EpiModel (version 2.0.1)

as.phylo.transmat: Convert transmat infection tree into a phylo object

Description

Converts the edgelist matrix in the transmat object into a phylo object by doing the required reordering and labeling.

Usage

# S3 method for transmat
as.phylo(x, collapse.singles, vertex.exit.times, ...)

Arguments

x

An object of class "transmat", the output from get_transmat.

collapse.singles

logical, DEPRECATED

vertex.exit.times

optional numeric vector providing the time of departure of vertices, to be used to scale the lengths of branches reaching to the tips. Index position on vector corresponds to network id. NA indicates no departure, so branch will extend to the end of the tree.

...

further arguments (unused)

Details

Converts a transmat object containing information about the history of a simulated infection into a phylo object representation suitable for plotting as a tree with plot.phylo. Each infection event becomes a 'node' (horizontal branch) in the resulting phylo tree, and each network vertex becomes a 'tip' of the tree. The infection events are labeled with the vertex id of the infector to make it possible to trace the path of infection.

The infection timing information is included to position the phylo-nodes, with the lines to the tips drawn to the max time value +1 (unless vertex.exit.times are passed in it effectively assumes all vertices are active/alive until the end of the simulation).

If the transmat contains multiple infection seeds (there are multiple trees with seperate root nodes) it will return a list of class 'multiPhylo', each element of which is a phylo object. See read.tree.

Note that in EpiModel versions <= 1.2.4, the phylo tree was constructed differently, translating network vertices to both phylo-nodes and tips and requiring 'collapse.singles' to prune it to an appropriate branching structure.

Examples

# NOT RUN {
set.seed(10)
nw <- network_initialize(n = 100)
formation <- ~edges
target.stats <- 50
coef.diss <- dissolution_coefs(dissolution = ~offset(edges), duration = 20)

est1 <- netest(nw, formation, target.stats, coef.diss, verbose = FALSE)

param <- param.net(inf.prob = 0.5)
init <- init.net(i.num = 1)
control <- control.net(type = "SI", nsteps = 40, nsims = 1, verbose = FALSE)

mod1 <- netsim(est1, param, init, control)
tm <- get_transmat(mod1)
tmPhylo <- as.phylo.transmat(tm)
plot(tmPhylo, show.node.label = TRUE,
              root.edge = TRUE,
              cex = 0.5)

# }