logistic_cal: Run logistic calibration

In pmcalibration: Calibration Curves for Clinical Prediction Models

Run logistic calibration

Description

Fit the models required to assess calibration in the large (calibration intercept), calibration slope, and overall 'weak' calibration (see, e.g., Van Calster et al. 2019). Fits the models required to do the three likelihood ratio tests described by Miller et al. (1993) (see summary.logistic_cal).

Usage

logistic_cal(y, p)

Arguments

y	binary outcome
p	predicted probabilities (these will be logit transformed)

Value

an object of class logistic_cal containing glm results for calculating calibration intercept, calibration slope, and LRTs.

References

Van Calster, B., McLernon, D. J., Van Smeden, M., Wynants, L., & Steyerberg, E. W. (2019). Calibration: the Achilles heel of predictive analytics. BMC medicine, 17(1), 1-7.

Miller, M. E., Langefeld, C. D., Tierney, W. M., Hui, S. L., & McDonald, C. J. (1993). Validation of probabilistic predictions. Medical Decision Making, 13(1), 49-57.

Examples

library(pmcalibration)
# simulate some data
n <- 500
dat <- sim_dat(N = n, a1 = .5, a3 = .2)

# predictions
p <- with(dat, invlogit(.5 + x1 + x2 + x1*x2*.1))

logistic_cal(y = dat$y, p = p)

pmcalibration documentation built on April 4, 2025, 2:23 a.m.