--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Wed May 30 13:43:40 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_1/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_A1/NS2A_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2A_1/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_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -7580.43         -7634.99
2      -7585.83         -7633.43
--------------------------------------
TOTAL    -7581.12         -7634.49
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_A1/NS2A_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_A1/NS2A_1/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_1/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}        10.275873    0.484566    8.951379   11.665340   10.256450    549.80    730.51    1.002
r(A<->C){all}   0.046232    0.000067    0.029964    0.061376    0.045887    691.07    744.71    1.000
r(A<->G){all}   0.241327    0.000319    0.205915    0.276539    0.241358    549.70    553.44    1.000
r(A<->T){all}   0.056932    0.000061    0.041280    0.071485    0.056639    683.07    835.83    1.000
r(C<->G){all}   0.033750    0.000058    0.020306    0.049952    0.033351    836.19    889.30    1.000
r(C<->T){all}   0.584958    0.000450    0.541341    0.625048    0.584039    569.80    581.38    1.000
r(G<->T){all}   0.036800    0.000056    0.022125    0.051236    0.036376    981.56   1041.03    1.000
pi(A){all}      0.300289    0.000113    0.280601    0.321731    0.299964    640.69    868.50    1.000
pi(C){all}      0.216282    0.000089    0.198416    0.234749    0.216265    679.26    805.10    1.002
pi(G){all}      0.246808    0.000108    0.227433    0.267562    0.246704    815.72    866.35    1.001
pi(T){all}      0.236621    0.000095    0.217929    0.255749    0.236795    738.45    898.58    1.000
alpha{1,2}      0.418408    0.001734    0.343737    0.504083    0.414891   1164.32   1197.36    1.000
alpha{3}        4.607867    0.961182    2.826767    6.546296    4.498651   1272.24   1384.16    1.000
pinvar{all}     0.030873    0.000338    0.000049    0.065160    0.028076   1223.28   1272.37    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	-7286.010144
Model 2: PositiveSelection	-7286.010144
Model 0: one-ratio	-7293.64689
Model 3: discrete	-7228.709293
Model 7: beta	-7230.250979
Model 8: beta&w>1	-7230.253144


Model 0 vs 1	15.27349200000026

Model 2 vs 1	0.0

Model 8 vs 7	0.004329999999754364