Quality metrics

These functions calculate conventional metrics of cluster solution quality.

Usage

calculate_silhouettes(sol_df)

calculate_dunn_indices(sol_df)

calculate_db_indices(sol_df)

Arguments

sol_df

A solutions_df class object created by batch_snf() with the parameter return_sim_mats = TRUE.

Value

A list of silhouette class objects, a vector of Dunn indices, or a vector of Davies-Bouldin indices depending on which function was used.

Details

calculate_silhouettes: A wrapper for cluster::silhouette that calculates silhouette scores for all cluster solutions in a provided solutions data frame. Silhouette values range from -1 to +1 and indicate an overall ratio of how close together observations within a cluster are to how far apart observations across clusters are. You can learn more about interpreting the results of this function by calling ?cluster::silhouette.

calculate_dunn_indices: Calculates Dunn indices for all cluster solutions in a provided solutions data frame.

calculate_db_indices: Calculates Davies-Bouldin indices for all cluster solutions in a provided solutions data frame.

For Dunn and Davies-Bouldin index calculations, a grid of values is presented based on multiple combinations of intra-cluster distance methods (complete or average distance) and inter-cluster distance methods (single-linkage, complete-linkage, average distance, and Hausdorff distance).

Examples

if (FALSE) { # \dontrun{
input_dl <- data_list(
    list(gender_df, "gender", "demographics", "categorical"),
    list(diagnosis_df, "diagnosis", "clinical", "categorical"),
    uid = "patient_id"
)

sc <- snf_config(input_dl, n_solutions = 5)

sol_df <- batch_snf(input_dl, sc, return_sim_mats = TRUE)

# calculate Davies-Bouldin indices
davies_bouldin_indices <- calculate_db_indices(sol_df)

# calculate Dunn indices
dunn_indices <- calculate_dunn_indices(sol_df)

# calculate silhouette scores
silhouette_scores <- calculate_silhouettes(sol_df)
} # }