--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Sun May 27 20:13:37 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_A1/NS1_5/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_A1/NS1_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS1_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_A1/NS1_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -8480.97 -8524.63
2 -8479.84 -8530.80
--------------------------------------
TOTAL -8480.25 -8530.10
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_A1/NS1_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS1_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_A1/NS1_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} 6.942357 0.211954 6.078477 7.860914 6.920747 589.54 593.27 1.000
r(A<->C){all} 0.033506 0.000032 0.022772 0.044934 0.033260 672.35 735.94 1.000
r(A<->G){all} 0.224353 0.000289 0.192791 0.258018 0.224030 407.19 477.26 1.000
r(A<->T){all} 0.050758 0.000050 0.035939 0.063665 0.050396 715.10 757.96 1.000
r(C<->G){all} 0.024693 0.000041 0.012131 0.036706 0.024273 659.23 669.93 1.000
r(C<->T){all} 0.633661 0.000425 0.591217 0.671762 0.634703 470.58 490.66 1.000
r(G<->T){all} 0.033029 0.000056 0.017917 0.047016 0.032683 610.29 738.50 1.000
pi(A){all} 0.347461 0.000118 0.326581 0.367556 0.347314 758.33 840.36 1.000
pi(C){all} 0.226592 0.000078 0.209564 0.243641 0.226499 986.03 1011.28 1.000
pi(G){all} 0.225673 0.000086 0.208600 0.243877 0.225749 733.33 787.52 1.000
pi(T){all} 0.200274 0.000068 0.183005 0.215301 0.200245 877.85 925.69 1.000
alpha{1,2} 0.207268 0.000177 0.181243 0.233596 0.206991 1267.28 1269.20 1.000
alpha{3} 4.798278 0.834584 3.148953 6.619867 4.721129 1307.19 1368.48 1.000
pinvar{all} 0.138667 0.000557 0.093799 0.184496 0.138248 1095.76 1122.01 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 -8102.207801
Model 2: PositiveSelection -8102.207801
Model 0: one-ratio -8228.634391
Model 3: discrete -8019.318829
Model 7: beta -8024.756597
Model 8: beta&w>1 -8022.914789
Model 0 vs 1 252.85318000000007
Model 2 vs 1 0.0
Model 8 vs 7 3.6836159999984375
