--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Tue Dec 06 14:34:16 WET 2016
codeml.models=0 1 2 3 7 8
mrbayes.mpich=
mrbayes.ngen=1000000
tcoffee.alignMethod=CLUSTALW2
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/ADOPS/398/Snr1-PA/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/398/Snr1-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/398/Snr1-PA/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/398/Snr1-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -3915.80 -3932.67
2 -3915.01 -3934.92
--------------------------------------
TOTAL -3915.33 -3934.33
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS/398/Snr1-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/398/Snr1-PA/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/398/Snr1-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 1.521540 0.012600 1.300875 1.741680 1.519128 1361.15 1431.08 1.000
r(A<->C){all} 0.092973 0.000244 0.062300 0.123825 0.091755 1026.57 1031.84 1.001
r(A<->G){all} 0.282576 0.000915 0.226149 0.343229 0.282244 732.61 799.03 1.001
r(A<->T){all} 0.081228 0.000637 0.036217 0.132752 0.079598 751.12 830.59 1.000
r(C<->G){all} 0.027221 0.000045 0.014868 0.040402 0.026887 937.75 977.65 1.000
r(C<->T){all} 0.430241 0.001262 0.363098 0.499345 0.430170 731.96 841.11 1.000
r(G<->T){all} 0.085761 0.000246 0.056248 0.116897 0.084894 807.64 880.40 1.001
pi(A){all} 0.212137 0.000138 0.188992 0.234845 0.212444 783.63 987.43 1.001
pi(C){all} 0.334413 0.000171 0.309296 0.360938 0.334221 992.80 1072.73 1.001
pi(G){all} 0.288570 0.000162 0.265838 0.315657 0.288238 1044.02 1101.56 1.000
pi(T){all} 0.164881 0.000092 0.145679 0.183041 0.164589 1077.93 1101.53 1.000
alpha{1,2} 0.037695 0.000467 0.000266 0.069867 0.039342 791.46 894.72 1.000
alpha{3} 4.521970 1.049778 2.698380 6.564698 4.413021 1108.47 1267.60 1.001
pinvar{all} 0.250813 0.001370 0.175195 0.319640 0.251034 1361.65 1419.79 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 -3543.510495
Model 2: PositiveSelection -3543.506831
Model 0: one-ratio -3543.506828
Model 3: discrete -3543.506876
Model 7: beta -3543.522495
Model 8: beta&w>1 -3543.522844
Model 0 vs 1 0.007333999999900698
Model 2 vs 1 0.007327999999688473
Model 8 vs 7 6.979999998293351E-4
