SimulateDyadicLinearERGM: Simulates an ERGM network using given covariate values
In epinet: Epidemic/Network-Related Tools

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

Simulates a random ERGM network using a given matrix of covariate values and a corresponding vector of parameter values.

1	SimulateDyadicLinearERGM(N, dyadiccovmat, eta)

`N`	number of individuals in the population.
`dyadiccovmat`	matrix of dyadic covariates.
`eta`	vector of parameters.

dyadiccovmat is an (N choose 2) by (k+2) matrix containing the dyadic covariates for the population, where N is the number of individuals in the population and k is the number of dyadic covariates used in the model. The matrix contains one row for each dyad (pair of nodes). Columns 1 and 2 give the ID of the two nodes comprising the dyad, and the remaining k columns give the covariate values; eta is the vector of parameters corresponding to the covariates.

For this class of dyadic independence network, the probability of an edge between individuals i and j is p_{i,j}, where

log(p_{i,j} / (1 - p_{i,j})) = eta * X_{i,j}

More information about this type of model can be found in Groendyke et al. (2012).

a network in edgelist matrix format

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

Groendyke, C., Welch, D. and Hunter, D. 2012. A Network-based Analysis of the 1861 Hagelloch Measles Data, Biometrics, 68-3.

SEIR.simulator for simulating an SEIR epidemic over a network.

# Construct a network of 30 individuals
set.seed(3)
N <- 30
# Build dyadic covariate matrix
# 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
examplenet <- SimulateDyadicLinearERGM(N, dyadiccovmat = dcm, eta = -1.8)

# Another example
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)