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
)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.
Examples
# Using just the base distance metrics ------------------------------------
dist_fns_list <- dist_fns_list()
#> ℹ No distance functions specified. Using defaults.
# 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
)