simulate.btergm {btergm}  R Documentation 
btergm
ObjectSimulate 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 lefthand 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) ## End(Not run)