partlyObservedNetwork | R Documentation |
An R6 Class used for internal representation of a partially observed network
An R6 Class used for internal representation of a partially observed network
This class is not exported to the user
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)
new()
constructor
partlyObservedNetwork$new( adjacencyMatrix, covariates = list(), similarity = l1_similarity )
adjacencyMatrix
The adjacency matrix of the network
covariates
A list with M entries (the M covariates), each of whom being either a size-N vector or N x N matrix.
similarity
An R x R -> R function to compute similarities between node covariates. Default is l1_similarity
, that is, -abs(x-y).
clustering()
method to cluster network data with missing value
partlyObservedNetwork$clustering( vBlocks, imputation = ifelse(is.null(private$phi), "median", "average") )
vBlocks
The vector of number of blocks considered in the collection.
imputation
character indicating the type of imputation among "median", "average"
imputation()
basic imputation from existing clustering
partlyObservedNetwork$imputation(type = c("median", "average", "zero"))
type
a character, the type of imputation. Either "median" or "average"
clone()
The objects of this class are cloneable with this method.
partlyObservedNetwork$clone(deep = FALSE)
deep
Whether to make a deep clone.
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