network.confusion: Confusion Matrix Metrics for Edge Comparison and Recovery

In EGAnet: Exploratory Graph Analysis – a Framework for Estimating the Number of Dimensions in Multivariate Data using Network Psychometrics

Confusion Matrix Metrics for Edge Comparison and Recovery

Description

Computes many commonly used confusion matrix metrics

Usage

network.confusion(
  base,
  comparison,
  metric = c("all", "sen", "spec", "ppv", "npv", "fdr", "fom", "ba", "f1", "csi", "mcc"),
  full.names = FALSE
)

Arguments

base	Matrix or data frame. Network that will be treated as the "ground truth" such that a false positive represents an edge that is present in comparison but not in this network
comparison	Matrix or data frame. Network that will be treated as the estimator such that a false positive represents an edge that is present in this network but not in base
metric	Character vector. Defaults to "all" metrics. Available options: "all" — All available metrics (default) "sen" — Sensitivity (True Positive Rate): \frac{TP}{TP + FN} "spec" — Specificity (True Negative Rate): \frac{TN}{TN + FP} "ppv" — Positive Predictive Value (Precision): \frac{TP}{TP + FP} "npv" — Negative Predictive Value: \frac{TN}{TN + FN} "fdr" — False Discovery Rate: 1 - PPV = \frac{FP}{TP + FP} "fom" — False Omission Rate: 1 - NPV = \frac{FN}{TN + FN} "ba" — Balanced Accuracy: \frac{Sensitivity + Specificity}{2} "f1" — F1 Score (harmonic mean of PPV and Sensitivity): \frac{2TP}{2TP + FP + FN} "csi" — Critical Success Index (Jaccard / Threat Score): \frac{TP}{TP + FP + FN} "mcc" — Matthews Correlation Coefficient: \frac{TP \times TN - FP \times FN}{\sqrt{(TP+FP)(TP+FN)(TN+FP)(TN+FN)}}
full.names	Boolean (length = 1). Whether full or abbreviated names should be used. Defaults to FALSE. Set to TRUE for full names

Value

Returns a named vector of confusion matrix metrics

Author(s)

Hudson Golino <hfg9s at virginia.edu> and Alexander P. Christensen <[e-mail]>

Examples

# Load data
wmt <- wmt2[,7:24]

# Set split
split <- sample(1:nrow(wmt), round(nrow(wmt) / 2))

# Estimate networks
split1 <- network.estimation(wmt[split,])
split2 <- network.estimation(wmt[-split,])

# Estimate metrics
network.confusion(split1, split2)

EGAnet documentation built on April 13, 2026, 5:07 p.m.