--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Fri Nov 18 13:02:12 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/285/KCNQ-PF/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/285/KCNQ-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/285/KCNQ-PF/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/285/KCNQ-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -8335.25         -8351.01
2      -8334.89         -8349.95
--------------------------------------
TOTAL    -8335.05         -8350.62
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/285/KCNQ-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/285/KCNQ-PF/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/285/KCNQ-PF/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         0.478603    0.000794    0.425663    0.534374    0.477467   1447.15   1474.08    1.000
r(A<->C){all}   0.121920    0.000207    0.094675    0.150640    0.121122   1056.03   1075.44    1.000
r(A<->G){all}   0.242744    0.000431    0.201016    0.281522    0.243286    942.10   1037.61    1.001
r(A<->T){all}   0.110841    0.000281    0.078035    0.142393    0.110274   1017.18   1150.04    1.000
r(C<->G){all}   0.056664    0.000073    0.040901    0.074161    0.056389   1097.71   1280.27    1.000
r(C<->T){all}   0.368143    0.000598    0.322158    0.418156    0.367258    969.37    969.42    1.000
r(G<->T){all}   0.099688    0.000170    0.074091    0.125115    0.099183   1338.65   1346.53    1.000
pi(A){all}      0.236720    0.000057    0.222977    0.252505    0.236776   1272.95   1301.04    1.000
pi(C){all}      0.281530    0.000061    0.267393    0.297992    0.281427   1252.06   1261.10    1.000
pi(G){all}      0.276988    0.000062    0.261323    0.291912    0.276973   1221.42   1271.18    1.000
pi(T){all}      0.204762    0.000048    0.191686    0.218691    0.204842    944.42   1143.65    1.000
alpha{1,2}      0.145631    0.000353    0.111322    0.185121    0.144944   1045.39   1124.33    1.001
alpha{3}        3.903405    1.025989    2.058386    5.901816    3.790650   1265.98   1383.49    1.000
pinvar{all}     0.499516    0.001004    0.434901    0.558415    0.501322   1145.57   1195.00    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	-7615.184325
Model 2: PositiveSelection	-7615.184325
Model 0: one-ratio	-7692.378189
Model 3: discrete	-7612.548293
Model 7: beta	-7613.263867
Model 8: beta&w>1	-7612.700513


Model 0 vs 1	154.3877279999997

Model 2 vs 1	0.0

Model 8 vs 7	1.1267079999997804