--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Sat Oct 07 11:55:40 WEST 2017
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=/usr/bin/
input.sequences=
mrbayes.pburnin=2500
mrbayes.bin=mb
tcoffee.bin=t_coffee
mrbayes.dir=/usr/bin/
tcoffee.dir=
tcoffee.minScore=3
input.fasta=/opt/ADOPS/DATA/Zika/B2_A/Zika-NS1_3/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/DATA/Zika/B2_A/Zika-NS1_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/DATA/Zika/B2_A/Zika-NS1_3/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/ADOPS/DATA/Zika/B2_A/Zika-NS1_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -4124.84 -4206.67
2 -4141.24 -4211.12
--------------------------------------
TOTAL -4125.53 -4210.44
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS/DATA/Zika/B2_A/Zika-NS1_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/DATA/Zika/B2_A/Zika-NS1_3/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/ADOPS/DATA/Zika/B2_A/Zika-NS1_3/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 16.912742 2.151838 13.946200 19.710080 16.876350 711.01 755.22 1.000
r(A<->C){all} 0.026113 0.000058 0.012480 0.040609 0.025414 578.87 622.49 1.001
r(A<->G){all} 0.241520 0.001029 0.183967 0.310206 0.240246 388.38 399.46 1.002
r(A<->T){all} 0.031994 0.000078 0.014953 0.048542 0.031066 510.59 661.83 1.002
r(C<->G){all} 0.016535 0.000041 0.005562 0.029630 0.015891 725.12 763.36 1.001
r(C<->T){all} 0.664716 0.001273 0.592778 0.732181 0.665640 368.91 381.01 1.000
r(G<->T){all} 0.019123 0.000049 0.006420 0.032900 0.018211 548.02 650.33 1.000
pi(A){all} 0.284204 0.000165 0.257874 0.308473 0.284109 819.80 879.85 1.001
pi(C){all} 0.209433 0.000121 0.187924 0.231386 0.209232 592.28 770.67 1.000
pi(G){all} 0.301573 0.000175 0.273809 0.326595 0.301572 586.85 714.07 1.003
pi(T){all} 0.204789 0.000119 0.183758 0.226283 0.204479 722.98 772.40 1.000
alpha{1,2} 0.069356 0.000008 0.063477 0.074324 0.069329 726.99 777.30 1.000
alpha{3} 0.240504 0.000139 0.218371 0.263493 0.239606 645.28 689.71 1.000
pinvar{all} 0.443283 0.001016 0.380726 0.504553 0.444082 838.87 931.87 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 -3348.764702
Model 2: PositiveSelection -3348.764702
Model 0: one-ratio -3362.117927
Model 3: discrete -3343.552193
Model 7: beta -3343.93651
Model 8: beta&w>1 -3343.939139
Model 0 vs 1 26.706449999999677
Model 2 vs 1 0.0
Model 8 vs 7 0.005258000000139873
