Quality Measures • metasnf

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
data_list <- generate_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 settings_matrix
settings_matrix <- generate_settings_matrix(
    data_list,
    nrow = 15,
    seed = 42
)

# collect similarity matrices and solutions matrix from batch_snf
batch_snf_results <- batch_snf(
    data_list,
    settings_matrix,
    return_similarity_matrices = TRUE
)

solutions_matrix <- batch_snf_results$"solutions_matrix"
similarity_matrices <- batch_snf_results$"similarity_matrices"

# calculate Davies-Bouldin indices
davies_bouldin_indices <- calculate_db_indices(
    solutions_matrix,
    similarity_matrices
)

# calculate Dunn indices
dunn_indices <- calculate_dunn_indices(
    solutions_matrix,
    similarity_matrices
)

# calculate silhouette scores
silhouette_scores <- calculate_silhouettes(
    solutions_matrix,
    similarity_matrices
)

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