--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Sat Nov 12 00:08:17 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/2/Abl-PC/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/2/Abl-PC/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/Abl-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/2/Abl-PC/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1     -15913.10        -15929.96
2     -15913.53        -15929.47
--------------------------------------
TOTAL   -15913.29        -15929.74
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/2/Abl-PC/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/Abl-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/2/Abl-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}         0.770011    0.000953    0.707533    0.828221    0.770083   1066.51   1192.69    1.000
r(A<->C){all}   0.079183    0.000059    0.064729    0.094475    0.079094    985.14   1049.48    1.000
r(A<->G){all}   0.236979    0.000208    0.207519    0.264152    0.236600    843.70    859.95    1.000
r(A<->T){all}   0.162665    0.000220    0.131940    0.189568    0.162883    987.80   1050.79    1.000
r(C<->G){all}   0.040559    0.000021    0.032002    0.049149    0.040454   1196.45   1262.56    1.000
r(C<->T){all}   0.380886    0.000295    0.348373    0.414955    0.380532    966.36    970.91    1.000
r(G<->T){all}   0.099728    0.000105    0.078698    0.118478    0.099605   1019.57   1037.15    1.000
pi(A){all}      0.235234    0.000031    0.224172    0.246446    0.235224    895.34    954.20    1.000
pi(C){all}      0.322726    0.000038    0.311028    0.335076    0.322724   1133.55   1162.92    1.000
pi(G){all}      0.281071    0.000034    0.269485    0.292600    0.280997   1039.38   1157.56    1.000
pi(T){all}      0.160969    0.000022    0.152278    0.169986    0.160996    998.60   1001.36    1.001
alpha{1,2}      0.138353    0.000081    0.121559    0.156293    0.138242   1305.13   1329.49    1.000
alpha{3}        6.621457    1.679684    4.393521    9.423974    6.468258   1336.94   1418.97    1.000
pinvar{all}     0.378687    0.000472    0.336223    0.423385    0.379360   1172.41   1336.70    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	-14384.726652
Model 2: PositiveSelection	-14384.726652
Model 0: one-ratio	-14506.88723
Model 3: discrete	-14377.03339
Model 7: beta	-14377.783013
Model 8: beta&w>1	-14377.285318


Model 0 vs 1	244.3211560000018

Model 2 vs 1	0.0

Model 8 vs 7	0.9953900000000431