--- EXPERIMENT NOTES




 --- EXPERIMENT PROPERTIES

#Fri Nov 25 00:59:00 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/30/CadN-PC/input.fasta
input.names=
mrbayes.params=
codeml.params=



 --- PSRF SUMMARY

      Estimated marginal likelihoods for runs sampled in files
"/opt/ADOPS/30/CadN-PC/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/30/CadN-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/30/CadN-PC/batch/allfiles/mrbayes/input.fasta.fasta.mrb.lstat)

Run   Arithmetic mean   Harmonic mean
--------------------------------------
1     -31171.24        -31188.70
2     -31171.74        -31186.66
--------------------------------------
TOTAL   -31171.46        -31188.13
--------------------------------------


Model parameter summaries over the runs sampled in files
"/opt/ADOPS/30/CadN-PC/batch/allfiles/mrbayes/input.fasta.fasta.mrb.run1.p" and "/opt/ADOPS/30/CadN-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/30/CadN-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}         1.189595    0.000931    1.133053    1.252446    1.188950   1410.92   1455.96    1.000
r(A<->C){all}   0.082354    0.000029    0.072153    0.093428    0.082244    978.32   1108.31    1.001
r(A<->G){all}   0.295840    0.000109    0.273485    0.315164    0.295598    702.09    756.34    1.002
r(A<->T){all}   0.069888    0.000030    0.060169    0.081398    0.069806   1047.05   1063.25    1.001
r(C<->G){all}   0.056879    0.000018    0.048792    0.065396    0.056788    869.67    989.98    1.000
r(C<->T){all}   0.440788    0.000125    0.418677    0.462162    0.440747    559.01    661.98    1.002
r(G<->T){all}   0.054252    0.000020    0.045356    0.063237    0.054095    901.03   1023.86    1.000
pi(A){all}      0.253458    0.000019    0.244998    0.262244    0.253323    697.27    840.55    1.000
pi(C){all}      0.261944    0.000018    0.253629    0.269772    0.261936    661.84    706.34    1.000
pi(G){all}      0.266981    0.000019    0.258335    0.275215    0.266916    497.45    797.75    1.000
pi(T){all}      0.217617    0.000015    0.209741    0.225156    0.217544    632.87    809.01    1.001
alpha{1,2}      0.085978    0.000009    0.080313    0.091878    0.085898   1439.94   1470.47    1.000
alpha{3}        8.213413    1.628749    5.921812   10.713210    8.106510   1296.62   1311.23    1.000
pinvar{all}     0.438154    0.000120    0.416767    0.459369    0.438205   1486.78   1493.89    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	-28979.626918
Model 2: PositiveSelection	-28979.626938
Model 0: one-ratio	-29045.783047
Model 3: discrete	-28949.183788
Model 7: beta	-28959.628256
Model 8: beta&w>1	-28957.150338


Model 0 vs 1	132.3122579999981

Model 2 vs 1	3.9999998989515007E-5

Model 8 vs 7	4.955836000001