--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Mon Apr 30 19:04:11 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_N1/E_2/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS1/DNG_N1/E_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_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_N1/E_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1     -12469.67        -12516.65
2     -12468.79        -12509.57
--------------------------------------
TOTAL   -12469.14        -12515.96
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS1/DNG_N1/E_2/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS1/DNG_N1/E_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_N1/E_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}         9.399429    0.302178    8.284445   10.476200    9.384797    538.51    565.01    1.000
r(A<->C){all}   0.034689    0.000025    0.025598    0.045019    0.034601    803.04    873.14    1.001
r(A<->G){all}   0.176774    0.000152    0.151480    0.199359    0.176845    554.52    574.07    1.000
r(A<->T){all}   0.050497    0.000034    0.039501    0.062356    0.050272    615.75    655.71    1.001
r(C<->G){all}   0.017198    0.000020    0.008373    0.025501    0.016938    693.06    806.78    1.001
r(C<->T){all}   0.696792    0.000243    0.668569    0.728087    0.697019    549.55    562.10    1.000
r(G<->T){all}   0.024051    0.000024    0.014532    0.033421    0.023871    852.02    933.77    1.000
pi(A){all}      0.350061    0.000072    0.334498    0.367593    0.349939    648.59    747.13    1.000
pi(C){all}      0.213442    0.000049    0.199398    0.226402    0.213687    456.09    613.65    1.000
pi(G){all}      0.242822    0.000061    0.227250    0.257754    0.242826    743.92    794.44    1.000
pi(T){all}      0.193676    0.000045    0.179503    0.205815    0.193549    625.52    727.58    1.000
alpha{1,2}      0.197264    0.000098    0.177429    0.216286    0.196838    871.13   1121.80    1.000
alpha{3}        4.842499    0.690217    3.356065    6.466790    4.770707   1034.34   1201.16    1.000
pinvar{all}     0.093876    0.000301    0.061314    0.127653    0.093513    788.23    914.69    1.001
------------------------------------------------------------------------------------------------------
* 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	-11942.430876
Model 2: PositiveSelection	-11942.430881
Model 0: one-ratio	-11981.93516
Model 3: discrete	-11801.936554
Model 7: beta	-11802.700904
Model 8: beta&w>1	-11802.662628


Model 0 vs 1	79.0085680000011

Model 2 vs 1	9.999999747378752E-6

Model 8 vs 7	0.07655199999862816