--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Tue Nov 22 08:58:41 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/3/acj6-PJ/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/3/acj6-PJ/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PJ/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/3/acj6-PJ/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -2211.19 -2230.84
2 -2210.98 -2231.54
--------------------------------------
TOTAL -2211.08 -2231.25
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS/3/acj6-PJ/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PJ/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/3/acj6-PJ/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 0.396651 0.003701 0.295318 0.521331 0.390237 1276.35 1335.02 1.000
r(A<->C){all} 0.115081 0.001229 0.049035 0.183334 0.112285 745.12 876.80 1.000
r(A<->G){all} 0.235409 0.002933 0.131444 0.339849 0.230460 827.79 864.87 1.000
r(A<->T){all} 0.126386 0.001867 0.043650 0.210179 0.122094 720.64 722.87 1.000
r(C<->G){all} 0.062252 0.000377 0.028406 0.101082 0.060335 1061.09 1104.27 1.000
r(C<->T){all} 0.449642 0.004082 0.327147 0.575020 0.449460 528.17 648.74 1.000
r(G<->T){all} 0.011231 0.000110 0.000008 0.032123 0.008286 903.77 958.44 1.000
pi(A){all} 0.241380 0.000156 0.218427 0.266851 0.241174 1013.39 1183.39 1.000
pi(C){all} 0.306940 0.000179 0.281624 0.333857 0.306532 1178.50 1252.93 1.000
pi(G){all} 0.272821 0.000178 0.248136 0.300715 0.273184 1065.83 1176.18 1.000
pi(T){all} 0.178860 0.000120 0.157491 0.199860 0.178516 1225.33 1252.68 1.000
alpha{1,2} 0.050017 0.000677 0.000121 0.088401 0.054054 1027.61 1169.60 1.000
alpha{3} 2.324506 0.646379 1.014259 3.946140 2.202287 1461.39 1481.20 1.000
pinvar{all} 0.754197 0.000686 0.701980 0.804399 0.755113 1416.25 1458.62 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 -2116.486373
Model 2: PositiveSelection -2116.483814
Model 0: one-ratio -2116.532034
Model 3: discrete -2116.483814
Model 7: beta -2116.483433
Model 8: beta&w>1 -2116.485896
Model 0 vs 1 0.0913219999993089
Model 2 vs 1 0.005118000000038592
Model 8 vs 7 0.004926000000523345
