--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Tue Nov 22 07:47:06 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/acj6-PE/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/3/acj6-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-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/acj6-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -2205.14         -2234.73
2      -2205.13         -2227.23
--------------------------------------
TOTAL    -2205.13         -2234.03
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/3/acj6-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-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/acj6-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}         0.315640    0.002686    0.220082    0.419878    0.311040   1342.86   1397.50    1.000
r(A<->C){all}   0.084137    0.001062    0.026188    0.149870    0.081130    860.03    882.14    1.000
r(A<->G){all}   0.260029    0.003798    0.142199    0.383183    0.255847    474.75    503.62    1.000
r(A<->T){all}   0.154218    0.002365    0.061459    0.245359    0.150074    788.27    808.38    1.000
r(C<->G){all}   0.062829    0.000441    0.024025    0.103231    0.060868   1061.61   1097.43    1.000
r(C<->T){all}   0.428427    0.004877    0.283915    0.562519    0.425465    400.97    470.37    1.000
r(G<->T){all}   0.010360    0.000104    0.000005    0.030362    0.007333    767.88   1011.12    1.001
pi(A){all}      0.243038    0.000143    0.220112    0.266212    0.243037   1043.84   1231.93    1.000
pi(C){all}      0.304775    0.000173    0.280382    0.331174    0.304813   1233.28   1278.59    1.000
pi(G){all}      0.264682    0.000163    0.240607    0.289446    0.264418   1161.02   1182.99    1.000
pi(T){all}      0.187505    0.000114    0.166301    0.208142    0.187633   1202.69   1207.14    1.000
alpha{1,2}      0.052249    0.000835    0.000353    0.097377    0.054898    965.52   1024.45    1.000
alpha{3}        2.312694    0.667555    1.002374    3.978123    2.176054   1219.79   1360.39    1.001
pinvar{all}     0.774445    0.000684    0.723657    0.825007    0.775335   1102.25   1301.62    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	-2114.20902
Model 2: PositiveSelection	-2114.100053
Model 0: one-ratio	-2119.258047
Model 3: discrete	-2114.100053
Model 7: beta	-2116.950875
Model 8: beta&w>1	-2114.099674


Model 0 vs 1	10.098054000000047

Model 2 vs 1	0.21793399999933172

Model 8 vs 7	5.702401999999893