dist_fns: Built-in distance functions

In metasnf: Meta Clustering with Similarity Network Fusion

Built-in distance functions

Description

These functions can be used when building a metasnf distance functions list. Each function converts a data frame into to a distance matrix.

Usage

euclidean_distance(df, weights_row)

gower_distance(df, weights_row)

sn_euclidean_distance(df, weights_row)

sew_euclidean_distance(df, weights_row)

hamming_distance(df, weights_row)

Arguments

df	Data frame containing at least 1 data column
weights_row	Single-row data frame where the column names contain the column names in df and the row contains the corresponding weights_row.

Details

Functions that work for numeric data:

• euclidean_distance: typical Euclidean distance

• sn_euclidean_distance: Data frame is first standardized and normalized before typical Euclidean distance is applied

• siw_euclidean_distance: Squared (including weights) Euclidean distance, where the weights are also squared

• sew_euclidean_distance: Squared (excluding weights) Euclidean distance, where the weights are not also squared

Functions that work for binary data:

• hamming_distance: typical Hamming distance

Functions that work for any type of data:

• gower_distance: Gower distance (cluster::daisy)

Value

A matrix class object containing pairwise distances.

metasnf documentation built on April 3, 2025, 5:40 p.m.