--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Tue Nov 22 05:05:27 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/Acon-PE/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

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

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -7998.28         -8020.45
2      -7998.36         -8015.68
--------------------------------------
TOTAL    -7998.32         -8019.77
--------------------------------------


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

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         0.997149    0.002709    0.889427    1.092614    0.995293   1109.00   1153.67    1.000
r(A<->C){all}   0.064113    0.000089    0.046489    0.082979    0.063685    905.20   1064.55    1.000
r(A<->G){all}   0.161289    0.000310    0.129075    0.196687    0.160542    819.09    821.23    1.000
r(A<->T){all}   0.106950    0.000282    0.076196    0.140512    0.106348    962.53   1037.59    1.000
r(C<->G){all}   0.050049    0.000044    0.037564    0.063548    0.049799    861.95    963.84    1.000
r(C<->T){all}   0.534077    0.000569    0.487331    0.579441    0.534475    631.34    720.32    1.000
r(G<->T){all}   0.083522    0.000133    0.061134    0.105766    0.083402    814.17    937.18    1.000
pi(A){all}      0.215568    0.000070    0.198241    0.231246    0.215393    728.51    920.67    1.000
pi(C){all}      0.324521    0.000078    0.307145    0.341237    0.324570    945.46   1000.72    1.001
pi(G){all}      0.272016    0.000076    0.255006    0.289233    0.271971   1036.15   1043.70    1.000
pi(T){all}      0.187895    0.000053    0.173517    0.202009    0.187865    727.03    767.52    1.000
alpha{1,2}      0.104895    0.000058    0.089031    0.118698    0.104528   1401.27   1406.00    1.000
alpha{3}        5.228203    1.167062    3.339908    7.451262    5.110090   1157.63   1327.49    1.000
pinvar{all}     0.346900    0.000659    0.299432    0.399939    0.346649   1168.62   1334.81    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	-7488.36443
Model 2: PositiveSelection	-7488.364448
Model 0: one-ratio	-7561.165264
Model 3: discrete	-7461.24543
Model 7: beta	-7461.607272
Model 8: beta&w>1	-7461.60783


Model 0 vs 1	145.60166800000115

Model 2 vs 1	3.600000127335079E-5

Model 8 vs 7	0.0011159999994561076