--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Fri Nov 18 20:00:08 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/274/Hsc70-3-PB/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/274/Hsc70-3-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/274/Hsc70-3-PB/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/274/Hsc70-3-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -5363.02         -5379.98
2      -5363.32         -5381.60
--------------------------------------
TOTAL    -5363.16         -5381.09
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/274/Hsc70-3-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/274/Hsc70-3-PB/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/274/Hsc70-3-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         0.675610    0.002300    0.588041    0.772063    0.674169   1188.12   1309.60    1.000
r(A<->C){all}   0.051511    0.000132    0.031488    0.075555    0.050726   1111.65   1194.82    1.001
r(A<->G){all}   0.175306    0.000604    0.129043    0.224259    0.174393    908.78    921.47    1.000
r(A<->T){all}   0.054235    0.000269    0.022471    0.085161    0.052798   1038.10   1052.41    1.001
r(C<->G){all}   0.056266    0.000075    0.040454    0.072783    0.056108    778.46    985.37    1.000
r(C<->T){all}   0.604897    0.000976    0.543389    0.663628    0.605416    814.31    885.24    1.000
r(G<->T){all}   0.057785    0.000130    0.037463    0.081331    0.057229   1026.33   1125.62    1.000
pi(A){all}      0.227010    0.000093    0.208202    0.245889    0.226955    917.83    983.65    1.000
pi(C){all}      0.292458    0.000095    0.272211    0.310319    0.292671    921.38   1117.92    1.000
pi(G){all}      0.292329    0.000102    0.273448    0.311890    0.292104   1237.59   1254.68    1.000
pi(T){all}      0.188203    0.000066    0.174143    0.205252    0.188233   1105.02   1205.77    1.000
alpha{1,2}      0.033334    0.000410    0.000103    0.066781    0.032021   1219.51   1289.50    1.001
alpha{3}        4.103937    0.988982    2.389336    6.087128    4.008867   1350.04   1425.52    1.000
pinvar{all}     0.510990    0.000675    0.457398    0.559969    0.510859   1393.39   1425.73    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	-4932.66204
Model 2: PositiveSelection	-4932.859411
Model 0: one-ratio	-4932.85941
Model 3: discrete	-4932.136197
Model 7: beta	-4932.138108
Model 8: beta&w>1	-4932.140858


Model 0 vs 1	0.39473999999972875

Model 2 vs 1	0.3947420000004058

Model 8 vs 7	0.005499999999301508