--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Sat May 05 01:01:32 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_N1/NS1_2/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N1/NS1_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/NS1_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_N1/NS1_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -8960.72 -9009.30
2 -8961.78 -9005.03
--------------------------------------
TOTAL -8961.12 -9008.62
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N1/NS1_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/NS1_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_N1/NS1_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} 7.792185 0.242197 6.835030 8.748124 7.762955 654.84 673.73 1.001
r(A<->C){all} 0.028577 0.000026 0.019043 0.038783 0.028395 843.15 871.76 1.002
r(A<->G){all} 0.220253 0.000252 0.189133 0.250636 0.220001 519.42 590.57 1.005
r(A<->T){all} 0.055693 0.000047 0.042811 0.069122 0.055311 889.61 894.53 1.000
r(C<->G){all} 0.023263 0.000032 0.013112 0.034483 0.022897 775.46 831.79 1.000
r(C<->T){all} 0.653654 0.000349 0.619141 0.690421 0.653577 561.56 585.36 1.007
r(G<->T){all} 0.018559 0.000036 0.007671 0.031189 0.018216 630.06 766.25 1.000
pi(A){all} 0.344289 0.000107 0.325413 0.365957 0.344333 800.27 880.45 1.001
pi(C){all} 0.231198 0.000075 0.214244 0.248421 0.231148 504.15 741.29 1.003
pi(G){all} 0.230409 0.000083 0.212691 0.247952 0.230384 845.47 853.79 1.000
pi(T){all} 0.194104 0.000061 0.179200 0.209735 0.194227 845.09 964.00 1.000
alpha{1,2} 0.193714 0.000138 0.170727 0.216343 0.193170 1230.80 1242.77 1.000
alpha{3} 5.803558 1.152172 3.896037 7.963801 5.661483 1501.00 1501.00 1.000
pinvar{all} 0.135554 0.000507 0.093593 0.180917 0.134787 987.77 1152.01 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 -8598.078479
Model 2: PositiveSelection -8598.078479
Model 0: one-ratio -8702.716886
Model 3: discrete -8491.209645
Model 7: beta -8494.419795
Model 8: beta&w>1 -8492.756939
Model 0 vs 1 209.27681400000074
Model 2 vs 1 0.0
Model 8 vs 7 3.3257119999980205
