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Construct an ARI matrix storing inter-solution similarities

Source: R/ari_matrix.R
calc_aris.Rd

This function constructs an ari_matrix class object from a solutions_df class object. The ARI matrix stores pairwise adjusted Rand indices for all cluster solutions as well as a numeric order for the solutions data frame based on the hierarchical clustering of the ARI matrix.

Usage

calc_aris(
  sol_df,
  processes = 1,
  verbose = FALSE,
  dist_method = "euclidean",
  hclust_method = "complete"
)

Arguments

sol_df

Solutions data frame containing cluster solutions to calculate pairwise ARIs for.

processes

Specify number of processes used to complete calculations

  • 1 (default) Sequential processing

  • 2 or higher: Parallel processing will use the future.apply::future_apply to distribute the calculations across the specified number of CPU cores. If higher than the number of available cores, a warning will be raised and the maximum number of cores will be used.

  • max: All available cores will be used. Note that no progress indicator is available during multi-core processing.

verbose

If TRUE, output progress to console.

dist_method

Distance method to use when calculating sorting order to of the matrix. Argument is directly passed into stats::dist. Options include "euclidean", "maximum", "manhattan", "canberra", "binary", or "minkowski".

hclust_method

Agglomerative method to use when calculating sorting order by stats::hclust. Options include "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", or "centroid".

Value

om_aris ARIs between clustering solutions of an solutions data frame

Examples

dl <- data_list(
    list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
    list(pubertal, "pubertal_status", "demographics", "continuous"),
    uid = "unique_id"
)
#> ℹ 168 observations dropped due to incomplete data.

sc <- snf_config(dl, n_solutions = 3)
#> ℹ No distance functions specified. Using defaults.
#> ℹ No clustering functions specified. Using defaults.
sol_df <- batch_snf(dl, sc)
calc_aris(sol_df)
#> ARI matrix for 3 cluster solutions.
#>           1         2         3
#> 1 1.0000000 0.7903103 0.5529381
#> 2 0.7903103 1.0000000 0.6327096
#> 3 0.5529381 0.6327096 1.0000000
#> ARI-based order: 3 1 2