--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Mon Apr 30 19:04:11 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/E_2/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N1/E_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_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/E_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -12469.67 -12516.65
2 -12468.79 -12509.57
--------------------------------------
TOTAL -12469.14 -12515.96
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N1/E_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_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/E_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} 9.399429 0.302178 8.284445 10.476200 9.384797 538.51 565.01 1.000
r(A<->C){all} 0.034689 0.000025 0.025598 0.045019 0.034601 803.04 873.14 1.001
r(A<->G){all} 0.176774 0.000152 0.151480 0.199359 0.176845 554.52 574.07 1.000
r(A<->T){all} 0.050497 0.000034 0.039501 0.062356 0.050272 615.75 655.71 1.001
r(C<->G){all} 0.017198 0.000020 0.008373 0.025501 0.016938 693.06 806.78 1.001
r(C<->T){all} 0.696792 0.000243 0.668569 0.728087 0.697019 549.55 562.10 1.000
r(G<->T){all} 0.024051 0.000024 0.014532 0.033421 0.023871 852.02 933.77 1.000
pi(A){all} 0.350061 0.000072 0.334498 0.367593 0.349939 648.59 747.13 1.000
pi(C){all} 0.213442 0.000049 0.199398 0.226402 0.213687 456.09 613.65 1.000
pi(G){all} 0.242822 0.000061 0.227250 0.257754 0.242826 743.92 794.44 1.000
pi(T){all} 0.193676 0.000045 0.179503 0.205815 0.193549 625.52 727.58 1.000
alpha{1,2} 0.197264 0.000098 0.177429 0.216286 0.196838 871.13 1121.80 1.000
alpha{3} 4.842499 0.690217 3.356065 6.466790 4.770707 1034.34 1201.16 1.000
pinvar{all} 0.093876 0.000301 0.061314 0.127653 0.093513 788.23 914.69 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 -11942.430876
Model 2: PositiveSelection -11942.430881
Model 0: one-ratio -11981.93516
Model 3: discrete -11801.936554
Model 7: beta -11802.700904
Model 8: beta&w>1 -11802.662628
Model 0 vs 1 79.0085680000011
Model 2 vs 1 9.999999747378752E-6
Model 8 vs 7 0.07655199999862816
