--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Sat Jun 02 20:06:39 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_A1/NS2B_5/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_A1/NS2B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2B_5/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_A1/NS2B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -4026.69 -4075.62
2 -4028.16 -4071.43
--------------------------------------
TOTAL -4027.18 -4074.94
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_A1/NS2B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2B_5/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_A1/NS2B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 7.124588 0.298460 6.127232 8.263139 7.093845 964.81 976.02 1.000
r(A<->C){all} 0.066252 0.000131 0.043401 0.088526 0.065766 655.28 818.10 1.000
r(A<->G){all} 0.250646 0.000679 0.202312 0.302209 0.249876 527.72 541.27 1.000
r(A<->T){all} 0.071268 0.000147 0.049149 0.095759 0.070423 811.15 833.53 1.000
r(C<->G){all} 0.038741 0.000108 0.018485 0.058716 0.038122 996.41 998.92 1.000
r(C<->T){all} 0.546956 0.000976 0.484762 0.605374 0.547237 488.75 551.29 1.000
r(G<->T){all} 0.026138 0.000080 0.010372 0.044096 0.025162 988.52 996.19 1.000
pi(A){all} 0.323125 0.000241 0.293787 0.353921 0.323430 948.31 1090.09 1.000
pi(C){all} 0.226402 0.000191 0.199964 0.253395 0.226094 768.16 832.58 1.000
pi(G){all} 0.235813 0.000211 0.209071 0.264508 0.235661 730.34 809.15 1.000
pi(T){all} 0.214660 0.000175 0.189871 0.241630 0.214228 810.96 1006.19 1.000
alpha{1,2} 0.292329 0.000970 0.239622 0.359700 0.289304 1448.51 1474.75 1.000
alpha{3} 3.690365 0.798533 2.028622 5.346438 3.602254 1431.34 1466.17 1.000
pinvar{all} 0.054811 0.000700 0.004618 0.104609 0.053790 1151.76 1266.43 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 -3832.589069
Model 2: PositiveSelection -3832.589049
Model 0: one-ratio -3844.501637
Model 3: discrete -3806.431394
Model 7: beta -3807.816542
Model 8: beta&w>1 -3807.817845
Model 0 vs 1 23.825135999999475
Model 2 vs 1 3.999999989900971E-5
Model 8 vs 7 0.002606000000014319
