--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Tue Jul 10 20:52:26 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/NS4A_2/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N3/NS4A_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N3/NS4A_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/NS4A_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -3514.85 -3568.82
2 -3515.10 -3566.29
--------------------------------------
TOTAL -3514.96 -3568.20
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N3/NS4A_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N3/NS4A_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/NS4A_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} 8.502252 0.430295 7.283136 9.786245 8.463103 783.73 855.84 1.000
r(A<->C){all} 0.035534 0.000089 0.017370 0.054034 0.034741 719.26 803.68 1.000
r(A<->G){all} 0.209074 0.000554 0.165199 0.256201 0.208458 524.21 564.74 1.001
r(A<->T){all} 0.064554 0.000155 0.042386 0.090068 0.063958 765.11 807.97 1.000
r(C<->G){all} 0.026491 0.000079 0.010935 0.044564 0.025669 536.10 701.20 1.000
r(C<->T){all} 0.629481 0.000885 0.571264 0.687423 0.630244 414.68 507.09 1.002
r(G<->T){all} 0.034865 0.000123 0.013625 0.056688 0.033967 733.71 802.13 1.001
pi(A){all} 0.310916 0.000264 0.280143 0.343159 0.310535 762.52 857.94 1.001
pi(C){all} 0.248114 0.000226 0.217366 0.275306 0.247914 709.70 759.03 1.000
pi(G){all} 0.231606 0.000213 0.204670 0.260616 0.231292 671.13 702.97 1.000
pi(T){all} 0.209364 0.000168 0.183708 0.233362 0.208968 626.78 672.03 1.001
alpha{1,2} 0.219295 0.000383 0.183301 0.257016 0.217270 997.29 1249.15 1.000
alpha{3} 4.690126 1.071187 2.835821 6.704374 4.561190 1248.30 1349.91 1.000
pinvar{all} 0.037029 0.000630 0.000052 0.084994 0.033113 1347.51 1375.61 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 -3197.53676
Model 2: PositiveSelection -3197.53676
Model 0: one-ratio -3206.942355
Model 3: discrete -3171.043127
Model 7: beta -3173.693609
Model 8: beta&w>1 -3173.694783
Model 0 vs 1 18.81119000000035
Model 2 vs 1 0.0
Model 8 vs 7 0.0023479999999835854
