`network.confusion()`

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 <alexpaulchristensen@gmail.com>

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)