--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Thu Jun 14 22:09:19 WEST 2018
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/DNG_A2/NS4B_5/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_A2/NS4B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A2/NS4B_5/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/DNG_A2/NS4B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -7467.41 -7516.01
2 -7464.50 -7512.42
--------------------------------------
TOTAL -7465.14 -7515.34
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_A2/NS4B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A2/NS4B_5/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/DNG_A2/NS4B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 7.322376 0.186984 6.474480 8.135656 7.312701 1156.20 1190.93 1.001
r(A<->C){all} 0.037522 0.000038 0.025807 0.050273 0.037108 817.51 935.27 1.001
r(A<->G){all} 0.212940 0.000291 0.182118 0.247364 0.212442 442.91 495.36 1.006
r(A<->T){all} 0.058910 0.000052 0.045724 0.074349 0.058625 981.65 1013.67 1.000
r(C<->G){all} 0.038997 0.000051 0.025648 0.052632 0.038588 996.41 1001.54 1.000
r(C<->T){all} 0.606584 0.000474 0.565596 0.649776 0.606954 452.50 526.65 1.004
r(G<->T){all} 0.045046 0.000058 0.030282 0.059832 0.044817 790.03 850.12 1.000
pi(A){all} 0.330880 0.000144 0.307030 0.354097 0.330971 970.44 1026.17 1.001
pi(C){all} 0.234722 0.000104 0.215365 0.255045 0.234564 510.61 756.17 1.002
pi(G){all} 0.216845 0.000109 0.195929 0.236524 0.216808 662.35 770.36 1.001
pi(T){all} 0.217553 0.000089 0.200942 0.237516 0.217342 754.25 858.52 1.001
alpha{1,2} 0.228270 0.000245 0.199082 0.259336 0.227648 1032.85 1148.72 1.000
alpha{3} 5.266272 0.970359 3.563336 7.146472 5.153153 1249.02 1323.47 1.000
pinvar{all} 0.114020 0.000674 0.065888 0.165313 0.113516 1308.42 1320.39 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 -6974.551581
Model 2: PositiveSelection -6974.551584
Model 0: one-ratio -7039.512939
Model 3: discrete -6924.165023
Model 7: beta -6930.365376
Model 8: beta&w>1 -6930.367753
Model 0 vs 1 129.92271600000095
Model 2 vs 1 6.000000212225132E-6
Model 8 vs 7 0.004754000001412351
