--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Wed Jun 06 20:23:35 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_1/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_A2/NS4B_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A2/NS4B_1/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_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -6813.63 -6854.34
2 -6814.03 -6858.11
--------------------------------------
TOTAL -6813.81 -6857.44
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_A2/NS4B_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A2/NS4B_1/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_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 7.406155 0.243577 6.470552 8.389127 7.378601 521.14 675.08 1.000
r(A<->C){all} 0.042358 0.000044 0.029682 0.055713 0.042102 1040.30 1055.44 1.000
r(A<->G){all} 0.229109 0.000409 0.191687 0.270525 0.228701 447.46 529.61 1.003
r(A<->T){all} 0.045964 0.000052 0.032257 0.060238 0.045681 844.06 936.99 1.000
r(C<->G){all} 0.024975 0.000044 0.011133 0.036755 0.024607 755.26 794.70 1.000
r(C<->T){all} 0.628463 0.000580 0.581426 0.676041 0.628574 433.06 496.66 1.003
r(G<->T){all} 0.029131 0.000049 0.016381 0.042947 0.028720 750.37 853.79 1.000
pi(A){all} 0.323952 0.000145 0.299246 0.346210 0.323831 917.93 966.09 1.000
pi(C){all} 0.234900 0.000113 0.214989 0.256182 0.234884 738.83 772.44 1.000
pi(G){all} 0.216447 0.000117 0.195068 0.237067 0.216488 710.28 739.26 1.001
pi(T){all} 0.224702 0.000104 0.205971 0.245145 0.224682 737.50 769.69 1.000
alpha{1,2} 0.219194 0.000223 0.191989 0.249530 0.218263 1108.28 1211.35 1.000
alpha{3} 5.023086 0.928869 3.255558 6.968522 4.913312 1190.47 1300.96 1.000
pinvar{all} 0.141693 0.000803 0.088722 0.196926 0.141794 1018.67 1184.57 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 -6355.813867
Model 2: PositiveSelection -6355.813867
Model 0: one-ratio -6396.472561
Model 3: discrete -6288.205742
Model 7: beta -6290.690906
Model 8: beta&w>1 -6290.692233
Model 0 vs 1 81.31738799999948
Model 2 vs 1 0.0
Model 8 vs 7 0.0026539999998931307
