epi2newick: Prints a transmission tree in Newick format.
In epinet: Epidemic/Network-Related Tools

Description Usage Arguments Details Value Author(s) References See Also Examples

Prints a simulated or inferred transmission tree in Newick format.

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epi2newick(epi)

epi2newickmcmc(mcmcoutput, index = dim(mcmcoutput$transtree)[2])

`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.

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.

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

David Welch david.welch@auckland.ac.nz, Chris Groendyke cgroendyke@gmail.com

Rambaut A. 2014. FigTree v1.4. http://tree.bio.ed.ac.uk/software/figtree/. Vaughan T. 2015. IcyTree https://icytree.org.

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.

# 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)