){all}      0.331237    0.000147    0.307417    0.354240    0.331043    855.08    858.35    1.000
pi(C){all}      0.231763    0.000108    0.212648    0.253474    0.231503    730.49    754.49    1.000
pi(G){all}      0.217931    0.000119    0.197111    0.239314    0.217740    822.35    860.33    1.000
pi(T){all}      0.219070    0.000100    0.199762    0.239221    0.219003    885.79    940.38    1.000
alpha{1,2}      0.185301    0.000156    0.161498    0.209716    0.184866   1106.00   1199.04    1.000
alpha{3}        4.042145    0.693543    2.550689    5.717268    3.953438   1501.00   1501.00    1.000
pinvar{all}     0.138047    0.000810    0.081720    0.193177    0.137138   1247.40   1374.20    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	-5013.134826
Model 2: PositiveSelection	-5013.134889
Model 0: one-ratio	-5053.102365
Model 3: discrete	-4963.271977
Model 7: beta	-4967.482043
Model 8: beta&w>1	-4967.483588


Model 0 vs 1	79.93507799999861

Model 2 vs 1	1.2599999899975955E-4

Model 8 vs 7	0.0030900000001565786