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	<updated>2026-07-17T03:21:47Z</updated>
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		<id>https://wiki.cs.earlham.edu/index.php?title=Mb-sump-output&amp;diff=5887&amp;oldid=prev</id>
		<title>Charliep at 17:39, 14 April 2008</title>
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		<updated>2008-04-14T17:39:43Z</updated>

		<summary type="html">&lt;p&gt;&lt;/p&gt;
&lt;p&gt;&lt;b&gt;New page&lt;/b&gt;&lt;/p&gt;&lt;div&gt;&amp;lt;pre&amp;gt;&lt;br /&gt;
MrBayes &amp;gt; sump filename=Testudinoidea_all-1.nex.mb nruns=1&lt;br /&gt;
&lt;br /&gt;
   Setting sump filename to Testudinoidea_all-1.nex.mb&lt;br /&gt;
   Setting sump output file to Testudinoidea_all-1.nex.mb.stat&lt;br /&gt;
   Setting sump nruns to 1&lt;br /&gt;
   Summarizing parameters in file Testudinoidea_all-1.nex.mb.p&lt;br /&gt;
   Writing output to screen but not to file (&amp;#039;Printtofile = No&amp;#039;)&lt;br /&gt;
   UNIX line termination&lt;br /&gt;
   Longest line length = 124&lt;br /&gt;
   Found 5001 parameter lines in file &amp;quot;Testudinoidea_all-1.nex.mb&amp;quot;&lt;br /&gt;
   All 5001 lines will be summarized (starting at line 3)&lt;br /&gt;
   (Only the last set of lines will be read, in case multiple&lt;br /&gt;
   parameter blocks are present in the same file.)&lt;br /&gt;
   5001 rows and 14 columns in each row&lt;br /&gt;
   Successfully read 5001 lines from last parameter block&lt;br /&gt;
&lt;br /&gt;
   Below is a rough plot of the generation (x-axis) versus the log   &lt;br /&gt;
   probability of observing the data (y-axis). You can use this      &lt;br /&gt;
   graph to determine what the burn in for your analysis should be.  &lt;br /&gt;
   When the log probability starts to plateau you may be at station- &lt;br /&gt;
   arity. Sample trees and parameters after the log probability      &lt;br /&gt;
   plateaus. Of course, this is not a guarantee that you are at sta- &lt;br /&gt;
   analysis should be. When the log probability starts to plateau    &lt;br /&gt;
   tionarity. When possible, run multiple analyses starting from dif-&lt;br /&gt;
   ferent random trees; if the inferences you make for independent   &lt;br /&gt;
   analyses are the same, this is reasonable evidence that the chains&lt;br /&gt;
   have converged. You can use MrBayes to run several independent    &lt;br /&gt;
   analyses simultaneously. During such a run, MrBayes will monitor  &lt;br /&gt;
   the convergence of topologies. After the run has been completed,  &lt;br /&gt;
   the &amp;#039;sumt&amp;#039; and &amp;#039;sump&amp;#039; functions will provide additional conver-   &lt;br /&gt;
   gence diagnostics for all the parameters in your model. Remember  &lt;br /&gt;
   that the burn in is the number of samples to discard. There are   &lt;br /&gt;
   a total of ngen / samplefreq samples taken during a MCMC analysis.&lt;br /&gt;
&lt;br /&gt;
   +------------------------------------------------------------+ -50896.09&lt;br /&gt;
   | ***********************************************************|&lt;br /&gt;
   |                                                            |&lt;br /&gt;
   |                                                            |&lt;br /&gt;
   |                                                            |&lt;br /&gt;
   |                                                            |&lt;br /&gt;
   |                                                            |&lt;br /&gt;
   |                                                            |&lt;br /&gt;
   |                                                            |&lt;br /&gt;
   |                                                            |&lt;br /&gt;
   |                                                            |&lt;br /&gt;
   |                                                            |&lt;br /&gt;
   |                                                            |&lt;br /&gt;
   |                                                            |&lt;br /&gt;
   |                                                            |&lt;br /&gt;
   |*                                                           |&lt;br /&gt;
   +------+-----+-----+-----+-----+-----+-----+-----+-----+-----+ -52237.00&lt;br /&gt;
   ^                                                            ^&lt;br /&gt;
   1                                                            5000000&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
   Estimated marginal likelihoods for run sampled in file &amp;quot;Testudinoidea_all-1.nex.mb.p&amp;quot;:&lt;br /&gt;
      (Use the harmonic mean for Bayes factor comparisons of models)&lt;br /&gt;
&lt;br /&gt;
   Arithmetic mean   Harmonic mean&lt;br /&gt;
   --------------------------------&lt;br /&gt;
      -50878.70 *       -87193.62 *&lt;br /&gt;
   --------------------------------&lt;br /&gt;
   * These estimates may be unreliable because &lt;br /&gt;
     some extreme values were excluded&lt;br /&gt;
&lt;br /&gt;
&lt;br /&gt;
   Model parameter summaries for run sampled in file &amp;quot;Testudinoidea_all-1.nex.mb&amp;quot;:&lt;br /&gt;
      (Based on a total of 5001 samples out of a total of 5001 samples from this analysis)&lt;br /&gt;
&lt;br /&gt;
                                              95% Cred. Interval&lt;br /&gt;
                                            ----------------------&lt;br /&gt;
   Parameter       Mean        Variance       Lower         Upper         Median&lt;br /&gt;
   ------------------------------------------------------------------------------&lt;br /&gt;
   TL            5.363889      0.364596      5.104000      5.576000      5.328000&lt;br /&gt;
   r(A&amp;lt;-&amp;gt;C)      0.052355      0.000010      0.047490      0.057619      0.052272&lt;br /&gt;
   r(A&amp;lt;-&amp;gt;G)      0.224344      0.000102      0.206142      0.243204      0.224514&lt;br /&gt;
   r(A&amp;lt;-&amp;gt;T)      0.053276      0.000011      0.047998      0.059155      0.053220&lt;br /&gt;
   r(C&amp;lt;-&amp;gt;G)      0.017005      0.000007      0.013740      0.020402      0.016936&lt;br /&gt;
   r(C&amp;lt;-&amp;gt;T)      0.629575      0.000189      0.609984      0.650019      0.629242&lt;br /&gt;
   r(G&amp;lt;-&amp;gt;T)      0.023446      0.000011      0.018576      0.028516      0.023368&lt;br /&gt;
   pi(A)         0.329686      0.000039      0.317861      0.342043      0.329402&lt;br /&gt;
   pi(C)         0.278162      0.000031      0.268185      0.288695      0.278227&lt;br /&gt;
   pi(G)         0.171375      0.000031      0.160837      0.181418      0.171369&lt;br /&gt;
   pi(T)         0.220777      0.000023      0.211556      0.229830      0.220873&lt;br /&gt;
   alpha         0.229991      0.000050      0.218927      0.241060      0.229703&lt;br /&gt;
   ------------------------------------------------------------------------------&lt;br /&gt;
&amp;lt;/pre&amp;gt;&lt;/div&gt;</summary>
		<author><name>Charliep</name></author>
	</entry>
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