metasnf (version 2.1.2)

plot.ari_matrix: Heatmap of pairwise adjusted rand indices between solutions

Description

Heatmap of pairwise adjusted rand indices between solutions

Usage

# S3 method for ari_matrix
plot(
  x,
  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(x), max(x)), c(min_colour, max_colour)),
  ...
)
meta_cluster_heatmap(
  x,
  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(x), max(x)), c(min_colour, max_colour)),
  ...
)

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.

Arguments

x

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.

Examples

# \donttest{
    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 <- plot(
        sol_aris,
        order = meta_cluster_order,
        split_vector = split_vec
    )
# }