engineEdgeNS function

Implementation of simulation engine for dynamic networks without using smoothing estimates of change statistics.

engineEdgeNS(
  start_network,
  inputcoeff,
  ns,
  model.terms,
  model.formula,
  graph_mode,
  group,
  intercept,
  exvar,
  maxlag,
  lagmat,
  ylag,
  lambda = NA,
  method = "bayesglm",
  alpha.glmnet,
  paramout = TRUE
)

Arguments

• start_network: Initial list of networks
• inputcoeff: coefficient vector
• ns: number of time points for simulation
• model.terms: model terms in formula
• model.formula: model formula (ergm)
• graph_mode: 'digraph' by default
• group: group terms
• intercept: intercept terms
• exvar: extraneous covariates
• maxlag: maximum lag
• lagmat: lag matrix
• ylag: lag vector for network lag terms
• lambda: NA
• method: 'bayesglm' by default
• alpha.glmnet: NA
• paramout: T/F parameter estimation is returned.

Returns

list: out_network: list of predicted networks coefmat: if paramout is TRUE, matrix of coefficients at all time.

Examples

## Not run:

input_network=rdNets[1:6];
model.terms=c("triadcensus.003", "triadcensus.012", "triadcensus.102", "triadcensus.021D", "gwesp");
model.formula = net~triadcensus(0:3)+gwesp(decay=0, fixed=FALSE, cutoff=30)-1;
graph_mode='digraph';
group='dnc';
alpha.glmnet=1
directed=TRUE;
method <- 'bayesglm'
maxlag <- 3
lambda=NA
intercept = c("edges")
cdim <- length(model.terms)
lagmat <- matrix(sample(c(0,1),(maxlag+1)*cdim,replace = TRUE),ncol = cdim)
ylag <- rep(1,maxlag)
lagmat[1,] <- rep(0,ncol(lagmat))
out <- paramEdge(input_network,model.terms, model.formula,
                graph_mode="digraph",group,intercept = c("edges"),exvar=NA,
                maxlag = 3,
                lagmat = lagmat,
                ylag = rep(1,maxlag),
                lambda = NA, method='bayesglm',
                alpha.glmnet=1)
#
start_network <- input_network
inputcoeff <- out$coef$coef
nvertex <- 47
ns <- 10
exvar <- NA
tmp <- suppressWarnings(engineEdgeNS(start_network=start_network,
                     inputcoeff=inputcoeff,ns=ns,
                     model.terms=model.terms, model.formula=model.formula,
                     graph_mode=graph_mode,group=group,intercept=intercept,
                     exvar=exvar,
                     maxlag=maxlag,
                     lagmat=lagmat,
                     ylag=ylag,
                     lambda = NA, method='bayesglm',
                     alpha.glmnet=alpha.glmnet))
## End(Not run)

Author(s)

Abhirup