# Prints a transmission tree in Newick format. Prints a simulated or inferred transmission tree in Newick format. 2.10 ```r epi2newick(epi) epi2newickmcmc(mcmcoutput, index = dim(mcmcoutput$transtree)[2]) ``` ## Arguments - `epi`: a simulated epidemic, in the form of the output produced by `SEIR.simulator`. - `mcmcoutput`: output from `epinet`. - `index`: a number indicating which of the MCMC samples to plot. Defaults to the final sample in the chain. ## Details Converts the epinet epidemic format into a transmssion tree represented as a Newick string which is the standard tree format used in phylogenetics. There are many packages available to analyse Newick format trees such as the ape package, IcyTree and FigTree. ## Returns A character string representing the epidemic transmission tree in Newick format. Note that this string contains control characters that can be removed by using `cat` ## See Also `epinet` for generating posterior samples of the parameters, `print.epinet` and `summary.epinet` for printing basic summary information about an epinet object, `write.epinet` for writing parameter and transmission tree posterior samples to file, and `plot.epinet` for plotting the posterior samples of the transmission tree. ## References Rambaut A. 2014. FigTree v1.4. [http://tree.bio.ed.ac.uk/software/figtree/](http://tree.bio.ed.ac.uk/software/figtree/). Vaughan T. 2015. IcyTree [https://icytree.org](https://icytree.org). ## Author(s) David Welch david.welch@auckland.ac.nz , Chris Groendyke cgroendyke@gmail.com ## Examples ```r # Simulate an epidemic through a network of 30 set.seed(3) N <- 30 # Build dyadic covariate matrix (X) # Have a single covariate for overall edge density; this is the Erdos-Renyi model nodecov <- matrix(1:N, nrow = N) dcm <- BuildX(nodecov) # Simulate network and then simulate epidemic over network examplenet <- SimulateDyadicLinearERGM(N, dyadiccovmat = dcm, eta = -1.8) exampleepidemic <- SEIR.simulator(examplenet, N = 30, beta = 0.3, ki = 2, thetai = 5, latencydist="gamma") cat(epi2newick(exampleepidemic)) ## Not run: # Build covariates set.seed(1) N <- 50 mycov <- data.frame(id = 1:N, xpos = runif(N), ypos = runif(N)) dyadCov <- BuildX(mycov,binaryCol = list(c(2, 3)),binaryFunc = c("euclidean")) # Build network eta <- c(0, -7) net <- SimulateDyadicLinearERGM(N = N,dyadiccovmat = dyadCov,eta = eta) # Simulate epidemic epi <- SEIR.simulator(M=net,N=N,beta=1,ki=3,thetai=7,ke=3,latencydist="gamma") # Run MCMC routine on simulated epidemic mcmcinput <- MCMCcontrol(nsamp = 1000000, thinning = 100, etapropsd = c(1, 1)) priors <- priorcontrol(bprior = c(0, 4), tiprior = c(1, 15), teprior = c(1, 15), etaprior = c(0, 10, 0, 10), kiprior = c(1, 7), keprior = c(1, 7), priordists = "uniform") out <- epinet(~ xpos.ypos.L2Dist, epidata = epi, dyadiccovmat = dyadCov, mcmcinput = mcmcinput, priors = priors) cat(epi2newickmcmc(out)) ## End(Not run) ```

epi2newick function