--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Sat Dec 10 16:00:37 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-PC/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

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

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1     -19514.66        -19534.39
2     -19514.70        -19534.67
--------------------------------------
TOTAL   -19514.68        -19534.54
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/443/zip-PC/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/443/zip-PC/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-PC/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         1.214018    0.001651    1.135277    1.292183    1.213351   1134.42   1207.51    1.000
r(A<->C){all}   0.058482    0.000030    0.047778    0.068889    0.058249   1007.82   1039.43    1.000
r(A<->G){all}   0.284426    0.000164    0.261228    0.310624    0.284339    878.17    939.41    1.000
r(A<->T){all}   0.097357    0.000096    0.078137    0.116337    0.097273    904.46   1044.08    1.000
r(C<->G){all}   0.026901    0.000009    0.020912    0.032641    0.026845   1050.08   1159.55    1.001
r(C<->T){all}   0.484990    0.000241    0.455585    0.515463    0.484795    885.62    918.56    1.000
r(G<->T){all}   0.047843    0.000028    0.038138    0.058863    0.047649    915.64   1024.73    1.000
pi(A){all}      0.266561    0.000030    0.255909    0.276992    0.266562    880.74    939.86    1.000
pi(C){all}      0.261781    0.000030    0.251469    0.272344    0.261757   1054.59   1151.75    1.000
pi(G){all}      0.305252    0.000032    0.294969    0.316864    0.305250    828.05   1039.92    1.000
pi(T){all}      0.166407    0.000020    0.157768    0.174940    0.166352    830.01    925.97    1.000
alpha{1,2}      0.088255    0.000015    0.080798    0.095817    0.088259   1223.02   1252.80    1.000
alpha{3}        8.212635    1.906688    5.771791   11.011170    8.053071   1172.85   1297.47    1.000
pinvar{all}     0.395211    0.000209    0.367217    0.422964    0.395237    981.03   1116.60    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