metasnf (version 2.1.2)

dist_fns_list: Build a distance metrics list

Description

The distance metrics list object (inherits classes dist_fns_list and list) is a list that stores R functions which can convert a data frame of features into a matrix of pairwise distances. The list is a nested one, where the first layer of the list can hold up to 5 items (one for each of the metasnf recognized feature types, continuous, discrete, ordinal, categorical, and mixed), and the second layer can hold an arbitrary number of distance functions for each of those types.

Usage

dist_fns_list(
  cnt_dist_fns = NULL,
  dsc_dist_fns = NULL,
  ord_dist_fns = NULL,
  cat_dist_fns = NULL,
  mix_dist_fns = NULL,
  automatic_standard_normalize = FALSE,
  use_default_dist_fns = FALSE
)

Value

A distance metrics list object.

Arguments

cnt_dist_fns

A named list of continuous distance metric functions.

dsc_dist_fns

A named list of discrete distance metric functions.

ord_dist_fns

A named list of ordinal distance metric functions.

cat_dist_fns

A named list of categorical distance metric functions.

mix_dist_fns

A named list of mixed distance metric functions.

automatic_standard_normalize

If TRUE, will automatically use standard normalization prior to calculation of any numeric distances. This parameter overrides all other distance functions list-related parameters.

use_default_dist_fns

If TRUE, prepend the base distance metrics (euclidean distance for continuous, discrete, and ordinal data and gower distance for categorical and mixed data) to the resulting distance metrics list.

Details

Call ?distance_metrics to see all distance metric functions provided in metasnf.

Examples

# Using just the base distance metrics  ------------------------------------
dist_fns_list <- dist_fns_list()

# Adding your own metrics --------------------------------------------------
# This will contain only the and user-provided distance function:
cubed_euclidean <- function(df, weights_row) {
    # (your code that converts a data frame to a distance metric here...)
    weights <- diag(weights_row, nrow = length(weights_row))
    weighted_df <- as.matrix(df) %*% weights
    distance_matrix <- weighted_df |>
        stats::dist(method = "euclidean") |>
        as.matrix()
    distance_matrix <- distance_matrix^3
    return(distance_matrix)
}

dist_fns_list <- dist_fns_list(
    cnt_dist_fns = list(
         "my_cubed_euclidean" = cubed_euclidean
    )
)

# Using default base metrics------------------------------------------------
# Call ?distance_metrics to see all distance metric functions provided in
# metasnf. The code below will contain a mix of user-provided and built-in
# distance metric functions.
dist_fns_list <- dist_fns_list(
    cnt_dist_fns = list(
         "my_distance_metric" = cubed_euclidean
    ),
    dsc_dist_fns = list(
         "my_distance_metric" = cubed_euclidean
    ),
    ord_dist_fns = list(
         "my_distance_metric" = cubed_euclidean
    ),
    cat_dist_fns = list(
         "my_distance_metric" = gower_distance
    ),
    mix_dist_fns = list(
         "my_distance_metric" = gower_distance
    ),
    use_default_dist_fns = TRUE
)