Summarize a logistic_cal object
Usage
# S3 method for class 'logistic_cal'
summary(object, conf_level = 0.95, ...)Value
estimates and conf_level*100 confidence intervals for calibration intercept and calibration slope.
The former is estimated from a glm (family = binomial("logit")) where the linear predictor (logit(p)) is included as an offset.
Results of the three likelihood ratio tests described by Miller et al. (2013) (see details).
Details
The likelihood ratio tests proposed by Miller et al. test the following: The first assesses weak calibration overall by testing the null hypothesis that the intercept (a) and slope (b) are equal to 0 and 1, respectively. The second assesses calibration in the large and tests the intercept against 0 with the slope fixed to 1. The third test assesses the calibration slope after correcting for calibration in the large (by estimating a new intercept term). Note the p-values from the calibration intercept and calibration slope estimates will typically agree with the p-values from the second and third likelihood ratio tests but will not always match perfectly as the former are based on z-statistics and the latter are based on log likelihood differences (chi-squared statistics).