binary_metrics_glm: Fit metrics for binary logit model

In swash: Swash-Backwash Model for the Single Epidemic Wave

Fit metrics for binary logit model

Description

Calculation of fit metrices for binary variables (Sensitivity, specificity, accuracy) out of binary logit models (glm object)

Usage

binary_metrics_glm(
  logit_model, 
  threshold = 0.5
  )

Arguments

logit_model	glm object with binary logit model
threshold	Threshold for destinction of probability with respect to TRUE or FALSE

Details

The function computes model performance metrices for binary outcomes. A binary logit model (glm) must be stated by the user. The function returns sensitivity, specificity, accurracy, and no-information rate.

Value

list with two entries:

fit_metrics:	list with fit metrics (sens, spec, ...)
observed_expected:	data.frame with observed, expected and hit (1/0)

Author(s)

Thomas Wieland

References

Altman DG, Bland JM (1994) Diagnostic tests. 1: Sensitivity and specificity. British Medical Journal 308, 1552. \Sexpr[results=rd]{tools:::Rd_expr_doi("https://doi.org/10.1136/bmj.308.6943.1552")}.

Boehmke B, Greenwell B (2020) Hands-On Machine Learning with R (1 ed.). Taylor & Francis, New York, NY.

See Also

metrics, binary_metrics

Examples

dep <- c(1,1,0,0,0,0,1,0,1, 1)
x <- c(2,3,1,1,0,1,3,2,1,3)

testmodel <-
  glm(
    dep~x,
    family=binomial()
  )
  
summary(testmodel)

binary_metrics_glm(testmodel)

swash documentation built on Feb. 15, 2026, 5:07 p.m.