https://wiki.cs.earlham.edu/index.php?title=Mb-sump-output&feed=atom&action=historyMb-sump-output - Revision history2024-03-28T22:02:44ZRevision history for this page on the wikiMediaWiki 1.32.1https://wiki.cs.earlham.edu/index.php?title=Mb-sump-output&diff=5887&oldid=prevCharliep at 17:39, 14 April 20082008-04-14T17:39:43Z<p></p>
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MrBayes > sump filename=Testudinoidea_all-1.nex.mb nruns=1<br />
<br />
Setting sump filename to Testudinoidea_all-1.nex.mb<br />
Setting sump output file to Testudinoidea_all-1.nex.mb.stat<br />
Setting sump nruns to 1<br />
Summarizing parameters in file Testudinoidea_all-1.nex.mb.p<br />
Writing output to screen but not to file ('Printtofile = No')<br />
UNIX line termination<br />
Longest line length = 124<br />
Found 5001 parameter lines in file "Testudinoidea_all-1.nex.mb"<br />
All 5001 lines will be summarized (starting at line 3)<br />
(Only the last set of lines will be read, in case multiple<br />
parameter blocks are present in the same file.)<br />
5001 rows and 14 columns in each row<br />
Successfully read 5001 lines from last parameter block<br />
<br />
Below is a rough plot of the generation (x-axis) versus the log <br />
probability of observing the data (y-axis). You can use this <br />
graph to determine what the burn in for your analysis should be. <br />
When the log probability starts to plateau you may be at station- <br />
arity. Sample trees and parameters after the log probability <br />
plateaus. Of course, this is not a guarantee that you are at sta- <br />
analysis should be. When the log probability starts to plateau <br />
tionarity. When possible, run multiple analyses starting from dif-<br />
ferent random trees; if the inferences you make for independent <br />
analyses are the same, this is reasonable evidence that the chains<br />
have converged. You can use MrBayes to run several independent <br />
analyses simultaneously. During such a run, MrBayes will monitor <br />
the convergence of topologies. After the run has been completed, <br />
the 'sumt' and 'sump' functions will provide additional conver- <br />
gence diagnostics for all the parameters in your model. Remember <br />
that the burn in is the number of samples to discard. There are <br />
a total of ngen / samplefreq samples taken during a MCMC analysis.<br />
<br />
+------------------------------------------------------------+ -50896.09<br />
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+------+-----+-----+-----+-----+-----+-----+-----+-----+-----+ -52237.00<br />
^ ^<br />
1 5000000<br />
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<br />
Estimated marginal likelihoods for run sampled in file "Testudinoidea_all-1.nex.mb.p":<br />
(Use the harmonic mean for Bayes factor comparisons of models)<br />
<br />
Arithmetic mean Harmonic mean<br />
--------------------------------<br />
-50878.70 * -87193.62 *<br />
--------------------------------<br />
* These estimates may be unreliable because <br />
some extreme values were excluded<br />
<br />
<br />
Model parameter summaries for run sampled in file "Testudinoidea_all-1.nex.mb":<br />
(Based on a total of 5001 samples out of a total of 5001 samples from this analysis)<br />
<br />
95% Cred. Interval<br />
----------------------<br />
Parameter Mean Variance Lower Upper Median<br />
------------------------------------------------------------------------------<br />
TL 5.363889 0.364596 5.104000 5.576000 5.328000<br />
r(A<->C) 0.052355 0.000010 0.047490 0.057619 0.052272<br />
r(A<->G) 0.224344 0.000102 0.206142 0.243204 0.224514<br />
r(A<->T) 0.053276 0.000011 0.047998 0.059155 0.053220<br />
r(C<->G) 0.017005 0.000007 0.013740 0.020402 0.016936<br />
r(C<->T) 0.629575 0.000189 0.609984 0.650019 0.629242<br />
r(G<->T) 0.023446 0.000011 0.018576 0.028516 0.023368<br />
pi(A) 0.329686 0.000039 0.317861 0.342043 0.329402<br />
pi(C) 0.278162 0.000031 0.268185 0.288695 0.278227<br />
pi(G) 0.171375 0.000031 0.160837 0.181418 0.171369<br />
pi(T) 0.220777 0.000023 0.211556 0.229830 0.220873<br />
alpha 0.229991 0.000050 0.218927 0.241060 0.229703<br />
------------------------------------------------------------------------------<br />
</pre></div>Charliep