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.
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
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.
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)
#> tp fn tn fp dispos disneg n correct testpos testneg
#> 1 3 0 2 0 3 2 5 5 3 2
