--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Thu Jun 07 20:01:12 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_3/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_A2/NS4B_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A2/NS4B_3/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_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -7742.29 -7788.73
2 -7741.85 -7784.54
--------------------------------------
TOTAL -7742.04 -7788.06
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_A2/NS4B_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A2/NS4B_3/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_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 8.021583 0.219675 7.116084 8.947767 8.023963 917.04 1015.19 1.000
r(A<->C){all} 0.032591 0.000028 0.022289 0.043030 0.032328 572.86 758.11 1.000
r(A<->G){all} 0.240391 0.000341 0.206115 0.279873 0.240201 405.58 443.68 1.000
r(A<->T){all} 0.055773 0.000049 0.042826 0.069782 0.055303 814.31 901.33 1.000
r(C<->G){all} 0.021403 0.000032 0.010063 0.031876 0.021164 825.21 861.01 1.000
r(C<->T){all} 0.604692 0.000482 0.559999 0.647217 0.604738 433.18 456.09 1.000
r(G<->T){all} 0.045149 0.000058 0.029885 0.059436 0.044863 803.00 830.90 1.000
pi(A){all} 0.341047 0.000144 0.315999 0.362375 0.341513 862.15 864.67 1.000
pi(C){all} 0.237030 0.000106 0.217276 0.256439 0.236859 674.69 726.25 1.000
pi(G){all} 0.211900 0.000104 0.190293 0.230706 0.211654 663.87 692.94 1.000
pi(T){all} 0.210022 0.000090 0.190071 0.226591 0.209886 686.60 702.70 1.000
alpha{1,2} 0.220068 0.000205 0.192477 0.249390 0.219273 1060.11 1213.98 1.000
alpha{3} 5.719110 1.052021 3.861267 7.748860 5.616145 1152.32 1229.67 1.000
pinvar{all} 0.136102 0.000664 0.087277 0.187880 0.135159 819.56 1035.85 1.001
------------------------------------------------------------------------------------------------------
* 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 -7296.264532
Model 2: PositiveSelection -7296.264537
Model 0: one-ratio -7314.39087
Model 3: discrete -7217.962161
Model 7: beta -7219.24212
Model 8: beta&w>1 -7219.24457
Model 0 vs 1 36.252676000000065
Model 2 vs 1 9.999999747378752E-6
Model 8 vs 7 0.004899999999906868
