| cal_metrics {pmcalibration} | R Documentation |
Calculate calibration metrics from calibration curve
Description
Calculates metrics used for summarizing calibration curves. See Austin and Steyerberg (2019)
Usage
cal_metrics(p, p_c)
Arguments
p |
predicted probabilities |
p_c |
probabilities from the calibration curve |
Value
a named vector of metrics based on absolute difference between predicted and calibration curve implied probabilities d = abs(p - p_c)
Eavg - average absolute difference (aka integrated calibration index or ICI)
E50 - median absolute difference
E90 - 90th percentile absolute difference
Emax - maximum absolute difference
ECI - average squared difference. Estimated calibration index (Van Hoorde et al. 2015)
References
Austin PC, Steyerberg EW. (2019) The Integrated Calibration Index (ICI) and related metrics for quantifying the calibration of logistic regression models. Statistics in Medicine. 38, pp. 1–15. https://doi.org/10.1002/sim.8281
Van Hoorde, K., Van Huffel, S., Timmerman, D., Bourne, T., Van Calster, B. (2015). A spline-based tool to assess and visualize the calibration of multiclass risk predictions. Journal of Biomedical Informatics, 54, pp. 283-93
Van Calster, B., Nieboer, D., Vergouwe, Y., De Cock, B., Pencina M., Steyerberg E.W. (2016). A calibration hierarchy for risk models was defined: from utopia to empirical data. Journal of Clinical Epidemiology, 74, pp. 167-176
Examples
library(pmcalibration)
LP <- rnorm(100) # linear predictor
p_c <- invlogit(LP) # actual probabilities
p <- invlogit(LP*1.3) # predicted probabilities that are miscalibrated
cal_metrics(p = p, p_c = p_c)