Description Usage Arguments Details Value Author(s) See Also Examples
Prints summaries of posterior samples generated by the
epinet
inference routine.
1 2 3 |
object |
an object of class epinet, produced from the |
... |
other arguments to be passed to the summary routine. |
Prints summaries of the epidemic and network parameters of an epinet inference object. Epidemic parameters are beta, thetae, ke, thetai, and ki. Network parameters are specified in the model formula, and may include an intercept term.
Strictly invoked for side effect.
Chris Groendyke cgroendyke@gmail.com, David Welch david.welch@auckland.ac.nz
epinet
for generating posterior samples of the parameters, and
plot.epinet
for plotting the posterior samples of the transmission tree.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 | # 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)
summary(examplemcmc)
## Not run:
# 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)
summary(out)
## End(Not run)
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