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

• partlyObservedNetwork$new()

• partlyObservedNetwork$clustering()

• partlyObservedNetwork$imputation()

• 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.