text: user1684: I am doing phylodynamic analysis of virus sequences using bayesian method using BEAUti and BEAST. As a part of my analysis I am using Tracer to check how my model is working. 
  I can interpret some of the results like from the Tracer like :
  clock.rate - rate of nucleotide substitution
  treemodel.rootheight- time when divergence occured
  ESS- measures sampling quality (ideally has to be greater that 200)
  But there are other many terms on this analysis which I have no idea how to explain. 
  Please can anybody help me to find out what other aspects of the evolution can be explained using this table.
  posterior   -33531.076
  prior   -481.982
  likelihood  -33049.094
  treeModel.rootHeight    32.271
  tmrca(Brazil)   3.853
  tmrca(Canada)   16.644
  tmrca(China )   11.752
  tmrca(Columbia) 1
  tmrca(Thailand) 1.093
  tmrca(USA)  32.271
  tmrca(allseq)   32.271
  constant.popSize    43.595
  CP1.kappa   18.716
  CP2.kappa   17.16
  CP3.kappa   22.796
  frequencies1    0.224
  frequencies3    0.221
  frequencies4    0.26    5001
  CP1.mu  0.76
  CP2.mu  1.221
  CP3.mu  1.019
  clock.rate  1.354E-3
  meanRate    1.354E-3
  CP1.treeLikelihood  -9462.258
  CP2.treeLikelihood  -12520.305
  branchRates 0E0
  coalescent  -467.376    
  
  user3848: When I have a moment I will fill in the blanks
  You must have an RNA virus (nothing else will mutate that fast). The basal group and root is the USA. It estimates the time to that basal event is 32.3 (likely years).
  "frequencies" frequency of ACGT where the missing one will be 0.25. 
  Its a simple model of constant population size.
  ESS is the most important parameter to ensure convergence of the Bayesian simulation . 
  Likelihood is the second most important parameter. I'll edit again shortly.
  
  I'll fill in one more blank and summarise ... 
  Kappa ...
  This will be the transition transversion ratio in HKY. I suspect it also will be transition transversion ratio in Kimuras 3-parameter model, this is because ... the transversions are further split into two mutation groups, ie. purine to pyrimidine has 2 further combinations and you end up with 2 ratios.  that makes sense, transition/transversion (kappa 1), transition/transversion 1 (kappa 2), transition/transversion2 (kappa 3) .... 
  
  CP1.kappa 18.716
  CP2.kappa 17.16
  CP3.kappa 22.796
  
  CP2 and CP3 are either side of CP1 .. which is what you would expect on my interpretation. 
  These are very high values by the way, essentially there are very few transversions in this data, so ... I dunno. You need to check the data, if you see lots (and lots) of A-G and T-C mutations, then thats what I believe it is saying.
  Briefly posterior and prior are the fundamental method of a Bayesian calculations, the output is probability expressed as a log, so the more negative the higher the probability (the prior looks like a random tree, or a reasonable tree with really rubbish parameters, likely the former).
  
  Summary
  Essentially I am going to call it a day here. The real issue is that the output summarise the entire modern theory in phylogenetics, so its a very long response required to address all of the parameters and I honestly don't think it helps your analysis. What I am clear about is:
  * You've omitted a gamma distribution (I think Tracer would summarise this) *
  So my advice is:
  
  ESS MUST be homogeneous between runs - big trouble if not
  gamma distribution MUST be run
  
  Beast can be tricky getting gamma to converge via ESS, just a word of warning. 
  I can't see the full robustness, because its absent... However, from the output, I think its pretty clear, that you (well me) can more or less see the model you ran. 
  
original_text: 1684: <p>I am doing phylodynamic analysis of virus sequences using bayesian method using BEAUti and BEAST. As a part of my analysis I am using Tracer to check how my model is working. </p>
  
  <p>I can interpret some of the results like from the Tracer like :</p>
  
  <p><strong>clock.rate - rate of nucleotide substitution</strong></p>
  
  <p><strong>treemodel.rootheight- time when divergence occured</strong></p>
  
  <p><strong>ESS- measures sampling quality (ideally has to be greater that 200)</strong></p>
  
