FDRCalc: False Discovery Rate (FDR) and True Positive Rate (TPR) from...

In GAReg: Genetic Algorithms in Regression

False Discovery Rate (FDR) and True Positive Rate (TPR) from index labels

Description

Computes the False Discovery Rate (FDR) and True Positive Rate (TPR, a.k.a. recall) by comparing a set of true labels to a set of predicted labels. Labels are treated as positive integer indices in \{1, \dots, N\}. Duplicates are ignored (unique indices are used).

Usage

FDRCalc(truelabel, predlabel, N)

Arguments

truelabel	Integer vector of ground-truth positive indices (values in 1..N).
predlabel	Integer vector of predicted positive indices (values in 1..N).
N	Integer scalar; size of the full index universe (total number of candidates).

Details

Let truelabel and predlabel be sets of indices. The function derives the confusion-matrix counts:

• tp = |truelabel \cap predlabel|

• fp = |predlabel \setminus truelabel|

• fn = |truelabel \setminus predlabel|

• tn = N - tp - fp - fn

and returns

FDR = fp / (fp + tp), \qquad TPR = tp / (tp + fn).

Inputs are coerced to integer and uniqued. A warning is emitted if any label is < 1, and an error is thrown if any label exceeds N. If tn < 0, a warning is issued indicating that N may not reflect the full universe.

Value

A named list with components:

• fdr False Discovery Rate, fp/(fp+tp) (NaN if fp+tp == 0).

• tpr True Positive Rate (recall), tp/(tp+fn) (NaN if tp+fn == 0).

• fp, fn, tp, tn Confusion-matrix counts.

Edge cases

If predlabel is empty, fdr is NaN and tpr is 0 (unless truelabel is also empty, in which case both fdr and tpr are NaN). If truelabel is empty and predlabel non-empty, tpr is NaN and fdr is 1.

Examples

# Simple example
N <- 10
true <- c(2, 4, 7)
pred <- c(4, 5, 7, 7) # duplicates are ignored
FDRCalc(true, pred, N)

# Empty predictions
FDRCalc(true, integer(0), N)

# All correct predictions
FDRCalc(true, true, N)

GAReg documentation built on March 29, 2026, 5:08 p.m.