--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Thu Jul 12 12:02:52 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_2/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N3/NS4B_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N3/NS4B_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_N3/NS4B_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -5934.26 -5985.98
2 -5934.13 -5983.21
--------------------------------------
TOTAL -5934.19 -5985.35
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N3/NS4B_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N3/NS4B_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_N3/NS4B_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} 7.130853 0.242613 6.226398 8.133440 7.117942 684.86 709.90 1.000
r(A<->C){all} 0.043035 0.000058 0.028465 0.058338 0.042501 831.72 878.21 1.000
r(A<->G){all} 0.210947 0.000372 0.174128 0.246675 0.210451 510.74 567.24 1.002
r(A<->T){all} 0.053843 0.000076 0.037199 0.070980 0.053501 715.51 754.31 1.000
r(C<->G){all} 0.017848 0.000050 0.005264 0.032536 0.017337 667.13 695.22 1.002
r(C<->T){all} 0.641040 0.000553 0.594108 0.685189 0.641833 593.59 631.39 1.002
r(G<->T){all} 0.033286 0.000071 0.017579 0.049779 0.032963 835.99 859.96 1.000
pi(A){all} 0.331237 0.000147 0.307417 0.354240 0.331043 855.08 858.35 1.000
pi(C){all} 0.231763 0.000108 0.212648 0.253474 0.231503 730.49 754.49 1.000
pi(G){all} 0.217931 0.000119 0.197111 0.239314 0.217740 822.35 860.33 1.000
pi(T){all} 0.219070 0.000100 0.199762 0.239221 0.219003 885.79 940.38 1.000
alpha{1,2} 0.185301 0.000156 0.161498 0.209716 0.184866 1106.00 1199.04 1.000
alpha{3} 4.042145 0.693543 2.550689 5.717268 3.953438 1501.00 1501.00 1.000
pinvar{all} 0.138047 0.000810 0.081720 0.193177 0.137138 1247.40 1374.20 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 -5013.134826
Model 2: PositiveSelection -5013.134889
Model 0: one-ratio -5053.102365
Model 3: discrete -4963.271977
Model 7: beta -4967.482043
Model 8: beta&w>1 -4967.483588
Model 0 vs 1 79.93507799999861
Model 2 vs 1 1.2599999899975955E-4
Model 8 vs 7 0.0030900000001565786
