Return the hierarchical clustering order of a matrix
Arguments
- matrix
Matrix to cluster.
- 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
A numeric vector of the ordering derivied by the specified hierarchical clustering method applied to the provided matrix.
Examples
# dl <- data_list(
# list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
# list(income, "household_income", "demographics", "continuous"),
# list(pubertal, "pubertal_status", "demographics", "continuous"),
# list(anxiety, "anxiety", "behaviour", "ordinal"),
# list(depress, "depressed", "behaviour", "ordinal"),
# uid = "unique_id"
# )
#
# sc <- snf_config(
# dl = dl,
# n_solutions = 20,
# min_k = 20,
# max_k = 50
# )
#
# sol_df <- batch_snf(dl, sc)
#
# ext_sol_df <- extend_solutions(
# sol_df,
# dl = dl,
# min_pval = 1e-10 # p-values below 1e-10 will be thresholded to 1e-10
# )
#
# # Calculate pairwise similarities between cluster solutions
# sol_aris <- calc_aris(sol_df)
#
# # Extract hierarchical clustering order of the cluster solutions
# meta_cluster_order <- get_matrix_order(sol_aris)