inference: Inference Errors

In erictleung/poofsi: Learn concepts from Nature's "Points of Significance"

Description

Returns the specificity and sensitivity values for a given alpha and beta value.

Usage

inference(alpha = 0.05, beta = 0.2)

Arguments

alpha	Type I error or the false positive error
beta	Type II error or the false negative error

Details

Specificity is also known as the true negative rate, which is calculated as the number of true negatives divided by the sum of true negatives and false positives.

Sensitivity is also known as the true positive rate, which is calculated as the number of true positives divided by the sum of true positives and false negatives.

Value

list containing the specificity and sensitivity

Examples

inference(alpha = 0.05, beta = 0.2)
inference(alpha = 0.001, beta = 0.2)
inference(alpha = 0.05, beta = 0.5)

erictleung/poofsi documentation built on May 16, 2019, 8:41 a.m.