Signal-variable detection from an unsupervised varPro fit

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.

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.

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()).

Examples

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

# }