--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Tue Nov 22 09:10:38 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/3/acj6-PK/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/3/acj6-PK/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PK/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/3/acj6-PK/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -2163.86         -2186.31
2      -2163.89         -2181.96
--------------------------------------
TOTAL    -2163.87         -2185.63
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/3/acj6-PK/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PK/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/3/acj6-PK/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         0.318417    0.002898    0.218979    0.430674    0.314010    930.08   1177.20    1.000
r(A<->C){all}   0.081192    0.001023    0.022479    0.144900    0.078688    830.83    922.44    1.000
r(A<->G){all}   0.248432    0.003789    0.129989    0.368342    0.244852    556.63    588.20    1.000
r(A<->T){all}   0.172115    0.002893    0.068418    0.274738    0.167887    699.42    725.58    1.000
r(C<->G){all}   0.059393    0.000415    0.024329    0.100519    0.057335    858.53    924.07    1.000
r(C<->T){all}   0.427720    0.004985    0.284940    0.555764    0.424244    603.37    717.45    1.000
r(G<->T){all}   0.011147    0.000131    0.000001    0.034025    0.007483    926.03    964.16    1.000
pi(A){all}      0.245664    0.000165    0.219292    0.269241    0.245351   1250.58   1269.21    1.000
pi(C){all}      0.304552    0.000180    0.279486    0.332543    0.304513   1274.57   1278.42    1.000
pi(G){all}      0.265579    0.000166    0.242424    0.292737    0.264790   1069.89   1128.59    1.001
pi(T){all}      0.184205    0.000120    0.163615    0.206490    0.184061   1193.97   1214.33    1.000
alpha{1,2}      0.054752    0.000851    0.000102    0.099357    0.058052    975.17   1105.37    1.000
alpha{3}        2.197450    0.588341    0.947330    3.794628    2.084394   1163.78   1300.12    1.000
pinvar{all}     0.779979    0.000660    0.730139    0.830188    0.781148   1293.69   1367.36    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	-2076.210814
Model 2: PositiveSelection	-2076.10255
Model 0: one-ratio	-2081.247505
Model 3: discrete	-2076.10255
Model 7: beta	-2078.940124
Model 8: beta&w>1	-2076.102176


Model 0 vs 1	10.073381999999583

Model 2 vs 1	0.21652799999992567

Model 8 vs 7	5.675896000000648