--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Tue Nov 22 09:25:22 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-PL/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/3/acj6-PL/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PL/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-PL/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -2298.26 -2318.69
2 -2298.43 -2317.24
--------------------------------------
TOTAL -2298.34 -2318.21
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS/3/acj6-PL/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PL/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-PL/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 0.385422 0.003186 0.285139 0.501020 0.382228 1256.91 1270.69 1.000
r(A<->C){all} 0.117348 0.001195 0.053253 0.186696 0.114094 777.46 817.87 1.000
r(A<->G){all} 0.260672 0.003090 0.155466 0.363654 0.256585 551.21 719.48 1.000
r(A<->T){all} 0.122000 0.001662 0.051686 0.206977 0.118424 748.03 786.59 1.000
r(C<->G){all} 0.063051 0.000408 0.024987 0.103635 0.060645 843.36 965.71 1.002
r(C<->T){all} 0.426503 0.003724 0.309816 0.545263 0.424279 605.28 705.98 1.000
r(G<->T){all} 0.010426 0.000095 0.000007 0.029784 0.007550 897.68 1025.79 1.001
pi(A){all} 0.239497 0.000153 0.215039 0.262443 0.239234 1234.44 1241.30 1.000
pi(C){all} 0.303275 0.000174 0.278360 0.329285 0.303265 1166.91 1250.79 1.000
pi(G){all} 0.270450 0.000176 0.243600 0.295813 0.270446 1157.68 1165.09 1.000
pi(T){all} 0.186778 0.000117 0.166299 0.208001 0.186486 1178.36 1245.65 1.000
alpha{1,2} 0.046951 0.000666 0.000101 0.087423 0.049117 1057.99 1154.03 1.000
alpha{3} 2.367067 0.660360 1.003678 3.947117 2.222249 1408.85 1454.92 1.000
pinvar{all} 0.765000 0.000593 0.717879 0.811266 0.765715 1366.61 1411.82 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 -2207.775789
Model 2: PositiveSelection -2207.773192
Model 0: one-ratio -2207.820215
Model 3: discrete -2207.773192
Model 7: beta -2207.772726
Model 8: beta&w>1 -2207.775324
Model 0 vs 1 0.0888520000007702
Model 2 vs 1 0.005193999999391963
Model 8 vs 7 0.005196000000069034
