Skip to contents

Compute pairwise functional distances and cluster curves

Source: R/functional_distances.R
functional_distances.Rd

Computes L2 functional distances among treatment mean trajectories evaluated on a common time grid from a functional_curves object. Also computes curve-level distances, performs hierarchical clustering, and optionally performs permutation testing.

Usage

functional_distances(
  object,
  cluster_k = 4,
  hc_method = "ward.D2",
  test_factor = NULL,
  n_perm = 999,
  test_mode = c("auto", "none", "global", "global_pairwise"),
  min_strata_for_pairwise = 6,
  perm_unit = NULL,
  perm_strata = NULL,
  bootstrap = FALSE,
  boot_B = 399,
  boot_seed = 1,
  boot_ci = c(0.025, 0.975),
  show_progress = TRUE,
  curve_level = NULL,
  ...
)

Arguments

object

An object of class functional_curves.

cluster_k

Number of clusters used to cut the hierarchical tree.

hc_method

Clustering linkage method passed to stats::hclust().

test_factor

Optional a priori grouping used for a distance-based permutation test.

n_perm

Number of permutations for the distance-based test.

test_mode

Character; permutation test mode.

min_strata_for_pairwise

Minimum number of strata levels required for pairwise tests.

perm_unit

Optional character naming the unit at which group labels are permuted.

perm_strata

Optional character naming a factor defining restricted permutation strata.

bootstrap

Logical; if TRUE, compute bootstrap envelopes and distance summaries.

boot_B

Integer number of bootstrap replicates.

boot_seed

Integer seed for bootstrap resampling.

boot_ci

Length-2 numeric vector of quantiles for bootstrap intervals.

show_progress

Logical; whether to show progress.

curve_level

Logical; whether to compute curve-level distance matrix.

...

Additional arguments.

Value

An object of class "functional_distances" containing:

  • distance: treatment-by-treatment functional distance matrix;

  • distance_table: pairwise distances as a data frame;

  • hc: hclust object;

  • clusters: treatment cluster assignments;

  • curve_distance: curve-by-curve distance matrix;

  • test: permutation test results;

  • bootstrap: bootstrap results if requested.

Examples

if (FALSE) { # \dontrun{
fd <- functional_distances(fc, cluster_k = 4)
print(fd)
plot_dendrogram(fd)
plot_curves(fd)
} # }