The goal of cumulcalib is to enable the use of the assessment of prediction model calibration using the cumulative calibration methodology. For more information, please refer to the original publication (arxiv version: https://arxiv.org/abs/2307.09713). The package also comes with a tutorial, which you can view after installing the package as
vignette("tutorial", package="cumulcalib")You can install the development version of cumulcalib from GitHub with:
# install.packages("remotes") #this package is necessary to connect to github
remotes::install_github("resplab/cumulcalib")library(cumulcalib)
set.seed(1)
p <- rbeta(1000, 1,5)
y <- rbinom(1000,1,p)
res <- cumulcalib(y, p)
summary(res)
#> C_n (mean calibration error): 0.00532270104567871
#> C* (maximum absolute cumulative calibration error): 0.00740996981029672
#> Method: Two-part Brownian Bridge (BB)
#> S_n (Z score for mean calibration error) 0.489295496431201
#> B* (test statistic for maximum absolute bridged calibration error): 0.904915434767163
#> Component-wise p-values: mean calibration=0.624632509005787 | Distance (bridged)=0.385979705481866
#> Combined p-value (Fisher's method): 0.584068794836004
#> Location of maximum drift: 812 | time value: 0.632911942275094 | predictor value: 0.28191196504736plot(res, draw_sig=F)