--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Tue Nov 22 07:19:46 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-PC/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

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

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -2154.58         -2173.84
2      -2154.94         -2172.30
--------------------------------------
TOTAL    -2154.74         -2173.34
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/3/acj6-PC/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/3/acj6-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/3/acj6-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}         0.320561    0.003053    0.225983    0.431234    0.315063   1315.09   1408.05    1.000
r(A<->C){all}   0.079721    0.001015    0.018683    0.140059    0.076956    649.86    800.98    1.000
r(A<->G){all}   0.249985    0.003618    0.136891    0.367246    0.245688    431.54    465.39    1.000
r(A<->T){all}   0.173637    0.002758    0.078937    0.277183    0.169730    656.21    722.00    1.002
r(C<->G){all}   0.060758    0.000423    0.023813    0.101911    0.058704    615.91    792.67    1.002
r(C<->T){all}   0.424995    0.005111    0.284470    0.564363    0.421768    497.23    555.73    1.000
r(G<->T){all}   0.010904    0.000112    0.000004    0.031795    0.007700    969.00   1019.26    1.000
pi(A){all}      0.242957    0.000159    0.218109    0.267401    0.242974   1188.26   1263.76    1.000
pi(C){all}      0.306022    0.000171    0.281861    0.332303    0.305910   1313.42   1326.12    1.000
pi(G){all}      0.266052    0.000164    0.238660    0.288888    0.266027   1122.22   1132.10    1.000
pi(T){all}      0.184969    0.000121    0.164492    0.207358    0.184582   1225.57   1282.61    1.000
alpha{1,2}      0.054758    0.000876    0.000207    0.099587    0.057599   1067.17   1103.49    1.000
alpha{3}        2.202812    0.610383    0.859386    3.717442    2.091979   1296.74   1394.97    1.000
pinvar{all}     0.778867    0.000699    0.725369    0.826849    0.780434   1271.98   1386.49    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	-2066.605014
Model 2: PositiveSelection	-2066.501271
Model 0: one-ratio	-2071.635569
Model 3: discrete	-2066.501271
Model 7: beta	-2069.328302
Model 8: beta&w>1	-2066.500897


Model 0 vs 1	10.061109999999644

Model 2 vs 1	0.20748600000024453

Model 8 vs 7	5.654809999999998