Heatmap of pairwise adjusted rand indices between solutions

Usage

meta_cluster_heatmap(
  aris,
  order = NULL,
  cluster_rows = FALSE,
  cluster_columns = FALSE,
  log_graph = FALSE,
  scale_diag = "none",
  min_colour = "#282828",
  max_colour = "firebrick2",
  col = circlize::colorRamp2(c(min(aris), max(aris)), c(min_colour, max_colour)),
  ...
)

Arguments

aris: Matrix of adjusted rand indices from calc_aris()
order: Numeric vector containing row order of the heatmap.
cluster_rows: Whether rows should be clustered.
cluster_columns: Whether columns should be clustered.
log_graph: If TRUE, log transforms the graph.
scale_diag: Method of rescaling matrix diagonals. Can be "none" (don't change diagonals), "mean" (replace diagonals with average value of off-diagonals), or "zero" (replace diagonals with 0).
min_colour: Colour used for the lowest value in the heatmap.
max_colour: Colour used for the highest value in the heatmap.
col: Colour ramp to use for the heatmap.
...: Additional parameters passed to similarity_matrix_heatmap(), the function that this function wraps.

Value

Returns a heatmap (class "Heatmap" from package ComplexHeatmap) that displays the pairwise adjusted Rand indices (similarities) between the cluster solutions of the provided solutions data frame.

Examples

#dl <- data_list(
#    list(cort_sa, "cortical_surface_area", "neuroimaging", "continuous"),
#    list(subc_v, "subcortical_volume", "neuroimaging", "continuous"),
#    list(income, "household_income", "demographics", "continuous"),
#    list(pubertal, "pubertal_status", "demographics", "continuous"),
#    uid = "unique_id"
#)
#
#set.seed(42)
#my_sc <- snf_config(
#    dl = dl,
#    n_solutions = 20,
#    min_k = 20,
#    max_k = 50
#)
#
#sol_df <- batch_snf(dl, my_sc)
#
#sol_df
#
#sol_aris <- calc_aris(sol_df)
#
#meta_cluster_order <- get_matrix_order(sol_aris)
#
## `split_vec` found by iteratively plotting ari_hm or by ?shiny_annotator()
#split_vec <- c(6, 10, 16)
#ari_hm <- meta_cluster_heatmap(
#    sol_aris,
#    order = meta_cluster_order,
#    split_vector = split_vec
#)