--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Fri Nov 11 18:03:19 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/Aac11-PA/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/2/Aac11-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/Aac11-PA/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/Aac11-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1      -5664.66         -5681.13
2      -5665.18         -5680.27
--------------------------------------
TOTAL    -5664.89         -5680.79
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/2/Aac11-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/2/Aac11-PA/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/Aac11-PA/batch/allfiles/mrbayes/input.fasta.fasta.mrb.pstat".

95% HPD Interval
--------------------
Parameter         Mean      Variance     Lower       Upper       Median    min ESS*  avg ESS    PSRF+
------------------------------------------------------------------------------------------------------
TL{all}         1.064657    0.004166    0.943744    1.196554    1.063627   1330.10   1415.55    1.000
r(A<->C){all}   0.090556    0.000149    0.068005    0.115320    0.089989    980.40   1106.95    1.000
r(A<->G){all}   0.303849    0.000628    0.255456    0.352896    0.303344    736.80    835.50    1.000
r(A<->T){all}   0.102790    0.000245    0.072519    0.132133    0.102412   1060.12   1142.03    1.000
r(C<->G){all}   0.037005    0.000060    0.023233    0.054767    0.036560    986.85   1048.91    1.000
r(C<->T){all}   0.405614    0.000792    0.352312    0.460548    0.404972    793.56    897.46    1.001
r(G<->T){all}   0.060187    0.000132    0.038672    0.083488    0.059463    967.12   1093.05    1.001
pi(A){all}      0.290347    0.000108    0.271145    0.311980    0.290311   1020.88   1133.16    1.000
pi(C){all}      0.252162    0.000103    0.232823    0.272160    0.252018   1269.38   1269.93    1.000
pi(G){all}      0.262078    0.000107    0.242352    0.282866    0.261918   1252.44   1264.73    1.000
pi(T){all}      0.195413    0.000079    0.178880    0.213858    0.195430   1081.73   1120.04    1.000
alpha{1,2}      0.121691    0.000097    0.103460    0.141800    0.121110   1320.65   1378.87    1.000
alpha{3}        5.247777    1.306284    3.174832    7.501634    5.114628   1256.02   1336.30    1.000
pinvar{all}     0.346297    0.000957    0.279314    0.401489    0.348008   1276.98   1311.93    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	-5172.087895
Model 2: PositiveSelection	-5172.087953
Model 0: one-ratio	-5202.200571
Model 3: discrete	-5167.94669
Model 7: beta	-5169.82779
Model 8: beta&w>1	-5169.716646


Model 0 vs 1	60.225352000001294

Model 2 vs 1	1.160000010713702E-4

Model 8 vs 7	0.22228800000084448