Variable dependency graph from a uvarpro model

A uvarpro fit records how strongly each variable depends on the others. This function pulls those cross-variable dependency scores from the fit with get.beta.entropy and sdependent, and returns them as a tidy list that plot.gg_udependent can draw as a network.

Usage

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

Arguments

object: A fitted uvarpro object (required).
threshold: Numeric; the positive dependency threshold passed on to sdependent(). An edge $i \to j$ is drawn when I[i, j] >= threshold. Default 0.25.
q.signal: Quantile threshold (0–1) for picking out the signal variables; passed on to sdependent(). Default 0.75.
directed: Logical; TRUE (default) builds a directed igraph.
min.degree: Integer or NULL. When set, only nodes with degree $\ge$ min.degree are kept in $nodes, $edges, and $graph.
...: Additional arguments forwarded to varPro::sdependent().

Value

A named list of class "gg_udependent" with elements:

$edges: Data frame: variable_from, variable_to, weight (raw cross-importance value).
$nodes: Data frame: variable (factor, levels by descending degree), degree (integer; out-degree when directed = TRUE, total degree when directed = FALSE), selected (logical, TRUE if in sdependent's signal set).
$graph: igraph object. NULL if no dependencies detected.

A "provenance" attribute carries threshold, q.signal, directed, min.degree, xvar.names, and n.

What cross-variable dependency is doing

UVarPro (Zhou, Lu and Ishwaran, 2026) extends the varpro framework to the unsupervised setting: grow a forest without a response, then use the same region-release contrasts varpro uses for supervised importance to ask, "which variables explain the structure in the data?" The lasso-driven variant frames each region-release contrast as a classification task (does an observation belong to the region or to its release?) and fits a lasso logistic regression with the other variables as predictors. The coefficient on variable $j$ in the model for variable $i$'s region-release contrast is the entry $I[i, j]$ of the matrix varPro::get.beta.entropy() returns.

Read that entry as "how much does knowing $j$ help separate $i$'s region from its release". A large $I[i, j]$ says $j$ carries information about the structure varpro picked up in $i$. varPro::sdependent thresholds that matrix at a user-chosen cut and returns the set of "signal" variables: the nodes with high enough out-degree to be worth keeping. We pass the threshold through to sdependent and use the same matrix to weight the edges of the resulting graph.

The graph is directed by default because $I[i, j]$ and $I[j, i]$ are separate lasso coefficients and need not agree; setting directed = FALSE collapses each pair by taking the larger of the two, which is appropriate when you only want to see that two variables are dependent, not which way the dependency reads.

What's in the output

$edges has one row per surviving edge with the raw weight I[i, j] (or, for undirected graphs, the max of the two directions). $nodes has one row per surviving variable with its degree (out-degree for directed, total degree for undirected) and a selected flag for membership in the sdependent signal set. $graph is the same information packaged as an igraph object, with weight, degree, and selected attached so plot.gg_udependent can render it without recomputing anything.

What you use this for

screen a wide unsupervised dataset for the small set of variables UVarPro thinks are carrying the signal: the nodes with high degree, or those flagged selected = TRUE;
spot clusters of mutually dependent variables (hubs and the spokes around them) that may be measuring the same underlying construct;
compare two datasets, or two preprocessing pipelines, by looking at how their dependency graphs change.

An edge in this graph is a statistical dependency in the unsupervised decomposition of the data. It is not a causal arrow. A high $I[i, j]$ says $j$ predicts $i$'s region membership, not that $j$ causes $i$.

References

Zhou, L., Lu, M. and Ishwaran, H. (2026). Variable priority for unsupervised variable selection. Pattern Recognition, 172:112727.

Examples

# \donttest{
set.seed(42)
uv <- varPro::uvarpro(iris[, -5], ntree = 50)
gg <- gg_udependent(uv)
print(gg)
#> <gg_udependent>  n=150  p=4  threshold=0.25
#>   Edges: 6  Nodes in graph: 3  Selected: 1/3
# }