--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Wed May 02 17:24:17 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/E_4/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N1/E_4/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_4/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_4/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -12489.33 -12533.25
2 -12489.32 -12532.48
--------------------------------------
TOTAL -12489.33 -12532.94
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N1/E_4/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_4/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_4/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 9.377637 0.305680 8.304878 10.455240 9.367509 565.09 622.74 1.000
r(A<->C){all} 0.036340 0.000023 0.027334 0.045985 0.036102 770.85 829.97 1.000
r(A<->G){all} 0.180672 0.000145 0.157676 0.204431 0.180585 522.38 560.57 1.000
r(A<->T){all} 0.047690 0.000031 0.037667 0.059188 0.047463 905.86 948.90 1.000
r(C<->G){all} 0.023142 0.000023 0.014076 0.032760 0.022939 716.08 767.06 1.000
r(C<->T){all} 0.690025 0.000241 0.660586 0.720244 0.689863 531.29 567.51 1.000
r(G<->T){all} 0.022130 0.000026 0.012244 0.031987 0.021993 649.60 657.59 1.000
pi(A){all} 0.346094 0.000072 0.330189 0.363424 0.346069 809.37 829.36 1.000
pi(C){all} 0.215244 0.000050 0.202405 0.228932 0.215026 611.90 732.06 1.000
pi(G){all} 0.240989 0.000060 0.226643 0.256652 0.240721 692.51 715.53 1.001
pi(T){all} 0.197673 0.000047 0.182875 0.210126 0.197525 720.06 758.54 1.000
alpha{1,2} 0.193925 0.000100 0.174806 0.213071 0.193206 1092.90 1142.00 1.000
alpha{3} 4.544400 0.618034 3.030927 5.974584 4.464177 1501.00 1501.00 1.000
pinvar{all} 0.077590 0.000282 0.046023 0.110009 0.076672 1277.18 1350.00 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 -11824.173141
Model 2: PositiveSelection -11824.173141
Model 0: one-ratio -11850.499226
Model 3: discrete -11677.499809
Model 7: beta -11678.227011
Model 8: beta&w>1 -11678.230481
Model 0 vs 1 52.652170000001206
Model 2 vs 1 0.0
Model 8 vs 7 0.006939999999303836
