engineEdgeBayes: Implementation of simulation engine for dynamic networks...

In SSDALab/dnr: Simulate Dynamic Networks using Exponential Random Graph Models (ERGM) Family

Description

Implementation of simulation engine for dynamic networks using smoothing estimates of change statistics.

Usage

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

## 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)

SSDALab/dnr documentation built on Dec. 28, 2020, 8:02 a.m.