dx_cm: Create a Confusion Matrix from Predictions and Truth

In overdodactyl/diagnosticSummary: Diagnostic Summaries

Create a Confusion Matrix from Predictions and Truth

Description

This function calculates a confusion matrix from predicted probabilities, true outcomes, a threshold for classification, and a designated positive label. It calculates true positives, false negatives, true negatives, false positives, and several other useful metrics.

Usage

dx_cm(predprob, truth, threshold, poslabel)

Arguments

predprob	Numeric vector of prediction probabilities.
truth	Numeric vector of true binary class outcomes.
threshold	Numeric value to determine the cutoff for classifying predictions as positive.
poslabel	The label of the positive class in the truth data.

Details

The function takes predicted probabilities and a threshold to create binary predictions which are then compared to the true labels to create a confusion matrix. It is useful for evaluating the performance of a binary classification model.

Value

A dataframe object of class "dx_cm" containing the components of the confusion matrix and additional metrics:

• tp: True Positives

• fn: False Negatives

• tn: True Negatives

• fp: False Positives

• dispos: Number of Actual Positives

• disneg: Number of Actual Negatives

• n: Total Number of Observations

• correct: Number of Correct Predictions

• testpos: Number of Predicted Positives

• testneg: Number of Predicted Negatives

Examples

# Example usage:
true_labels <- c(1, 0, 1, 1, 0)
predicted_probs <- c(0.9, 0.3, 0.6, 0.8, 0.1)
cm <- dx_cm(predicted_probs, true_labels, threshold = 0.5, poslabel = 1)
print(cm)

overdodactyl/diagnosticSummary documentation built on Jan. 28, 2024, 10:07 a.m.