--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Tue Nov 22 04:20:28 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/Acon-PD/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

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

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -7997.33         -8015.58
2      -7998.52         -8016.40
--------------------------------------
TOTAL    -7997.76         -8016.07
--------------------------------------


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

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         0.998339    0.002675    0.904035    1.104129    0.997485   1221.90   1361.45    1.000
r(A<->C){all}   0.063690    0.000088    0.045963    0.082026    0.063428    922.40    976.61    1.001
r(A<->G){all}   0.162121    0.000296    0.127795    0.193675    0.161992    766.88    829.47    1.000
r(A<->T){all}   0.106612    0.000280    0.075769    0.139707    0.105446   1081.16   1107.45    1.001
r(C<->G){all}   0.049846    0.000044    0.037184    0.062623    0.049610    929.16   1000.95    1.000
r(C<->T){all}   0.534294    0.000574    0.490650    0.582935    0.534458    760.62    793.70    1.000
r(G<->T){all}   0.083436    0.000126    0.061307    0.104237    0.083296    795.92    978.62    1.000
pi(A){all}      0.215588    0.000070    0.199644    0.231647    0.215559    942.40    968.61    1.000
pi(C){all}      0.324384    0.000078    0.306794    0.341370    0.324555   1091.13   1215.79    1.000
pi(G){all}      0.272252    0.000076    0.255927    0.289122    0.272100   1085.25   1099.16    1.000
pi(T){all}      0.187776    0.000052    0.174327    0.202020    0.187791   1119.26   1143.80    1.000
alpha{1,2}      0.105084    0.000061    0.090372    0.120883    0.104733   1304.41   1373.23    1.000
alpha{3}        5.232067    1.267374    3.152098    7.384083    5.128449   1200.68   1312.99    1.000
pinvar{all}     0.346989    0.000737    0.292838    0.398514    0.347444   1290.07   1358.24    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	-7488.36443
Model 2: PositiveSelection	-7488.364448
Model 0: one-ratio	-7561.165264
Model 3: discrete	-7461.24543
Model 7: beta	-7461.607272
Model 8: beta&w>1	-7461.60783


Model 0 vs 1	145.60166800000115

Model 2 vs 1	3.600000127335079E-5

Model 8 vs 7	0.0011159999994561076