<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>

CC BY-SA 4.0
2019-02-20T02:05:53.217Z
Thanks Michael. Yes, this is Picornavirus (RNA Virus).
LoveVirology
CC BY-SA 4.0
2019-03-04T11:46:29.610Z
It turns out there is a beast website, which I think would be worth while consulting http://beast.community
M__
CC BY-SA 4.0
2019-02-01T00:17:57.877Z
2019-03-04T12:17:04.233Z
2019-03-04T12:17:04.233Z
M__
<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>

CC BY-SA 4.0
2018-05-28T07:27:34.393Z
Did you tried reading about the tools and methods you used and what is the output you get and how to interpret it? Also could you confirm it is [this tool](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3408070/)?
llrs
CC BY-SA 4.0
2018-05-28T15:15:00.233Z
I did. I followed all the tutorials and documentations. I could not find something that helps me to understand how these parameters should be interpreted.
LoveVirology
CC BY-SA 4.0
2018-05-28T15:43:28.727Z
Could you provide the links. Maybe someone reading the same could understand it better
llrs
CC BY-SA 4.0
2018-05-28T15:55:15.973Z
Here is the link http://beast.community/phylodynamics_of_epidemic_influenza
LoveVirology
CC BY-SA 4.0
2018-05-28T16:01:11.720Z
Remember that you can [edit] the post to improve and add more information
llrs
CC BY-SA 4.0
2018-05-28T21:39:53.120Z
You should probably pick up a basic textbook for statistical phylogenetics to get more familiar with the methods you're using. I know the BEAST people have published one (probably Drummond), but I recommend Ziheng Yang's excellent _Molecular Evolution: A Statistical Approach_
NatWH
<phylogenetics><phyloseq><sequence-alignment><alignment>
Interpreting bayesian phylogenetic model using Tracer?
# Interpreting bayesian phylogenetic model using Tracer?

(Asked by: username_0 on 2018-05-27)

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

**Tags:** phylogenetics, phyloseq, sequence-alignment, alignment
> **Comments:**
> **username_1**: Did you tried reading about the tools and methods you used and what is the output you get and how to interpret it? Also could you confirm it is [this tool](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3408070/)?  
> **username_0**: I did. I followed all the tutorials and documentations. I could not find something that helps me to understand how these parameters should be interpreted.  
> **username_1**: Could you provide the links. Maybe someone reading the same could understand it better  
> **username_0**: Here is the link http://beast.community/phylodynamics_of_epidemic_influenza  
> **username_1**: Remember that you can [edit] the post to improve and add more information  
> **username_2**: You should probably pick up a basic textbook for statistical phylogenetics to get more familiar with the methods you're using. I know the BEAST people have published one (probably Drummond), but I recommend Ziheng Yang's excellent _Molecular Evolution: A Statistical Approach_  

---

## 1 answers

### Answer by username_3 on 2019-02-01. Score: 1

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.

> **Comments:**
> **username_0**: Thanks Michael. Yes, this is Picornavirus (RNA Virus).  
> **username_3**: It turns out there is a beast website, which I think would be worth while consulting http://beast.community  

---

