paramVertex function

Parameter estimation for Vertex dynamics

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

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?

Returns

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.

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.

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)

Author(s)

Abhirup