cumulcalib

The goal of cumulcalib is to enable the assessment of prediction model calibration using the cumulative calibration methodology. For more information, please refer to the original publication (https://doi.org/10.1002/sim.10138). The package also comes with a tutorial, which you can access on CRAN or view after installing the package as

vignette("tutorial", package="cumulcalib")

Installation

The package can be installed from CRAN:

install.packages("cumulcalib")

You can also install the development version from GitHub with:

# install.packages("remotes") #this package is necessary to connect to github
remotes::install_github("resplab/cumulcalib")

Example

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: 0.28191196504736

plot(res, draw_sig=F)