--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Fri Nov 11 18:03:19 WET 2016
codeml.models=0 1 2 3 7 8
mrbayes.mpich=
mrbayes.ngen=1000000
tcoffee.alignMethod=CLUSTALW2
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/ADOPS/2/Aac11-PA/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/2/Aac11-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/Aac11-PA/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/ADOPS/2/Aac11-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -5664.66 -5681.13
2 -5665.18 -5680.27
--------------------------------------
TOTAL -5664.89 -5680.79
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS/2/Aac11-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/Aac11-PA/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/ADOPS/2/Aac11-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 1.064657 0.004166 0.943744 1.196554 1.063627 1330.10 1415.55 1.000
r(A<->C){all} 0.090556 0.000149 0.068005 0.115320 0.089989 980.40 1106.95 1.000
r(A<->G){all} 0.303849 0.000628 0.255456 0.352896 0.303344 736.80 835.50 1.000
r(A<->T){all} 0.102790 0.000245 0.072519 0.132133 0.102412 1060.12 1142.03 1.000
r(C<->G){all} 0.037005 0.000060 0.023233 0.054767 0.036560 986.85 1048.91 1.000
r(C<->T){all} 0.405614 0.000792 0.352312 0.460548 0.404972 793.56 897.46 1.001
r(G<->T){all} 0.060187 0.000132 0.038672 0.083488 0.059463 967.12 1093.05 1.001
pi(A){all} 0.290347 0.000108 0.271145 0.311980 0.290311 1020.88 1133.16 1.000
pi(C){all} 0.252162 0.000103 0.232823 0.272160 0.252018 1269.38 1269.93 1.000
pi(G){all} 0.262078 0.000107 0.242352 0.282866 0.261918 1252.44 1264.73 1.000
pi(T){all} 0.195413 0.000079 0.178880 0.213858 0.195430 1081.73 1120.04 1.000
alpha{1,2} 0.121691 0.000097 0.103460 0.141800 0.121110 1320.65 1378.87 1.000
alpha{3} 5.247777 1.306284 3.174832 7.501634 5.114628 1256.02 1336.30 1.000
pinvar{all} 0.346297 0.000957 0.279314 0.401489 0.348008 1276.98 1311.93 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 -5172.087895
Model 2: PositiveSelection -5172.087953
Model 0: one-ratio -5202.200571
Model 3: discrete -5167.94669
Model 7: beta -5169.82779
Model 8: beta&w>1 -5169.716646
Model 0 vs 1 60.225352000001294
Model 2 vs 1 1.160000010713702E-4
Model 8 vs 7 0.22228800000084448
