paramVertex: Parameter estimation for Vertex dynamics

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

Description

Parameter estimation fro dynamic vertex case. The interface remaining almost identical to the static vertex one.

Usage

paramVertex(
  InputNetwork,
  VertexStatsvec = rep(1, nvertexstats),
  maxLag,
  VertexLag = rep(1, maxLag),
  VertexLagMatrix = matrix(1, maxLag, length(VertexStatsvec)),
  VertexModelGroup = NA,
  VertexAttLag = rep(1, maxLag),
  dayClass = NA,
  EdgeModelTerms,
  EdgeModelFormula,
  EdgeGroup,
  EdgeIntercept = c("edges"),
  EdgeNetparam = NA,
  EdgeExvar = NA,
  EdgeLag = rep(1, maxLag),
  EdgeLagMatrix = matrix(1, maxLag, length(EdgeModelTerms)),
  regMethod = "bayesglm",
  paramout = FALSE
)

Arguments

InputNetwork	list of networks.
VertexStatsvec	binary vector of size 8.
maxLag	maximum lag, numeric.
VertexLag	binary vector of length maxLag.
VertexLagMatrix	binary matrix of size maxLag x 8.
VertexModelGroup	Grouping term for vertex model. Must be from vertex attribute list.
VertexAttLag	Lag vector for vertex group terms. Of length maxLag.
dayClass	Any network level present time attribute vector. Here used to indicate week/weekend as 0/1.
EdgeModelTerms	Model terms in edge model.
EdgeModelFormula	Model formula in edge model.
EdgeGroup	Group terms in edge model.
EdgeIntercept	Intercept for edge model.
EdgeNetparam	Network level parameter for edge model (currently only supported parameter is current network size).
EdgeExvar	Extraneous variable for edge model.
EdgeLag	binary vector of length maxLag.
EdgeLagMatrix	binary matrix of dim maxLag x length(EdgeModelTerms)
regMethod	Regression method. default: "bayesglm"
paramout	T/F Should the parameter estimates be returned?

Details

The Vertex model parameter list is as follows (Freeman degree, In degree, Out degree, Eigen Centrality, Between centrality, Info centrality, Closeness centrality, log k cycles, log size). For more details about the definitions of the terms, please refer to the vertexstats.R file, which implements all of these. The definitions are in sna or igraph.

Value

list with following elements:
EdgeCoef: edge coefficients.
Edgemplematfull: MPLE matrix from edges.
Edgemplemat: Subsetted MPLE matrix.
VertexCoef: Coefficients from vertex.
Vstats: Vertex statistics matrix.
EdgePredictor0: Edge predictors with imputations with 0.
EdgePredictor1: Edge predictors with imputations with 1.
EdgePredictorNA: Edge predictors with imputations with NA.
EdgeFit: Edge model.
VertexStatsFull: Vertex statistics matrix, full.
VertexFit: Vertex model.

Author(s)

Abhirup

Examples

nvertexstats <- 9
maxLag = 3
VertexLag = rep(1, maxLag)
VertexLagMatrix <- matrix(0, maxLag, nvertexstats)
VertexLagMatrix[, c(4, 7)] <- 1
VertexLagMatrix[c(2,3),7] <- 0

getWeekend <- function(z){
    weekends <- c("Saturday", "Sunday")
    if(!network::is.network(z)){
        if(is.na(z)) return(NA)
    } else {
         zDay <- get.network.attribute(z, attrname = "day")
         out <- ifelse(zDay %in% weekends, 1, 0)
         return(out)   
    }
}

dayClass <- numeric(length(beach))
for(i in seq_along(dayClass)) {
    dayClass[i] <- getWeekend(beach[[i]])
}
dayClass <- na.omit(dayClass)


out <- paramVertex(InputNetwork = beach,
                   maxLag = 3,
                   VertexStatsvec = rep(1, nvertexstats),
                   VertexModelGroup = "regular",
                   VertexLag = rep(1, maxLag),
                   VertexLagMatrix = VertexLagMatrix,
                   dayClass = dayClass,
                   EdgeModelTerms = NA,
                   EdgeModelFormula = NA,
                   EdgeGroup = NA,
                   EdgeIntercept = c("edges"),
                   EdgeNetparam = c("logSize"),
                   EdgeExvar = NA,
                   EdgeLag = c(1, 1, 0),
                   paramout = TRUE)

dnr documentation built on Dec. 1, 2020, 1:07 a.m.