predictEdge: Edge prediction of weighted bipartite network.

In jafarilab/NIMAA: Nominal Data Mining Analysis

Edge prediction of weighted bipartite network.

Description

This function utilizes several data imputation methods in order to predict the existence of a link between two nodes by imputing the edges' weight in a weighted bipartite network of nominal data.

Usage

predictEdge(inc_mat, method = c("svd", "median", "als", "CA"))

Arguments

inc_mat	An incidence matrix containing missing values (edge weights), represented by NAs.
method	A string or list of string. By default, it is set to this list: c("svd", "median", "als", "CA"). Other available methods in MICE, knn, FAMD, PCA, and pmm, can be called to perform at a single step.

Details

This function performs a variety of numerical imputations according to the user's input, and returns a list of imputed data matrices based on each method separately, such as median which replaces the missing values with the median of each rows (observations), and knn which uses the k-Nearest Neighbour algorithm to impute missing values.

Value

A list of matrices with original and imputed values by different methods.

See Also

knn.impute, softImpute, imputeCA, imputeFAMD, imputePCA, mice.

Examples

# load part of the beatAML data
beatAML_data <- NIMAA::beatAML[1:10000,]

# convert to incidence matrix
beatAML_incidence_matrix <- nominalAsBinet(beatAML_data)

# predict the edges by imputation the weights
predictEdge(beatAML_incidence_matrix)

jafarilab/NIMAA documentation built on July 29, 2023, 5:36 a.m.