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

engineEdgeNS function