--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Tue Nov 22 08:58:41 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-PJ/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

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

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -2211.19         -2230.84
2      -2210.98         -2231.54
--------------------------------------
TOTAL    -2211.08         -2231.25
--------------------------------------


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

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         0.396651    0.003701    0.295318    0.521331    0.390237   1276.35   1335.02    1.000
r(A<->C){all}   0.115081    0.001229    0.049035    0.183334    0.112285    745.12    876.80    1.000
r(A<->G){all}   0.235409    0.002933    0.131444    0.339849    0.230460    827.79    864.87    1.000
r(A<->T){all}   0.126386    0.001867    0.043650    0.210179    0.122094    720.64    722.87    1.000
r(C<->G){all}   0.062252    0.000377    0.028406    0.101082    0.060335   1061.09   1104.27    1.000
r(C<->T){all}   0.449642    0.004082    0.327147    0.575020    0.449460    528.17    648.74    1.000
r(G<->T){all}   0.011231    0.000110    0.000008    0.032123    0.008286    903.77    958.44    1.000
pi(A){all}      0.241380    0.000156    0.218427    0.266851    0.241174   1013.39   1183.39    1.000
pi(C){all}      0.306940    0.000179    0.281624    0.333857    0.306532   1178.50   1252.93    1.000
pi(G){all}      0.272821    0.000178    0.248136    0.300715    0.273184   1065.83   1176.18    1.000
pi(T){all}      0.178860    0.000120    0.157491    0.199860    0.178516   1225.33   1252.68    1.000
alpha{1,2}      0.050017    0.000677    0.000121    0.088401    0.054054   1027.61   1169.60    1.000
alpha{3}        2.324506    0.646379    1.014259    3.946140    2.202287   1461.39   1481.20    1.000
pinvar{all}     0.754197    0.000686    0.701980    0.804399    0.755113   1416.25   1458.62    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	-2116.486373
Model 2: PositiveSelection	-2116.483814
Model 0: one-ratio	-2116.532034
Model 3: discrete	-2116.483814
Model 7: beta	-2116.483433
Model 8: beta&w>1	-2116.485896


Model 0 vs 1	0.0913219999993089

Model 2 vs 1	0.005118000000038592

Model 8 vs 7	0.004926000000523345