validateEdgePrediction: Validate and compare edge prediction methods.

In NIMAA: Nominal Data Mining Analysis

Validate and compare edge prediction methods.

Description

This function compares the imputation approaches for predicting edges using the clustering result of a submatrix with non-missing values as a benchmark. This function performs the same analysis as the findCluster function on every imputed incidence matrices independently. Then, using different similarity measures, all imputation approaches are compared to each other, revealing how edge prediction methods affects network communities (clusters). The best method should result in a higher degree of similarity (common node membership) to the non-missing submatrix as a benchmark.

Usage

validateEdgePrediction(imputation, refer_community, clustering_args)

Arguments

imputation	A list or a matrix containing the results of imputation method (s).
refer_community	An igraph community object obtained through findCluster using a given method.
clustering_args	A list indicating the clustering arguments values used in findCluster for a given method. This list is retrievable from the output of findCluster.

Value

A list containing the following indices: Jaccard similarity, Dice similarity coefficient, Rand index, Minkowski (inversed), and Fowlkes-Mallows index. The higher value indicates the greater similarity between the imputed dataset and the benchmark.

Examples

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

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

# do clustering
cls <- findCluster(beatAML_incidence_matrix, part = 1)

# predict the edges by imputation the weights
imputed_beatAML <- predictEdge(beatAML_incidence_matrix)

# validate the edge prediction
validateEdgePrediction(imputation = imputed_beatAML,
refer_community = cls$fast_greedy,
clustering_args = cls$clustering_args
)

NIMAA documentation built on April 11, 2022, 5:05 p.m.