--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Tue May 01 12:19:02 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_3/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N1/E_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_3/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_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -12117.99 -12163.23
2 -12120.14 -12164.05
--------------------------------------
TOTAL -12118.57 -12163.72
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N1/E_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_3/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_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 8.883683 0.303010 7.784483 9.935343 8.870359 476.73 572.78 1.000
r(A<->C){all} 0.046526 0.000030 0.035833 0.057214 0.046335 917.78 940.50 1.000
r(A<->G){all} 0.193069 0.000151 0.169862 0.217268 0.192885 578.52 675.40 1.000
r(A<->T){all} 0.045094 0.000031 0.035173 0.056808 0.045002 997.44 1060.23 1.000
r(C<->G){all} 0.018233 0.000021 0.009446 0.027433 0.018057 614.75 767.36 1.000
r(C<->T){all} 0.664697 0.000239 0.631770 0.692145 0.665073 552.96 555.92 1.000
r(G<->T){all} 0.032381 0.000033 0.021609 0.043769 0.032138 775.72 856.26 1.000
pi(A){all} 0.343073 0.000069 0.326207 0.358382 0.343094 683.82 980.92 1.000
pi(C){all} 0.214483 0.000048 0.200844 0.227913 0.214231 818.70 829.95 1.000
pi(G){all} 0.244550 0.000059 0.228715 0.258513 0.244632 646.25 751.17 1.000
pi(T){all} 0.197895 0.000043 0.184897 0.210508 0.197931 711.05 866.14 1.000
alpha{1,2} 0.196855 0.000106 0.177159 0.216743 0.196524 1230.72 1259.27 1.000
alpha{3} 4.179512 0.547246 2.772413 5.593344 4.091450 1346.03 1367.73 1.001
pinvar{all} 0.087943 0.000315 0.052177 0.121557 0.087222 1084.47 1087.20 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 -11404.48168
Model 2: PositiveSelection -11404.48168
Model 0: one-ratio -11439.671203
Model 3: discrete -11263.455063
Model 7: beta -11265.175861
Model 8: beta&w>1 -11265.180711
Model 0 vs 1 70.3790459999982
Model 2 vs 1 0.0
Model 8 vs 7 0.009700000002339948
