Quality Measures
In metasnf: Meta Clustering with Similarity Network Fusion

knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>"
)

options(crayon.enabled = FALSE, cli.num_colors = 0)

Download a copy of the vignette to follow along here: quality_measures.Rmd

This vignette walks through calculation of silhouette scores, Dunn indices, and Davies-Boulding indices a we will highlight the main stability measure options in the metasnf package.

To use these functions, you will need to have the clv package installed.

# load package
library(metasnf)

# generate data_list
dl <- data_list(
    list(cort_t, "cort_t", "neuroimaging", "continuous"),
    list(cort_sa, "cort_sa", "neuroimaging", "continuous"),
    list(subc_v, "subc_v", "neuroimaging", "continuous"),
    list(income, "income", "demographics", "continuous"),
    list(pubertal, "pubertal", "demographics", "continuous"),
    uid = "unique_id"
)

# build SNF config
set.seed(42)
sc <- snf_config(
    dl = dl,
    n_solutions = 15
)

# collect similarity matrices and solutions data frame from batch_snf
sol_df <- batch_snf(
    dl = 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)

# plot the silhouette scores of the first solutions
plot(silhouette_scores[[1]])