--- EXPERIMENT NOTES
--- EXPERIMENT PROPERTIES
#Mon Nov 21 14:18:48 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/295/Lmpt-PE/input.fasta
input.names=
mrbayes.params=
codeml.params=
--- PSRF SUMMARY
Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/295/Lmpt-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/295/Lmpt-PE/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/295/Lmpt-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)
Run Arithmetic mean Harmonic mean
--------------------------------------
1 -1995.72 -2013.26
2 -1995.78 -2016.93
--------------------------------------
TOTAL -1995.75 -2016.26
--------------------------------------
Model parameter summaries over the runs sampled in files
"/opt/ADOPS/295/Lmpt-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/295/Lmpt-PE/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/295/Lmpt-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".
95% HPD Interval
--------------------
Parameter Mean Variance Lower Upper Median min ESS* avg ESS PSRF+
------------------------------------------------------------------------------------------------------
TL{all} 0.834238 0.012002 0.627644 1.051122 0.825141 1419.53 1460.27 1.000
r(A<->C){all} 0.080960 0.000492 0.040268 0.124935 0.079031 985.86 1102.20 1.000
r(A<->G){all} 0.220212 0.002522 0.128312 0.320676 0.217389 539.12 646.88 1.000
r(A<->T){all} 0.010102 0.000094 0.000005 0.029522 0.007173 761.61 928.49 1.000
r(C<->G){all} 0.097730 0.000470 0.057155 0.140022 0.095920 773.93 880.28 1.000
r(C<->T){all} 0.562495 0.003140 0.458799 0.674044 0.562662 660.73 706.22 1.000
r(G<->T){all} 0.028500 0.000275 0.000097 0.060267 0.026316 950.92 1043.47 1.000
pi(A){all} 0.225826 0.000218 0.196206 0.253302 0.225463 1225.58 1339.66 1.000
pi(C){all} 0.311483 0.000271 0.278221 0.341525 0.311604 1254.54 1261.33 1.000
pi(G){all} 0.275871 0.000265 0.245869 0.308100 0.275597 1196.40 1260.28 1.000
pi(T){all} 0.186820 0.000176 0.162500 0.214740 0.186298 1094.16 1181.14 1.000
alpha{1,2} 0.085992 0.000275 0.057500 0.119788 0.085828 877.60 1024.87 1.000
alpha{3} 2.224142 0.488473 1.061426 3.648855 2.111553 1329.23 1376.04 1.000
pinvar{all} 0.600376 0.001409 0.527285 0.669611 0.602321 1256.34 1372.71 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 -1818.876561
Model 2: PositiveSelection -1818.874206
Model 0: one-ratio -1818.988065
Model 3: discrete -1818.874206
Model 7: beta -1818.873078
Model 8: beta&w>1 -1818.875434
Model 0 vs 1 0.22300799999993615
Model 2 vs 1 0.004710000000159198
Model 8 vs 7 0.004711999999926775
