epinet (version 2.1.11)

SimulateDyadicLinearERGM: Simulates an ERGM network using given covariate values

Description

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

Usage

SimulateDyadicLinearERGM(N, dyadiccovmat, eta)

Value

a network in edgelist matrix format

Arguments

N

number of individuals in the population.

dyadiccovmat

matrix of dyadic covariates.

eta

vector of parameters.

Author

David Welch , Chris Groendyke

Details

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 $$\log \left( \frac{p_{\{i,j\} }}{1-p_{\{i,j\} }} \right) = \sum_{k} \eta_k X_{\{i,j\},k}$$

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

References

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

See Also

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

Examples

# 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)