--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Sat Jul 14 17:58:13 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_N3/NS4B_5/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N3/NS4B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N3/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_N3/NS4B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -5689.01 -5737.23
2 -5690.32 -5740.51
--------------------------------------
TOTAL -5689.46 -5739.85
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N3/NS4B_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N3/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_N3/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} 6.832959 0.233609 5.931382 7.798695 6.823122 570.30 736.69 1.002
r(A<->C){all} 0.040638 0.000058 0.026585 0.056070 0.040369 891.07 969.76 1.000
r(A<->G){all} 0.188175 0.000323 0.153356 0.224146 0.187574 727.84 737.74 1.001
r(A<->T){all} 0.046890 0.000070 0.030528 0.063644 0.046546 805.41 845.57 1.000
r(C<->G){all} 0.037397 0.000080 0.020284 0.054620 0.037162 779.82 794.00 1.000
r(C<->T){all} 0.668077 0.000527 0.622085 0.711732 0.668232 670.73 683.24 1.002
r(G<->T){all} 0.018823 0.000047 0.006274 0.032361 0.018129 692.83 800.76 1.000
pi(A){all} 0.333964 0.000153 0.309510 0.356708 0.334089 981.75 984.73 1.000
pi(C){all} 0.233548 0.000104 0.214836 0.253613 0.233208 984.44 989.05 1.000
pi(G){all} 0.216720 0.000123 0.194423 0.236870 0.216726 883.67 908.64 1.000
pi(T){all} 0.215767 0.000102 0.196737 0.236394 0.215845 950.88 954.41 1.000
alpha{1,2} 0.181292 0.000184 0.156042 0.208022 0.180678 1347.44 1424.22 1.000
alpha{3} 4.918173 1.001275 3.107344 6.857062 4.828633 1355.50 1400.59 1.000
pinvar{all} 0.131331 0.000907 0.071104 0.187147 0.131162 1050.07 1175.54 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 -5107.099971
Model 2: PositiveSelection -5107.099971
Model 0: one-ratio -5141.931236
Model 3: discrete -5040.227209
Model 7: beta -5042.357472
Model 8: beta&w>1 -5042.359827
Model 0 vs 1 69.66253000000142
Model 2 vs 1 0.0
Model 8 vs 7 0.004710000001068693
