calibrate_logistic: Calibrate logistic coefficients to prevalence and treatment...

In simulab: Simulates a Wide Variety of Data Shapes, Sizes and Distributions

Calibrate logistic coefficients to prevalence and treatment contrast

Description

Calibrate logistic coefficients to prevalence and treatment contrast

Usage

calibrate_logistic(
  data,
  coefficients,
  prevalence,
  risk_ratio = NULL,
  risk_difference = NULL,
  treatment = "treatment",
  treatment_prevalence = 0.5,
  auc = NULL,
  tolerance = 1e-08
)

Arguments

data	Covariate data.
coefficients	Named covariate coefficients.
prevalence	Target population prevalence.
risk_ratio	Optional treated/control risk ratio.
risk_difference	Optional treated-control risk difference.
treatment	Name for the optional treatment coefficient.
treatment_prevalence	Treatment prevalence used for calibration.
auc	Optional target population area under the ROC curve. Covariate coefficients are proportionally scaled to reach this target.
tolerance	Numerical root tolerance.

Value

A base data.frame with one row per calibrated coefficient and columns term, coefficient, calibration and coefficient_scale. Coefficients are log-odds on the logit scale. The first row is "(Intercept)", followed by the treatment row when risk_ratio or risk_difference was supplied, then one row per name of coefficients. calibration names the target that was solved for – "prevalence", "auc", "risk_ratio" or "risk_difference" – and coefficient_scale is the factor the supplied covariate coefficients were multiplied by, which is 1 unless auc was given.

Examples

set.seed(1)
covariates <- data.frame(x1 = stats::rnorm(200), x2 = stats::rnorm(200))

calibrate_logistic(
  covariates, coefficients = c(x1 = 0.5, x2 = -0.3), prevalence = 0.2
)

# Scale the coefficients to hit a target discrimination instead.
calibrate_logistic(
  covariates, coefficients = c(x1 = 0.5, x2 = -0.3),
  prevalence = 0.2, auc = 0.75
)

simulab documentation built on Oct. 15, 2026, 5:08 p.m.