--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Wed Nov 08 10:32:55 WET 2017
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/ZikaADOPSresults/pr/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/ZikaADOPSresults/pr/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/ZikaADOPSresults/pr/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/ZikaADOPSresults/pr/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -941.72 -979.63
2 -941.91 -984.24
--------------------------------------
TOTAL -941.81 -983.56
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/ZikaADOPSresults/pr/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/ZikaADOPSresults/pr/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/ZikaADOPSresults/pr/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 1.284241 0.071519 0.819832 1.798684 1.243649 533.06 719.34 1.002
r(A<->C){all} 0.052870 0.000491 0.014432 0.096465 0.050331 651.63 694.94 1.003
r(A<->G){all} 0.182709 0.003526 0.076589 0.300048 0.176887 362.76 393.55 1.000
r(A<->T){all} 0.016335 0.000170 0.000001 0.040703 0.013252 439.45 587.58 1.000
r(C<->G){all} 0.009389 0.000078 0.000003 0.026448 0.006890 744.40 819.67 1.000
r(C<->T){all} 0.716030 0.005516 0.565985 0.850447 0.720446 333.78 384.20 1.001
r(G<->T){all} 0.022666 0.000152 0.002607 0.047007 0.020660 362.65 614.03 1.000
pi(A){all} 0.261494 0.000570 0.216056 0.308144 0.260412 1076.43 1094.60 1.000
pi(C){all} 0.215484 0.000519 0.173447 0.261379 0.215106 513.09 822.57 1.000
pi(G){all} 0.290855 0.000629 0.239508 0.336884 0.291105 1099.73 1165.36 1.000
pi(T){all} 0.232167 0.000543 0.189275 0.279048 0.231033 968.25 1088.37 1.000
alpha{1,2} 0.160170 0.001649 0.091637 0.245280 0.155157 1114.69 1175.96 1.000
alpha{3} 1.655989 0.479472 0.530707 3.007114 1.530987 996.14 998.82 1.001
pinvar{all} 0.210514 0.009180 0.009376 0.365474 0.216725 893.98 927.94 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 -913.421485
Model 2: PositiveSelection -913.421485
Model 0: one-ratio -915.624986
Model 3: discrete -911.681233
Model 7: beta -912.031149
Model 8: beta&w>1 -912.031209
Model 0 vs 1 4.407002000000148
Model 2 vs 1 0.0
Model 8 vs 7 1.199999999244028E-4
