--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Tue Nov 22 08:45:27 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-PI/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

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

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -2266.17         -2284.86
2      -2265.95         -2284.74
--------------------------------------
TOTAL    -2266.06         -2284.80
--------------------------------------


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

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         0.388850    0.003261    0.282135    0.497652    0.384365   1312.29   1397.93    1.000
r(A<->C){all}   0.118751    0.001152    0.060245    0.190239    0.115789    848.97    932.21    1.000
r(A<->G){all}   0.247701    0.002851    0.148755    0.355528    0.245181    471.46    516.19    1.000
r(A<->T){all}   0.111127    0.001423    0.041648    0.184680    0.107305    777.09    799.93    1.000
r(C<->G){all}   0.065652    0.000415    0.029946    0.107426    0.063244    844.44    944.36    1.000
r(C<->T){all}   0.445810    0.003740    0.322958    0.563454    0.446044    672.17    746.40    1.000
r(G<->T){all}   0.010958    0.000105    0.000022    0.031156    0.007902    782.15    813.64    1.000
pi(A){all}      0.241819    0.000149    0.217424    0.265722    0.241292   1266.11   1281.69    1.000
pi(C){all}      0.306077    0.000175    0.280298    0.331841    0.306253   1162.05   1280.55    1.000
pi(G){all}      0.270149    0.000173    0.244127    0.295796    0.270271   1262.34   1311.24    1.000
pi(T){all}      0.181955    0.000121    0.160300    0.202828    0.181911   1185.48   1279.24    1.000
alpha{1,2}      0.045390    0.000683    0.000112    0.086819    0.047038   1048.01   1140.40    1.000
alpha{3}        2.455511    0.675468    1.067352    4.100930    2.337238   1389.62   1445.31    1.000
pinvar{all}     0.746716    0.000690    0.694248    0.797080    0.747571   1285.60   1323.85    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	-2168.428845
Model 2: PositiveSelection	-2168.426242
Model 0: one-ratio	-2168.474962
Model 3: discrete	-2168.426242
Model 7: beta	-2168.42576
Model 8: beta&w>1	-2168.428362


Model 0 vs 1	0.0922339999997348

Model 2 vs 1	0.005205999999816413

Model 8 vs 7	0.005204000000048836