logistic_cal {pmcalibration} | R Documentation |
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)