--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Wed May 09 19:49:47 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_N2/NS2A_5/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N2/NS2A_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS2A_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_N2/NS2A_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -6687.90 -6749.29
2 -6689.43 -6740.72
--------------------------------------
TOTAL -6688.40 -6748.60
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N2/NS2A_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS2A_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_N2/NS2A_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} 10.091904 0.507607 8.690828 11.466010 10.061140 553.20 640.55 1.000
r(A<->C){all} 0.047588 0.000074 0.032047 0.065131 0.047219 702.10 799.36 1.000
r(A<->G){all} 0.221296 0.000317 0.187808 0.256169 0.220797 569.56 596.25 1.002
r(A<->T){all} 0.050872 0.000067 0.035392 0.067844 0.050537 806.83 835.57 1.000
r(C<->G){all} 0.030681 0.000080 0.014980 0.049214 0.030119 803.43 883.96 1.000
r(C<->T){all} 0.613832 0.000478 0.569521 0.653827 0.613844 607.62 656.62 1.000
r(G<->T){all} 0.035731 0.000076 0.017969 0.051533 0.035228 701.94 703.63 1.001
pi(A){all} 0.306469 0.000125 0.284766 0.328552 0.306235 805.97 816.20 1.001
pi(C){all} 0.215753 0.000092 0.197790 0.235027 0.215828 879.00 908.98 1.000
pi(G){all} 0.242005 0.000106 0.220428 0.261123 0.241868 876.44 940.60 1.000
pi(T){all} 0.235773 0.000099 0.215658 0.254474 0.235757 613.95 768.23 1.002
alpha{1,2} 0.390371 0.001521 0.321652 0.469621 0.387710 1119.36 1172.20 1.000
alpha{3} 3.635619 0.662038 2.245630 5.334005 3.535999 1171.84 1336.42 1.000
pinvar{all} 0.029416 0.000367 0.000015 0.064571 0.026258 1135.68 1145.80 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 -6348.524594
Model 2: PositiveSelection -6348.524594
Model 0: one-ratio -6369.868452
Model 3: discrete -6295.535119
Model 7: beta -6298.611008
Model 8: beta&w>1 -6296.870221
Model 0 vs 1 42.687715999998545
Model 2 vs 1 0.0
Model 8 vs 7 3.481573999999455
