--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Sat Dec 10 17:14:46 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/443/zip-PE/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/443/zip-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/443/zip-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/443/zip-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1     -19514.92        -19533.16
2     -19514.79        -19534.29
--------------------------------------
TOTAL   -19514.85        -19533.87
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/443/zip-PE/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/443/zip-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/443/zip-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}         1.215655    0.001732    1.134090    1.296109    1.214994   1327.41   1414.20    1.000
r(A<->C){all}   0.058633    0.000030    0.048463    0.069499    0.058388   1073.39   1131.06    1.000
r(A<->G){all}   0.285018    0.000165    0.260335    0.310015    0.284845    865.61    884.73    1.002
r(A<->T){all}   0.097573    0.000096    0.078643    0.116181    0.097275    907.85    962.40    1.001
r(C<->G){all}   0.026821    0.000009    0.020883    0.032614    0.026782   1146.80   1184.72    1.000
r(C<->T){all}   0.483899    0.000234    0.454249    0.513925    0.483721    760.91    766.33    1.004
r(G<->T){all}   0.048056    0.000030    0.038642    0.059881    0.047821    934.80   1009.30    1.000
pi(A){all}      0.266473    0.000029    0.256338    0.277072    0.266544    936.66   1008.27    1.002
pi(C){all}      0.262179    0.000028    0.252192    0.273199    0.262236   1009.57   1011.90    1.000
pi(G){all}      0.304938    0.000031    0.293762    0.315134    0.304843    953.10   1061.46    1.000
pi(T){all}      0.166410    0.000019    0.158503    0.175565    0.166359    662.30    835.77    1.000
alpha{1,2}      0.088088    0.000016    0.080174    0.095644    0.087983   1360.31   1426.51    1.000
alpha{3}        8.191684    1.894632    5.805221   11.089200    8.072615   1354.38   1381.77    1.000
pinvar{all}     0.394605    0.000207    0.366530    0.422258    0.394426   1326.43   1340.30    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	-17922.60291
Model 2: PositiveSelection	-17922.60291
Model 0: one-ratio	-17945.954531
Model 3: discrete	-17903.295745
Model 7: beta	-17911.101232
Model 8: beta&w>1	-17909.940876


Model 0 vs 1	46.70324199999595

Model 2 vs 1	0.0

Model 8 vs 7	2.32071200000064