--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Wed May 30 13:43:40 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/NS2A_1/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_A1/NS2A_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2A_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_A1/NS2A_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -7580.43 -7634.99
2 -7585.83 -7633.43
--------------------------------------
TOTAL -7581.12 -7634.49
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_A1/NS2A_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2A_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_A1/NS2A_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} 10.275873 0.484566 8.951379 11.665340 10.256450 549.80 730.51 1.002
r(A<->C){all} 0.046232 0.000067 0.029964 0.061376 0.045887 691.07 744.71 1.000
r(A<->G){all} 0.241327 0.000319 0.205915 0.276539 0.241358 549.70 553.44 1.000
r(A<->T){all} 0.056932 0.000061 0.041280 0.071485 0.056639 683.07 835.83 1.000
r(C<->G){all} 0.033750 0.000058 0.020306 0.049952 0.033351 836.19 889.30 1.000
r(C<->T){all} 0.584958 0.000450 0.541341 0.625048 0.584039 569.80 581.38 1.000
r(G<->T){all} 0.036800 0.000056 0.022125 0.051236 0.036376 981.56 1041.03 1.000
pi(A){all} 0.300289 0.000113 0.280601 0.321731 0.299964 640.69 868.50 1.000
pi(C){all} 0.216282 0.000089 0.198416 0.234749 0.216265 679.26 805.10 1.002
pi(G){all} 0.246808 0.000108 0.227433 0.267562 0.246704 815.72 866.35 1.001
pi(T){all} 0.236621 0.000095 0.217929 0.255749 0.236795 738.45 898.58 1.000
alpha{1,2} 0.418408 0.001734 0.343737 0.504083 0.414891 1164.32 1197.36 1.000
alpha{3} 4.607867 0.961182 2.826767 6.546296 4.498651 1272.24 1384.16 1.000
pinvar{all} 0.030873 0.000338 0.000049 0.065160 0.028076 1223.28 1272.37 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 -7286.010144
Model 2: PositiveSelection -7286.010144
Model 0: one-ratio -7293.64689
Model 3: discrete -7228.709293
Model 7: beta -7230.250979
Model 8: beta&w>1 -7230.253144
Model 0 vs 1 15.27349200000026
Model 2 vs 1 0.0
Model 8 vs 7 0.004329999999754364
