plot.gg_udependent: Plot a 'gg_udependent' variable dependency graph

In ggRandomForests: Visually Exploring Random Forests

Plot a gg_udependent variable dependency graph

Description

Draws the dependency graph held in a gg_udependent object as a ggraph network. Node colour marks whether a variable made the signal set, and the width and opacity of an edge tell you how strong the dependency between its two variables is.

Usage

## S3 method for class 'gg_udependent'
plot(x, layout = "fr", ...)

Arguments

x	A gg_udependent object from gg_udependent.
layout	Character; the igraph/ggraph layout algorithm. Common choices are "fr" (Fruchterman-Reingold, the default), "kk" (Kamada-Kawai), "stress", "circle", and "grid".
...	Not currently used.

Details

This plot needs the ggraph package, which is in Suggests rather than installed for you. If it is missing, run install.packages("ggraph").

A signal variable (selected = TRUE) gets a blue node (#4e8fcd); the rest are grey (#888888). Node size grows with degree. Edge width and opacity both grow with the raw dependency weight I[i,j].

Value

A ggplot object (built via ggraph).

Reading the network

Each node is a variable; each edge is a cross-variable dependency that cleared the threshold passed to gg_udependent. The Fruchterman-Reingold layout (the default) places mutually connected variables near each other, so the picture tends to show hubs and the clusters around them rather than a tidy ring. The eye usually goes first to the largest blue node: a variable that is both in the signal set and connects to many others is a hub of the dependency structure. Edges with wider, more opaque strokes are stronger dependencies; thin, faint edges sit near the threshold and are the ones that would disappear first if you raised it.

Grey, low-degree nodes are the ones UVarPro thinks are not contributing much to the structure. (Truly isolated nodes are dropped by gg_udependent() before the graph is drawn; what you see is the connected component.) A cluster of mutually connected variables is worth checking for redundancy; they may be several views of the same underlying quantity.

What this tells you

Use the figure to pick a working set of variables: the hubs and their immediate neighbours are the candidates UVarPro flags as carrying structure. If a cluster of high-degree variables looks like it might be measuring the same thing, that is a cue to look at their pairwise correlations or fit them as a block rather than individually. The threshold and layout are recorded in the caption so a different choice is easy to spot in a later figure.

See Also

gg_udependent

Examples

if (requireNamespace("ggraph", quietly = TRUE)) {
  set.seed(42)
  uv <- varPro::uvarpro(iris[, -5], ntree = 50)
  plot(gg_udependent(uv))
}

ggRandomForests documentation built on June 13, 2026, 5:07 p.m.