TAMING THE BEAST

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The dataset consists of an alignment of 63 Hepatitis C sequences sampled in 1993 in Egypt (Ray et al., 2000). This dataset has been used previously to test the performance of skyline methods (Drummond et al., 2005; Stadler et al., 2013).

In practice, we can get away much smaller sub-chain lengths, which you can verify by running multiple NS analysis with increasing sub-chain lengths. If the ML and SD estimates do not substantially differ, you know the shorter sub-chain length was sufficient. How many particles do I need? Marginal likelihood: -12417.389793288146 sqrt(H/N)=(1.9543337689486355)=?=SD=(1.9614418034828585) Information: 122.2214553744953

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In June this year we organised the first Taming the BEAST workshop, surrounded by the Swiss Alps, in Engelberg, Switzerland. Marginal likelihood: -12426.207750474812 sqrt(H/N)=(1.8913059067381148)=?=SD=(1.8374367294317693) Information: 114.46521705159945 If the difference is smaller, you can guess how much the SD estimates must shrink to get a difference that is sufficiently large. Since the SD=sqrt(H/N), we have that N=H/(SD*SD) and H comes from the NS run with a few particles. Run the analysis again, with the increased number of particles, and see if the difference becomes large enough. We will be using R to analyze the output of the Birth-Death Skyline plot. RStudio provides a user-friendly graphical user interface to R that makes it easier to edit and run scripts. (It is not necessary to use RStudio for this tutorial). We can leave the rest of the priors as they are and save the XML file. We want to shorten the chain length and decrease the sampling frequency so the analysis completes in a reasonable time and the output files stay small. (Keep in mind that it will be necessary to run a longer chain for parameters to mix properly).

The workshop organisers and participants outside of the London School of Hygiene and Tropical Medicine. Navigate to the Priors panel and select Coalescent Bayesian Skyline as the tree prior ( Figure 5). Figure 5: Choose the Coalescent Bayesian Skyline as a tree prior. Marginal likelihood: -12428.557546706481 sqrt(H/N)=(11.22272275528845)=?=SD=(11.252847709777592) Information: 125.94950604206919 The sequences were all sampled in 1993 so we are dealing with a homochronous alignment and do not need to specify tip dates.Once the analyses have run, open the log file in Tracer and compare estimates and see whether the analyses substantially differ. You can also compare the trees in DensiTree. To get more accurate estimates, the number of particles can be increased. The expected SD is sqrt(H/N) where N is the number of particles and H the information. The information H is conveniently estimated in the nested sampling run as well. Because we shortened the chain most parameters have very low ESS values. If you like, you can compare your results with the example results we obtained with identical settings and a chain of 30,000,000 ( hcv_coal_30M.log).

The analysis will take about 10 minutes to complete. Read through the next section while waiting for your results or start preparing the XML file for the birth-death skyline analysis. The Coalescent Bayesian Skyline parameterization The exported file will have five rows, the time, the mean, median, lower and upper boundary of the 95% HPD interval of the estimates, which you can use to plot the data with other software (R, Matlab, etc). Choosing the Dimension NS works in theory if and only if the points generated at each iteration are independent. If you already did an MCMC run and know the effective sample size (ESS) for each parameter, to be sure every parameter in every sample is independent you can take the length of the MCMC run divided by the smallest ESS as sub-chain length. This tend to result in quite large sub-chain lengths.We hope that the community will play an active role in curating the tutorials, either by updating or correcting existing tutorials, or by contributing new tutorials. Estimates of N e N_e N e ​ therefore do not directly tell us something about the number of infected, nor the transmission rate. However, changes in N e N_e N e ​ can be informative about changes in the transmission rate or the number of infected (if they do not cancel out). The parallel implementation makes it possible to run many particles in parallel, giving a many-particle estimate in the same time as a single particle estimate (PS/SS can be parallelised by steps as well). The output is written on screen, which I forgot to save. Can I estimate them directly from the log files? An NS analysis produces two trace log files: one for the nested sampling run (say myFile.log) and one with the posterior sample ( myFile.posterior.log).