# Implementation of simulation engine for dynamic networks using smoothing estimates of change statistics. ```r engineEdgeBayes( start_network, inputcoeff, ns, model.terms, model.formula, graph_mode, group, intercept, exvar, maxlag, lagmat, ylag, lambda = NA, method = "bayesglm", alpha.glmnet, paramout = TRUE, Theta = NA ) ``` ## 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. - `Theta`: = prior probability matrix. ## Examples ```r ## Not run: startNet <- rdNets[1:50] model.terms=c("triadcensus.003", "triadcensus.012", "triadcensus.102", "triadcensus.021D", "gwesp") model.formula = net~triadcensus(0:3)+gwesp(alpha=0, fixed=FALSE, cutoff=30)-1 graph_mode <- 'digraph' group <- 'dnc' alpha.glmnet <- 1 method <- 'bayesglm' maxlag <- 3 lambda <- NA intercept <- "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.coef <- paramEdge(input_network = startNet, model.terms = model.terms, model.formula = model.formula, graph_mode='digraph', group=group,intercept = intercept, exvar=NA, maxlag = maxlag, lagmat = lagmat, ylag = ylag, lambda = NA, method='bayesglm', alpha.glmnet=1) inputcoeff <- out.coef$coef$coef.edge nvertex <- 47 ##find vertex here ns <- 1 exvar <- NA for(i in seq_along(startNet)) Theta <- Theta + startNet[[i]][,] Theta <- Theta/length(startNet) Theta <- thresh(Theta) out.bayes <- engineEdgeBayes(start_network=startNet, 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, Theta = Theta) ## End(Not run) ```

engineEdgeBayes function