--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Tue Nov 22 09:25:22 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-PL/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

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

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -2298.26         -2318.69
2      -2298.43         -2317.24
--------------------------------------
TOTAL    -2298.34         -2318.21
--------------------------------------


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

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         0.385422    0.003186    0.285139    0.501020    0.382228   1256.91   1270.69    1.000
r(A<->C){all}   0.117348    0.001195    0.053253    0.186696    0.114094    777.46    817.87    1.000
r(A<->G){all}   0.260672    0.003090    0.155466    0.363654    0.256585    551.21    719.48    1.000
r(A<->T){all}   0.122000    0.001662    0.051686    0.206977    0.118424    748.03    786.59    1.000
r(C<->G){all}   0.063051    0.000408    0.024987    0.103635    0.060645    843.36    965.71    1.002
r(C<->T){all}   0.426503    0.003724    0.309816    0.545263    0.424279    605.28    705.98    1.000
r(G<->T){all}   0.010426    0.000095    0.000007    0.029784    0.007550    897.68   1025.79    1.001
pi(A){all}      0.239497    0.000153    0.215039    0.262443    0.239234   1234.44   1241.30    1.000
pi(C){all}      0.303275    0.000174    0.278360    0.329285    0.303265   1166.91   1250.79    1.000
pi(G){all}      0.270450    0.000176    0.243600    0.295813    0.270446   1157.68   1165.09    1.000
pi(T){all}      0.186778    0.000117    0.166299    0.208001    0.186486   1178.36   1245.65    1.000
alpha{1,2}      0.046951    0.000666    0.000101    0.087423    0.049117   1057.99   1154.03    1.000
alpha{3}        2.367067    0.660360    1.003678    3.947117    2.222249   1408.85   1454.92    1.000
pinvar{all}     0.765000    0.000593    0.717879    0.811266    0.765715   1366.61   1411.82    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	-2207.775789
Model 2: PositiveSelection	-2207.773192
Model 0: one-ratio	-2207.820215
Model 3: discrete	-2207.773192
Model 7: beta	-2207.772726
Model 8: beta&w>1	-2207.775324


Model 0 vs 1	0.0888520000007702

Model 2 vs 1	0.005193999999391963

Model 8 vs 7	0.005196000000069034