--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Sun Apr 29 05:38:51 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_1/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N1/E_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_1/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_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -12024.10 -12065.89
2 -12023.67 -12073.17
--------------------------------------
TOTAL -12023.86 -12072.48
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N1/E_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_1/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_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 9.072763 0.299181 8.059762 10.178320 9.052256 639.02 661.22 1.003
r(A<->C){all} 0.043055 0.000029 0.033157 0.054150 0.042952 400.12 669.49 1.000
r(A<->G){all} 0.192167 0.000155 0.167520 0.216700 0.191629 617.01 654.02 1.000
r(A<->T){all} 0.043105 0.000032 0.032089 0.053404 0.042942 899.99 905.72 1.000
r(C<->G){all} 0.013017 0.000019 0.005066 0.021518 0.012666 901.48 913.97 1.000
r(C<->T){all} 0.677146 0.000247 0.645984 0.707100 0.678055 497.76 565.72 1.000
r(G<->T){all} 0.031511 0.000033 0.020119 0.042610 0.031145 868.42 879.63 1.000
pi(A){all} 0.345488 0.000072 0.329451 0.362842 0.345416 996.77 1019.42 1.000
pi(C){all} 0.216971 0.000049 0.204284 0.231514 0.216880 1021.12 1066.87 1.001
pi(G){all} 0.240442 0.000060 0.225561 0.255996 0.240444 822.11 968.19 1.000
pi(T){all} 0.197099 0.000044 0.184397 0.210145 0.197128 634.31 641.20 1.000
alpha{1,2} 0.202626 0.000117 0.181877 0.223807 0.202087 1172.25 1245.76 1.000
alpha{3} 5.170748 0.789481 3.575563 6.949871 5.082841 1387.90 1444.45 1.000
pinvar{all} 0.099602 0.000310 0.064680 0.132414 0.098983 1071.61 1165.32 1.002
------------------------------------------------------------------------------------------------------
* 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 -11214.420128
Model 2: PositiveSelection -11214.420127
Model 0: one-ratio -11261.320842
Model 3: discrete -11068.601597
Model 7: beta -11071.594983
Model 8: beta&w>1 -11071.59836
Model 0 vs 1 93.80142799999885
Model 2 vs 1 2.0000006770715117E-6
Model 8 vs 7 0.0067539999981818255