  <p>But there are other many terms on this analysis which I have no idea how to explain. </p>
  
  <p>Please can anybody help me to find out what other aspects of the evolution can be explained using this table.</p>
  
  <p>posterior   -33531.076<br>
  prior   -481.982<br>
  likelihood  -33049.094<br>
  treeModel.rootHeight    32.271<br>
  tmrca(Brazil)   3.853<br>
  tmrca(Canada)   16.644<br>
  tmrca(China )   11.752<br>
  tmrca(Columbia) 1<br>
  tmrca(Thailand) 1.093<br>
  tmrca(USA)  32.271<br>
  tmrca(allseq)   32.271<br>
  constant.popSize    43.595<br>
  CP1.kappa   18.716<br>
  CP2.kappa   17.16<br>
  CP3.kappa   22.796<br>
  frequencies1    0.224<br>
  frequencies3    0.221<br>
  frequencies4    0.26    5001<br>
  CP1.mu  0.76
  CP2.mu  1.221<br>
  CP3.mu  1.019<br>
  clock.rate  1.354E-3<br>
  meanRate    1.354E-3<br>
  CP1.treeLikelihood  -9462.258<br>
  CP2.treeLikelihood  -12520.305<br>
  branchRates 0E0<br>
  coalescent  -467.376    </p>
   3848: <p>When I have a moment I will fill in the blanks</p>
  
  <p>You must have an RNA virus (nothing else will mutate that fast). The basal group and root is the USA. It estimates the time to that basal event is 32.3 (likely years).</p>
  
  <p>"frequencies" frequency of ACGT where the missing one will be 0.25. </p>
  
  <p>Its a simple model of constant population size.</p>
  
  <p>ESS is the most important parameter to ensure convergence of the Bayesian simulation . </p>
  
  <p>Likelihood is the second most important parameter. I'll edit again shortly.</p>
  
  <hr>
  
  <p>I'll fill in one more blank and summarise ... </p>
  
  <p><strong>Kappa</strong> ...
  This will be the transition transversion ratio in HKY. I suspect it also will be transition transversion ratio in Kimuras 3-parameter model, this is because ... the transversions are further split into two mutation groups, ie. purine to pyrimidine has 2 further combinations and you end up with 2 ratios.  that makes sense, transition/transversion (kappa 1), transition/transversion 1 (kappa 2), transition/transversion2 (kappa 3) .... </p>
  
  <ul>
  <li>CP1.kappa 18.716</li>
  <li>CP2.kappa 17.16</li>
  <li>CP3.kappa 22.796</li>
  </ul>
  
  <p>CP2 and CP3 are either side of CP1 .. which is what you would expect on my interpretation. </p>
  
  <p>These are very high values by the way, essentially there are very few transversions in this data, so ... I dunno. You need to check the data, if you see lots (and lots) of A-G and T-C mutations, then thats what I believe it is saying.</p>
  
  <p>Briefly posterior and prior are the fundamental method of a Bayesian calculations, the output is probability expressed as a log, so the more negative the higher the probability (the prior looks like a random tree, or a reasonable tree with really rubbish parameters, likely the former).</p>
  
  <hr>
  
  <p><strong>Summary</strong>
  Essentially I am going to call it a day here. The real issue is that the output summarise the entire modern theory in phylogenetics, so its a very long response required to address all of the parameters and I honestly don't think it helps your analysis. What I am clear about is:</p>
  
  <p><strong>* You've omitted a gamma distribution (I think Tracer would summarise this) *</strong></p>
  
  <p>So my advice is:</p>
  
  <ul>
  <li>ESS MUST be homogeneous between runs - big trouble if not</li>
  <li>gamma distribution MUST be run</li>
  </ul>
  
  <p>Beast can be tricky getting gamma to converge via ESS, just a word of warning. </p>
  
  <p>I can't see the full robustness, because its absent... However, from the output, I think its pretty clear, that you (well me) can more or less see the model you ran. </p>
  