Densify clustered paths

Usage

densify_cluster(
  x,
  GOF_rank = Inf,
  n = 10L,
  max_distance = 1,
  samples = 500,
  replace = TRUE
)

Arguments

x: clustered t-T paths. Output of cluster_paths() merged (or joined) with paths.
GOF_rank: numeric. Selects only the GOF_rank-th highest GOF ranked paths. If all GOFs should be used, set to Inf. Default is 10.
n: integer. Adds n (10 by default) equally-spaced extra points along each path segment (between vertices).
max_distance: numeric. Adds points at a maximum distance of max_distance (in Myr) from each other. 1 by default.
samples: integer or character. Number of random samples of the data. This number should be less or equal then the amount of paths. The default is 100. Paths will be randomly selected after the data has been filtered by the GOF_rank(). Optional, set samples to 'all' to consider all paths ignoring the GOF_rank() filter (this sets GOF to Inf). Set samples to GOF to consider all paths after the GOF_rank filter.
replace: logical. Should sampling be with replacement?

Value

tibble

Examples

if (FALSE) { # \dontrun{
data(tT_paths)
tT_paths_subset <- subset(tT_paths$paths, Comp_GOF >= 0.5)
cluster_paths(tT_paths_subset, cluster = 3) |>
  merge(tT_paths_subset, by = "segment") |>
  dplyr::group_by(cluster) |>
  densify_cluster()
} # }