--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Fri May 04 08:39:17 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_1/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N1/NS1_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/NS1_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_N1/NS1_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -8288.60 -8339.00
2 -8289.73 -8335.05
--------------------------------------
TOTAL -8289.01 -8338.33
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N1/NS1_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/NS1_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_N1/NS1_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} 7.629462 0.264793 6.664328 8.648123 7.615478 626.90 630.16 1.000
r(A<->C){all} 0.027256 0.000031 0.016680 0.038603 0.027068 915.24 920.38 1.000
r(A<->G){all} 0.214614 0.000290 0.180679 0.246830 0.214410 431.54 540.16 1.000
r(A<->T){all} 0.054603 0.000054 0.040500 0.069083 0.054372 677.09 782.05 1.000
r(C<->G){all} 0.026701 0.000044 0.014042 0.039669 0.026263 542.31 740.79 1.000
r(C<->T){all} 0.657788 0.000427 0.619472 0.700583 0.657409 402.98 504.74 1.000
r(G<->T){all} 0.019037 0.000043 0.006703 0.031868 0.018747 750.48 751.18 1.000
pi(A){all} 0.347989 0.000109 0.328535 0.369582 0.347952 678.08 788.42 1.001
pi(C){all} 0.230371 0.000083 0.213430 0.248975 0.230040 938.95 1006.28 1.001
pi(G){all} 0.224151 0.000089 0.207534 0.243785 0.223809 672.00 819.42 1.000
pi(T){all} 0.197489 0.000063 0.182538 0.213198 0.197507 464.53 617.41 1.000
alpha{1,2} 0.194319 0.000141 0.172632 0.218273 0.193720 1146.54 1259.02 1.000
alpha{3} 4.908844 0.902357 3.132158 6.668747 4.812321 1128.50 1314.75 1.000
pinvar{all} 0.142613 0.000575 0.097070 0.187134 0.141572 923.34 1029.30 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 -7921.44414
Model 2: PositiveSelection -7921.44414
Model 0: one-ratio -8031.533019
Model 3: discrete -7827.202985
Model 7: beta -7831.309872
Model 8: beta&w>1 -7829.697256
Model 0 vs 1 220.17775800000163
Model 2 vs 1 0.0
Model 8 vs 7 3.2252319999988686
