--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Tue Nov 22 05:50:07 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-PF/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

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

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -7998.18         -8015.08
2      -7997.72         -8019.07
--------------------------------------
TOTAL    -7997.92         -8018.39
--------------------------------------


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

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         0.999535    0.002945    0.894480    1.106559    0.998345   1057.35   1168.21    1.001
r(A<->C){all}   0.063928    0.000086    0.047048    0.082888    0.063710   1009.91   1057.40    1.000
r(A<->G){all}   0.161711    0.000313    0.128489    0.196728    0.160843    818.90    889.81    1.000
r(A<->T){all}   0.106632    0.000276    0.075284    0.140539    0.106170    916.16    938.97    1.000
r(C<->G){all}   0.049864    0.000043    0.037065    0.062107    0.049707    879.33    943.08    1.000
r(C<->T){all}   0.534893    0.000548    0.490013    0.580564    0.535147    742.67    802.33    1.000
r(G<->T){all}   0.082972    0.000130    0.061438    0.105616    0.082458   1058.88   1079.04    1.000
pi(A){all}      0.215713    0.000070    0.200287    0.232907    0.215616   1000.48   1059.94    1.000
pi(C){all}      0.324325    0.000077    0.307841    0.341245    0.324085   1102.33   1113.84    1.001
pi(G){all}      0.272088    0.000076    0.255496    0.289248    0.271937   1258.87   1275.23    1.000
pi(T){all}      0.187875    0.000051    0.174233    0.201505    0.188025   1061.71   1117.07    1.000
alpha{1,2}      0.105092    0.000059    0.089790    0.120047    0.104903   1303.48   1402.24    1.000
alpha{3}        5.213098    1.202557    3.327931    7.480774    5.092952   1293.17   1321.69    1.000
pinvar{all}     0.347008    0.000703    0.294439    0.397517    0.347326   1366.88   1379.98    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