compare_model: Compare two networks descriptively

In Nestimate: Network Estimation, Bootstrap, and Higher-Order Analysis

Compare two networks descriptively

Description

Computes a battery of descriptive comparison metrics between two networks or two weight matrices: weight deviations (mean / median / RMS / max absolute difference, relative mean absolute difference, coefficient-of- variation ratio), four correlation measures (Pearson, Spearman, Kendall, distance correlation), five dissimilarity measures (Euclidean, Manhattan, Canberra, Bray-Curtis, Frobenius), five similarity measures (Cosine, Jaccard, Dice, Overlap, RV), pattern agreements, and side-by-side network metrics. Optionally adds centrality differences and centrality correlations.

Usage

compare_model(x, ...)

## S3 method for class 'netobject'
compare_model(
  x,
  y,
  scaling = "none",
  measures = character(0),
  network = TRUE,
  ...
)

## S3 method for class 'cograph_network'
compare_model(
  x,
  y,
  scaling = "none",
  measures = character(0),
  network = TRUE,
  ...
)

## S3 method for class 'matrix'
compare_model(
  x,
  y,
  scaling = "none",
  measures = character(0),
  network = TRUE,
  ...
)

Arguments

x	A netobject, cograph_network, or numeric square matrix.
...	Ignored.
y	A netobject, cograph_network, or numeric square matrix.
scaling	Scaling applied to both weight matrices before comparison. One of: "none" Identity (default). "minmax" (w - \min) / (\max - \min); maps to [0, 1]. "max" w / \max(|w|); preserves sign. "rank" Min-max of average ranks; ordinal scaling. "zscore" (w - \bar w) / s_w; standard score. "robust" (w - \mathrm{med}(w)) / \mathrm{mad}(w); Huber-style robust z-score, resists outliers. "log" \log(w); requires w > 0. "log1p" \log(1 + w); admits w \ge 0. "softmax" Numerically stable softmax over the flattened vector. "quantile" Empirical CDF of the flattened vector. "frobenius" Divide the matrix by its Frobenius norm \|W\|_F = \sqrt{\sum w_{ij}^2}; matrix-level normalisation. "row" Row-stochastic normalisation (each row's absolute values sum to 1). Only meaningful for non-negative matrices; rows summing to zero are left unchanged. Scalings that produce negative weights (zscore, robust) are compatible with network = TRUE because the side-by-side metrics use Nestimate's base-R Floyd-Warshall, which handles negative weights.
measures	Character vector of centrality measures to compare. Empty by default (no centrality block). Valid names are "InStrength", "OutStrength", and "Betweenness". Unknown names are ignored with a warning.
network	Logical. Include side-by-side network metrics from summary()? Default TRUE.

Details

Mirrors tna::compare() numerically. Inputs are converted to weight matrices and scaled before comparison; the choice of scaling determines how weights from different estimators are placed on a common footing.

Value

A net_comparison object: a named list with matrices, difference_matrix, edge_metrics, summary_metrics, optionally network_metrics, centrality_differences, centrality_correlations.

Nestimate documentation built on July 11, 2026, 1:09 a.m.