https://wiki.cs.earlham.edu/index.php?title=Mb-sump-output&feed=atom&action=history Mb-sump-output - Revision history 2024-03-28T22:02:44Z Revision history for this page on the wiki MediaWiki 1.32.1 https://wiki.cs.earlham.edu/index.php?title=Mb-sump-output&diff=5887&oldid=prev Charliep at 17:39, 14 April 2008 2008-04-14T17:39:43Z <p></p> <p><b>New page</b></p><div>&lt;pre&gt;<br /> MrBayes &gt; 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 &quot;Testudinoidea_all-1.nex.mb&quot;<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 /> | ***********************************************************|<br /> | |<br /> | |<br /> | |<br /> | |<br /> | |<br /> | |<br /> | |<br /> | |<br /> | |<br /> | |<br /> | |<br /> | |<br /> | |<br /> |* |<br /> +------+-----+-----+-----+-----+-----+-----+-----+-----+-----+ -52237.00<br /> ^ ^<br /> 1 5000000<br /> <br /> <br /> Estimated marginal likelihoods for run sampled in file &quot;Testudinoidea_all-1.nex.mb.p&quot;:<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 &quot;Testudinoidea_all-1.nex.mb&quot;:<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&lt;-&gt;C) 0.052355 0.000010 0.047490 0.057619 0.052272<br /> r(A&lt;-&gt;G) 0.224344 0.000102 0.206142 0.243204 0.224514<br /> r(A&lt;-&gt;T) 0.053276 0.000011 0.047998 0.059155 0.053220<br /> r(C&lt;-&gt;G) 0.017005 0.000007 0.013740 0.020402 0.016936<br /> r(C&lt;-&gt;T) 0.629575 0.000189 0.609984 0.650019 0.629242<br /> r(G&lt;-&gt;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 /> &lt;/pre&gt;</div> Charliep