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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
set.seed(42)
settings_matrix <- generate_settings_matrix(
data_list,
nrow = 15
)
# 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]])These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.