Heatmap of pairwise adjusted rand indices between solutions

Usage

# S3 method for class '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)),
  ...
)

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.

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

# \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"
    )
#> ℹ 175 observations dropped due to incomplete data.
    
    set.seed(42)
    my_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, my_sc)
    
    sol_df
#> 20 cluster solutions of 100 observations:
#> solution nclust mc uid_NDAR_INV0567T2Y9 uid_NDAR_INV0IZ157F8 
#> 8;5;250m 1     1      8 NA                       5                    2 
#> 8;5;250m 2     2      4 NA                       1                    3 
#> 8;5;250m 3     3      2 NA                       2                    1 
#> 8;5;250m 4     4      2 NA                       2                    1 
#> 8;5;250m 5     5      3 NA                       3                    1 
#> 8;5;250m 6     6      2 NA                       2                    1 
#> 8;5;250m 7     7      4 NA                       1                    2 
#> 8;5;250m 8     8      6 NA                       5                    4 
#> 8;5;250m 9     9      2 NA                       1                    2 
#> 8;5;250m 0    10      2 NA                       2                    1 
#> 8;5;246m  ℹ 10  or ws 
#> 8;5;246m  ℹ 98  or riables: uid_NDAR_INV0J4PYA5F <dbl>, uid_NDAR_INV10OMKVLE <dbl>, 
#> 8;5;246m    uid ND NV15FPCW4O <dbl>, uid_NDAR_INV19NB4RJK <dbl>, 
#> 8;5;246m    uid ND NV1HLGR738 <dbl>, uid_NDAR_INV1KR0EZFU <dbl>, 
#> 8;5;246m    uid ND NV1L3Y9EOP <dbl>, uid_NDAR_INV1TCP5GNM <dbl>, 
#> 8;5;246m    uid ND NV1ZHRDJ6B <dbl>, uid_NDAR_INV2EJ41YSZ <dbl>, 
#> 8;5;246m    uid ND NV2PK6C85M <dbl>, uid_NDAR_INV2XO1PHCT <dbl>, … 
#> 3 solutions and 98 observations not shown.
#> Use `print(n = ...)` to change the number of rows printed.
#> Use `t()` to view compact cluster solution format.
#> 
    
    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
    )
#> Sorting by order.
# }