swash (version 1.3.2)

binary_metrics_glm: 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
  )

Value

list with two entries:

fit_metrics:

list with fit metrics (sens, spec, ...)

observed_expected:

data.frame with observed, expected and hit (1/0)

Arguments

logit_model

glm object with binary logit model

threshold

Threshold for destinction of probability with respect to TRUE or FALSE

Author

Thomas Wieland

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.

References

Altman DG, Bland JM (1994) Diagnostic tests. 1: Sensitivity and specificity. British Medical Journal 308, 1552. 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)