epinet (version 2.1.11)

write.epinet: Writes posterior samples from an epinet object to an output file

Description

Outputs posterior samples of an object created by the epinet inference routine; creates two output files.

Usage

write.epinet(out, filename)

Value

Strictly invoked for side effect.

Arguments

out

an object of class epinet, produced from the epinet inference function.

filename

the name of the output file.

Author

David Welch , Chris Groendyke

Details

Writes two output files corresponding to the output produced from the epinet inference function. The first is a .log file, containing the posterior samples from the epidemic parameters in tab delimited form. [This .log file can be read by Tracer, which calculates summary statististics and diagnostics, and displays trace plots, histograms, etc.] The second file (which is only written if the transmission trees are returned from the inference routine), is a .trees file, containing the inferred transmission trees, output in Newick format.

References

Rambaut A., Suchard M., Xie D., Drummond A.J. 2014. Tracer v1.6. http://beast.community/tracer.html.

See Also

epinet for generating posterior samples of the parameters, print.epinet and summary.epinet for printing basic summary information about an epinet object, epi2newickmcmc for printing an inferred transmission tree to the screen in Newick format, and plot.epinet for plotting the posterior samples of the transmission tree.

Examples

# 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")
# Set inputs for MCMC algorithm
mcmcinput <- MCMCcontrol(nsamp = 5000, thinning = 10, etapropsd = 0.2) 
priorcontrol <- priorcontrol(bprior = c(0, 1), tiprior = c(1, 3), teprior = c(1, 3), 
    etaprior = c(0, 10), kiprior = c(2, 8), keprior = c(2, 8), priordists = "uniform")
# Run MCMC algorithm on this epidemic
# Note: Not enough data or iterations for any real inference
examplemcmc <- epinet( ~ 1, exampleepidemic, dcm, mcmcinput, priorcontrol)
if (FALSE) write.epinet(examplemcmc, "examplemcmc")

if (FALSE) {
# Note: This may take a few minutes to run.
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)
write.epinet(out, "SampleInferenceOutput")
}