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Truncated dominant community state types with configurable low-support handling

Source: R/linf.R
linf.csts.Rd

Forms provisional depth-1 dominance sample sets from the dominant feature of each sample and then applies the minimum support threshold n0. Sets with fewer than n0 samples are handled according to low.freq.policy:

  • "pure": retain only sets with support >= n0 as named dCSTs and collapse all low-support sets into rare.label.

  • "absorb": reassign each low-support sample to the retained state with the largest value among the retained features (ties handled by tie.method).

Usage

linf.csts(
  S,
  feature.ids = NULL,
  feature.labels = NULL,
  n0 = 50,
  low.freq.policy = c("pure", "absorb"),
  rare.label = "RARE_DOMINANT",
  tie.method = c("first", "random", "error"),
  return.diagnostics = FALSE,
  return.landmarks = FALSE,
  landmark.types = c("endpoint.max", "endpoint.min"),
  landmark.view = c("active", "pure", "absorb"),
  backend = c("auto", "dense", "sparse")
)

Arguments

S

Numeric matrix (samples x features), typically L-infinity relatives.

feature.ids

Optional character vector of stable feature identifiers, length ncol(S).

feature.labels

Optional character vector of display labels, length ncol(S).

n0

Integer >= 1. Minimum support required to retain a dominance sample set.

low.freq.policy

Character. One of "pure" or "absorb". Default: "pure".

rare.label

Character scalar used when low.freq.policy = "pure". Default: "RARE_DOMINANT".

tie.method

Character. Tie handling passed to linf.dominant.features() and used during absorb reassignment ("first", "random", "error").

return.diagnostics

Logical. If TRUE, return reassignment diagnostics.

return.landmarks

Logical. If TRUE, attach a depth-1 landmark summary computed by linf.landmarks.

landmark.types

Character vector of landmark types passed to linf.landmarks when return.landmarks = TRUE.

landmark.view

Character. Landmark view passed to linf.landmarks when return.landmarks = TRUE.

backend

Character. Matrix backend to use: "auto", "dense", or "sparse". The default "auto" preserves sparse input and otherwise uses the dense path.

Value

List with:

  • depth1.feature.index, depth1.feature.id, depth1.feature.label: active depth-1 assignment

  • lineage.id, lineage.label: active leaf-lineage assignment

  • policy-specific variants of the depth-1 and leaf-lineage fields, ending in .pure or .absorb

  • lineage.ids, lineage.labels: active hierarchy, plus policy-specific .pure and .absorb hierarchies

  • depth: current hierarchy depth

  • retained.feature.indices, retained.feature.ids, retained.feature.labels

  • provisional.feature.index, provisional.feature.id, provisional.feature.label

  • feature.ids, feature.labels

  • size.table, size.table.id

  • n0, low.freq.policy, rare.label

  • diagnostics (if return.diagnostics = TRUE)

  • landmarks (if return.landmarks = TRUE)

Examples

X <- rbind(
  s1 = c(A = 10, B = 2, C = 1),
  s2 = c(A = 9, B = 3, C = 1),
  s3 = c(A = 1, B = 10, C = 2),
  s4 = c(A = 1, B = 9, C = 3),
  s5 = c(A = 1, B = 2, C = 10)
)
fit <- linf.csts(normalize.linf(X), n0 = 2, low.freq.policy = "pure")
table(fit$lineage.label)
#> 
#>             A             B RARE_DOMINANT 
#>             2             2             1 
fit$retained.feature.ids
#> [1] "A" "B"