gg_sdependent: Signal-variable detection from an unsupervised varPro fit

In ggRandomForests: Visually Exploring Random Forests

Signal-variable detection from an unsupervised varPro fit

Description

Tidy wrapper around varPro::sdependent() for a uvarpro object. Where gg_udependent() draws the cross-variable dependency graph, gg_sdependent() surfaces sdependent()'s signal-variable detection: a ranked table of the per-variable signal score and graph degree, with the variables flagged as "signal" (those whose dependency structure clears the detection threshold).

Usage

gg_sdependent(
  object,
  ...,
  threshold = 0.25,
  q.signal = 0.75,
  directed = TRUE,
  min.degree = NULL,
  beta_fit = NULL
)

Arguments

object	A uvarpro object from varPro::uvarpro().
...	Forwarded to varPro::get.beta.entropy() when beta_fit = NULL; ignored, with a warning, when beta_fit is supplied.
threshold, q.signal, directed, min.degree	Passed to varPro::sdependent() (defaults match gg_udependent()).
beta_fit	Optional precomputed varPro::get.beta.entropy() matrix.

Details

sdependent() runs on the varPro::get.beta.entropy() lasso-coefficient matrix and returns, with plot = FALSE, a list of imp.score (per-variable signal score), degree (node degree in the dependency graph), and signal.vars (the detected signal set). This wrapper tidies that into one row per candidate variable, ranked by imp.score. Because the entropy matrix is the expensive part, beta_fit accepts a precomputed varPro::get.beta.entropy() matrix (shared with gg_beta_uvarpro() and gg_udependent()).

Value

A gg_sdependent object (a data.frame), one row per candidate variable, most-signal first, with columns:

variable

factor; levels reversed so the top variable lands at the top after coord_flip().

imp_score

sdependent() per-variable signal score.

degree

node degree in the dependency graph.

signal

logical; variable is in sdependent()$signal.vars.

The provenance attribute records source, family ("unsupv"), threshold, q.signal, directed, n_signal, and n_var.

See Also

gg_udependent() (the dependency graph), gg_beta_uvarpro() (lasso importance), varPro::sdependent(), varPro::uvarpro().

Examples

if (requireNamespace("varPro", quietly = TRUE)) {
  set.seed(1)
  o <- varPro::uvarpro(mtcars, ntree = 50)
  gg <- gg_sdependent(o)
  plot(gg)
}

ggRandomForests documentation built on Aug. 4, 2026, 5:09 p.m.