partlyObservedNetwork function

An R6 Class used for internal representation of a partially observed network

An R6 Class used for internal representation of a partially observed network

An R6 Class used for internal representation of a partially observed network

Details

This class is not exported to the user

Active bindings

• samplingRate: The percentage of observed dyads

• nbNodes: The number of nodes

• nbDyads: The number of dyads

• is_directed: logical indicating if the network is directed or not

• networkData: The adjacency matrix of the network

• covarArray: the array of covariates

• covarMatrix: the matrix of covariates

• samplingMatrix: matrix of observed and non-observed edges

• samplingMatrixBar: matrix of observed and non-observed edges

• observedNodes: a vector of observed and non-observed nodes (observed means at least one non NA value)

Methods

Public methods

• list("partlyObservedNetwork$new()")

• list("partlyObservedNetwork$clustering()")

• list("partlyObservedNetwork$imputation()")

• list("partlyObservedNetwork$clone()")

Method new()

constructor

Usage

partlyObservedNetwork$new(
  adjacencyMatrix,
  covariates = list(),
  similarity = l1_similarity
)

Arguments

• adjacencyMatrix: The adjacency matrix of the network

• covariates: A list with M entries (the M covariates), each of whom being either a size-N vector or N x N matrix.

• similarity: An R x R -> R function to compute similarities between node covariates. Default is l1_similarity, that is, -abs(x-y).

Method clustering()

method to cluster network data with missing value

Usage

partlyObservedNetwork$clustering(
  vBlocks,
  imputation = ifelse(is.null(private$phi), "median", "average")
)

Arguments

• vBlocks: The vector of number of blocks considered in the collection.

• imputation: character indicating the type of imputation among "median", "average"

Method imputation()

basic imputation from existing clustering

Usage

partlyObservedNetwork$imputation(type = c("median", "average", "zero"))

Arguments

• type: a character, the type of imputation. Either "median" or "average"

Method clone()

The objects of this class are cloneable with this method.

Usage

partlyObservedNetwork$clone(deep = FALSE)

Arguments

• deep: Whether to make a deep clone.