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 derived by the specified hierarchical clustering method applied to the provided matrix.
Examples
# \donttest{
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"
)
#> ℹ 188 observations dropped due to incomplete data.
sc <- snf_config(
dl = dl,
n_solutions = 20,
min_k = 20,
max_k = 50
)
#> ℹ No distance functions specified. Using defaults.
#> ℹ No clustering functions specified. Using defaults.
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)
# }