engineEdgeNS {dnr}

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

Description

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

Usage

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.

Value

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

Author(s)

Abhirup

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)