--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Fri May 18 23:09:54 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_N2/NS3_5/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N2/NS3_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS3_5/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_N2/NS3_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1     -13110.61        -13150.77
2     -13106.58        -13152.08
--------------------------------------
TOTAL   -13107.25        -13151.62
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N2/NS3_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N2/NS3_5/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_N2/NS3_5/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         7.632280    0.194270    6.811406    8.507804    7.616064    379.71    510.03    1.000
r(A<->C){all}   0.041190    0.000025    0.031484    0.050798    0.041033    679.13    693.13    1.000
r(A<->G){all}   0.203134    0.000163    0.178955    0.228315    0.202445    610.99    617.85    1.000
r(A<->T){all}   0.041084    0.000027    0.031055    0.051736    0.041050    863.69    964.80    1.000
r(C<->G){all}   0.023046    0.000021    0.014347    0.032114    0.022838    867.74    900.39    1.001
r(C<->T){all}   0.670354    0.000244    0.638729    0.699630    0.670671    561.21    572.88    1.000
r(G<->T){all}   0.021193    0.000023    0.012484    0.031330    0.020995    707.93    791.05    1.000
pi(A){all}      0.354954    0.000063    0.339704    0.370710    0.354906    720.18    779.65    1.000
pi(C){all}      0.213999    0.000042    0.201380    0.226726    0.214192    782.30    845.15    1.000
pi(G){all}      0.232255    0.000053    0.217051    0.245360    0.232326    773.40    899.83    1.000
pi(T){all}      0.198792    0.000038    0.186191    0.210047    0.198683    729.90    752.82    1.000
alpha{1,2}      0.151181    0.000042    0.139032    0.164209    0.150781   1083.28   1195.60    1.000
alpha{3}        4.996657    0.683331    3.521265    6.639369    4.910608   1123.51   1255.35    1.000
pinvar{all}     0.118681    0.000313    0.085609    0.154113    0.118331   1033.98   1263.92    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	-11713.095707
Model 2: PositiveSelection	-11713.095707
Model 0: one-ratio	-11738.883263
Model 3: discrete	-11578.064193
Model 7: beta	-11579.774705
Model 8: beta&w>1	-11579.77772


Model 0 vs 1	51.575111999998626

Model 2 vs 1	0.0

Model 8 vs 7	0.006030000000464497