--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Fri Nov 25 13:09:30 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/1/14-3-3zeta-PA/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/1/14-3-3zeta-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/1/14-3-3zeta-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/1/14-3-3zeta-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -1543.55 -1561.01
2 -1543.96 -1563.06
--------------------------------------
TOTAL -1543.73 -1562.49
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS/1/14-3-3zeta-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/1/14-3-3zeta-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/1/14-3-3zeta-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} 0.246522 0.001502 0.173765 0.323333 0.242426 1112.86 1306.93 1.000
r(A<->C){all} 0.111993 0.001174 0.051622 0.181044 0.108417 555.21 675.77 1.000
r(A<->G){all} 0.154391 0.001814 0.080132 0.240578 0.149644 749.36 783.88 1.000
r(A<->T){all} 0.066539 0.001037 0.014722 0.132432 0.062067 850.93 910.96 1.000
r(C<->G){all} 0.061977 0.000652 0.015878 0.110995 0.059227 879.61 936.93 1.000
r(C<->T){all} 0.555556 0.004706 0.422485 0.687568 0.559302 715.13 732.62 1.000
r(G<->T){all} 0.049543 0.000750 0.003571 0.102933 0.044281 836.64 901.39 1.000
pi(A){all} 0.285183 0.000249 0.256090 0.318082 0.285053 1159.97 1227.91 1.000
pi(C){all} 0.261794 0.000247 0.233084 0.294652 0.261387 1183.78 1342.39 1.001
pi(G){all} 0.257922 0.000255 0.226904 0.288827 0.257691 974.12 1011.49 1.001
pi(T){all} 0.195101 0.000198 0.167834 0.222710 0.194877 1205.51 1247.99 1.000
alpha{1,2} 0.057345 0.001727 0.000122 0.136091 0.050348 1331.52 1372.07 1.001
alpha{3} 2.109083 0.675405 0.745799 3.711802 1.979228 1296.79 1398.89 1.000
pinvar{all} 0.478803 0.007900 0.292310 0.633813 0.489065 1140.09 1320.54 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 -1451.36118
Model 2: PositiveSelection -1451.36118
Model 0: one-ratio -1458.92382
Model 3: discrete -1451.332921
Model 7: beta -1451.333746
Model 8: beta&w>1 -1451.361179
Model 0 vs 1 15.125279999999748
Model 2 vs 1 0.0
Model 8 vs 7 0.054865999999947235
