Simulate Networks from a btergm Object
Simulate networks from a btergm object using MCMC sampler.
Simulate networks from an mtergm object using MCMC sampler.
## S3 method for class 'btergm' simulate( object, nsim = 1, seed = NULL, index = NULL, formula = getformula(object), coef = object@coef, verbose = TRUE, ... ) ## S3 method for class 'mtergm' simulate( object, nsim = 1, seed = NULL, index = NULL, formula = getformula(object), coef = object@coef, verbose = TRUE, ... )
object |
A |
nsim |
The number of networks to be simulated. Note that for values
greater than one, a |
seed |
Random number integer seed. See set.seed. |
index |
Index of the network from which the new network(s) should be simulated. The index refers to the list of response networks on the left-hand side of the model formula. Note that more recent networks are located at the end of the list. By default, the last (= most recent) network is used. |
formula |
A model formula from which the new network(s) should be
simulated. By default, the formula is taken from the |
coef |
A vector of parameter estimates. By default, the coefficients are
extracted from the given |
verbose |
Print additional details while running the simulations? |
... |
Further arguments are handed over to the
|
The simulate.btergm function is a wrapper for the
simulate_formula function in the ergm package (see
help("simulate.ergm")). It can be used to simulate new networks from a
btergm object. The index argument specifies from which of the
original networks the new network(s) should be simulated. For example, if
object is an estimation based on cosponsorship networks from the 99th
to the 107th Congress (as in Desmarais and Cranmer 2012), and the
cosponsorship network in the 108th Congress should be predicted using the
simulate.btergm function, then the argument index = 9 should be
passed to the function because the network should be based on the 9th network
in the list (that is, the latest network, which is the cosponsorship network
for the 107th Congress). Note that all relevant objects (the networks and the
covariates) must be present in the workspace (as was the case during the
estimation of the model).
Desmarais, Bruce A. and Skyler J. Cranmer (2012): Statistical Mechanics of Networks: Estimation and Uncertainty. Physica A 391: 1865–1876. doi: 10.1016/j.physa.2011.10.018.
Leifeld, Philip, Skyler J. Cranmer and Bruce A. Desmarais (2018): Temporal Exponential Random Graph Models with btergm: Estimation and Bootstrap Confidence Intervals. Journal of Statistical Software 83(6): 1–36. doi: 10.18637/jss.v083.i06.
## Not run:
# fit a TERGM to some toy data
library("network")
set.seed(5)
networks <- list()
for(i in 1:10){ # create 10 random networks with 10 actors
mat <- matrix(rbinom(100, 1, .25), nrow = 10, ncol = 10)
diag(mat) <- 0 # loops are excluded
nw <- network(mat) # create network object
networks[[i]] <- nw # add network to the list
}
covariates <- list()
for (i in 1:10) { # create 10 matrices as covariate
mat <- matrix(rnorm(100), nrow = 10, ncol = 10)
covariates[[i]] <- mat # add matrix to the list
}
fit <- btergm(networks ~ edges + istar(2) +
edgecov(covariates), R = 100)
# simulate 12 new networks from the last (= 10th) time step
sim1 <- simulate(fit, nsim = 12)
# simulate 1 network from the first time step
sim2 <- simulate(fit, index = 1)
# simulate network from t = 5 with larger covariate coefficient
coefs <- coef(fit)
coefs["edgecov.covariates[[i]]"] <- 0.5
sim3 <- simulate(fit, index = 5, coef = coefs)
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