--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Tue May 08 11:58:45 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_N2/NS2A_2/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N2/NS2A_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS2A_2/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_N2/NS2A_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -6358.13 -6405.70
2 -6356.96 -6403.10
--------------------------------------
TOTAL -6357.38 -6405.08
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N2/NS2A_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS2A_2/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_N2/NS2A_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 9.548085 0.487475 8.176708 10.891300 9.529109 510.16 617.49 1.000
r(A<->C){all} 0.047911 0.000078 0.031588 0.066215 0.047409 731.62 832.54 1.000
r(A<->G){all} 0.215643 0.000345 0.181863 0.253837 0.215102 600.78 629.96 1.000
r(A<->T){all} 0.047195 0.000076 0.030392 0.064492 0.046880 696.77 822.24 1.001
r(C<->G){all} 0.040415 0.000100 0.021995 0.060317 0.039880 579.31 649.57 1.000
r(C<->T){all} 0.612638 0.000514 0.567190 0.657025 0.612702 554.85 646.32 1.000
r(G<->T){all} 0.036197 0.000081 0.018982 0.053569 0.035502 663.78 794.12 1.000
pi(A){all} 0.312225 0.000132 0.291281 0.335848 0.311960 904.87 918.64 1.000
pi(C){all} 0.210386 0.000093 0.192708 0.230710 0.210216 639.22 650.35 1.000
pi(G){all} 0.241728 0.000114 0.220169 0.261711 0.241632 569.35 638.73 1.000
pi(T){all} 0.235660 0.000106 0.214447 0.254498 0.235511 666.22 814.09 1.000
alpha{1,2} 0.387883 0.001552 0.313329 0.461967 0.384381 1091.64 1159.01 1.000
alpha{3} 3.641500 0.703365 2.120942 5.294857 3.543628 1292.23 1394.14 1.000
pinvar{all} 0.023798 0.000306 0.000001 0.057398 0.020292 1102.29 1139.68 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 -5914.519222
Model 2: PositiveSelection -5914.519222
Model 0: one-ratio -5933.104667
Model 3: discrete -5860.306763
Model 7: beta -5862.582064
Model 8: beta&w>1 -5862.582312
Model 0 vs 1 37.17088999999942
Model 2 vs 1 0.0
Model 8 vs 7 4.959999987477204E-4
