--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Tue Nov 22 08:30:28 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-PH/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

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

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -2202.33         -2226.49
2      -2202.26         -2222.74
--------------------------------------
TOTAL    -2202.30         -2225.82
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/3/acj6-PH/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-PH/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-PH/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         0.398948    0.003586    0.291985    0.522766    0.394293   1187.85   1344.42    1.000
r(A<->C){all}   0.116702    0.001314    0.047389    0.185891    0.113409    539.30    694.28    1.000
r(A<->G){all}   0.240539    0.003111    0.140886    0.356073    0.235726    607.17    636.80    1.002
r(A<->T){all}   0.128960    0.001939    0.051691    0.215578    0.123400    858.59    864.40    1.000
r(C<->G){all}   0.061557    0.000391    0.025350    0.100529    0.059699    776.74    869.03    1.000
r(C<->T){all}   0.440501    0.004222    0.319848    0.571056    0.441155    552.44    611.49    1.001
r(G<->T){all}   0.011741    0.000119    0.000001    0.035259    0.008666    919.40   1037.66    1.001
pi(A){all}      0.238666    0.000159    0.213234    0.263546    0.238715   1112.06   1220.97    1.000
pi(C){all}      0.308803    0.000172    0.281245    0.333079    0.308930   1286.69   1343.28    1.000
pi(G){all}      0.272981    0.000175    0.246631    0.297647    0.272874   1129.15   1155.12    1.000
pi(T){all}      0.179549    0.000118    0.157869    0.199807    0.179360   1135.79   1182.40    1.000
alpha{1,2}      0.048430    0.000662    0.000106    0.087386    0.051533   1196.38   1264.40    1.000
alpha{3}        2.346881    0.620205    1.033658    3.967005    2.224011   1321.58   1411.29    1.000
pinvar{all}     0.753122    0.000680    0.702684    0.803493    0.754077   1335.67   1338.28    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	-2106.513824
Model 2: PositiveSelection	-2106.511272
Model 0: one-ratio	-2106.559576
Model 3: discrete	-2106.511272
Model 7: beta	-2106.510882
Model 8: beta&w>1	-2106.513346


Model 0 vs 1	0.09150399999998626

Model 2 vs 1	0.005103999999846565

Model 8 vs 7	0.004928000000290922