--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Tue Nov 29 16:38:20 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/59/CG13024-PB/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/59/CG13024-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/59/CG13024-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/59/CG13024-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -2426.96         -2443.86
2      -2427.10         -2444.07
--------------------------------------
TOTAL    -2427.03         -2443.97
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/59/CG13024-PB/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/59/CG13024-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/59/CG13024-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}         1.004648    0.012456    0.806620    1.234269    0.996298   1217.83   1320.82    1.000
r(A<->C){all}   0.089564    0.000428    0.050870    0.129398    0.087875    915.11    998.83    1.000
r(A<->G){all}   0.210864    0.001282    0.141135    0.280922    0.208585    839.39    864.07    1.000
r(A<->T){all}   0.101494    0.000852    0.047083    0.160701    0.099267   1020.98   1057.71    1.000
r(C<->G){all}   0.084672    0.000297    0.051391    0.118019    0.083938    802.53    954.78    1.000
r(C<->T){all}   0.464835    0.002383    0.370195    0.559038    0.465560    624.50    681.68    1.000
r(G<->T){all}   0.048571    0.000370    0.012783    0.085004    0.046742    994.58   1075.27    1.000
pi(A){all}      0.244072    0.000236    0.213065    0.273137    0.243804    681.79    849.37    1.000
pi(C){all}      0.318011    0.000238    0.288030    0.347349    0.317587   1174.18   1208.64    1.000
pi(G){all}      0.262192    0.000232    0.232789    0.291849    0.262020    992.08   1079.31    1.000
pi(T){all}      0.175725    0.000163    0.149717    0.199258    0.175639   1051.85   1153.16    1.000
alpha{1,2}      0.124867    0.000373    0.089709    0.162963    0.123570   1280.41   1339.82    1.003
alpha{3}        2.846692    0.792181    1.321923    4.649449    2.735073   1240.40   1355.95    1.000
pinvar{all}     0.454350    0.002338    0.353485    0.541223    0.456702   1040.04   1199.42    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	-2075.381039
Model 2: PositiveSelection	-2075.381039
Model 0: one-ratio	-2085.143498
Model 3: discrete	-2072.665669
Model 7: beta	-2074.670976
Model 8: beta&w>1	-2072.86893


Model 0 vs 1	19.52491800000007

Model 2 vs 1	0.0

Model 8 vs 7	3.604091999999582