--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Wed May 30 22:48:27 WEST 2018
codeml.models=0 1 2 3 7 8
mrbayes.mpich=
mrbayes.ngen=1000000
tcoffee.alignMethod=MUSCLE
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/ADOPS1/DNG_A1/NS2A_2/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_A1/NS2A_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2A_2/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/ADOPS1/DNG_A1/NS2A_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -8385.58         -8434.60
2      -8382.77         -8441.80
--------------------------------------
TOTAL    -8383.40         -8441.11
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_A1/NS2A_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2A_2/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/ADOPS1/DNG_A1/NS2A_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}        11.566017    0.534391   10.104130   12.923390   11.532040    671.81    708.33    1.000
r(A<->C){all}   0.049471    0.000059    0.035362    0.064765    0.049294    796.87    855.08    1.000
r(A<->G){all}   0.226667    0.000276    0.193948    0.258717    0.226234    532.12    544.99    1.000
r(A<->T){all}   0.045942    0.000043    0.033464    0.058841    0.045826    747.90    882.39    1.001
r(C<->G){all}   0.044026    0.000062    0.029417    0.059468    0.043731    948.69    982.74    1.002
r(C<->T){all}   0.595115    0.000410    0.556346    0.634547    0.595283    535.27    545.81    1.000
r(G<->T){all}   0.038779    0.000053    0.024843    0.052531    0.038489    845.10    858.86    1.000
pi(A){all}      0.317967    0.000120    0.297092    0.339066    0.317420    719.26    750.03    1.000
pi(C){all}      0.209727    0.000079    0.191770    0.225931    0.209913    889.76    897.60    1.000
pi(G){all}      0.239231    0.000091    0.221292    0.258406    0.238885    874.71    907.46    1.000
pi(T){all}      0.233075    0.000089    0.214937    0.251503    0.233252    709.88    826.80    1.000
alpha{1,2}      0.416498    0.001904    0.336330    0.506916    0.413370   1130.99   1133.15    1.000
alpha{3}        4.915084    1.056482    3.055367    6.934221    4.797835   1394.75   1396.74    1.000
pinvar{all}     0.056982    0.000505    0.014197    0.099763    0.055434   1024.21   1072.58    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	-8068.479572
Model 2: PositiveSelection	-8068.479572
Model 0: one-ratio	-8099.531773
Model 3: discrete	-8002.302433
Model 7: beta	-8005.758876
Model 8: beta&w>1	-8005.761042


Model 0 vs 1	62.10440199999903

Model 2 vs 1	0.0

Model 8 vs 7	0.004332000000431435