ciCalibrate is an R package for computing support intervals for unknown
univariate parameters. A support interval can either be computed based on a
parameter estimate and standard error or based on a confidence interval for the
respective parameter. The main function for doing so is ciCalibrate
, see the
documentation with ?ciCalibrate
for the available options. Theoretical
background on support intervals is provided in the accompanying paper Pawel et
al. (2023) and also Wagenmakers
et al. (2020).
## development version from GitHub (requires remotes package)
## remotes::install_github(repo = "SamCH93/ciCalibrate")
## from CRAN
install.packages(pkgs = "ciCalibrate")
library("ciCalibrate")
## data from RECOVERY trial
logHR <- -0.19 # estimate
se <- 0.05 # standard error of estimate
ci95 <- logHR + c(-1, 1) * qnorm(p = 0.975) * se # 95% Wald-CI
## default normal prior for logHR under the alternative H1
pm <- 0 # center around value of no effect
psd <- 2 # unit-information standard deviation for a logHR
## compute a support interval with support level = 10
si10 <- ciCalibrate(estimate = logHR, se = se, siLevel = 10, method = "SI-normal",
priorMean = pm, priorSD = psd)
## compute instead with confidence interval as input
si10 <- ciCalibrate(ci = ci95, ciLevel = 0.95, siLevel = 10, method = "SI-normal",
priorMean = pm, priorSD = psd)
si10
#> Point Estimate [95% Confidence Interval]
#> -0.19 [-0.29,-0.092]
#>
#> Calibration Method
#> Normal prior for parameter under alternative
#> with mean m = 0 and standard deviation sd = 2
#>
#> k = 10 Support Interval
#> [-0.27,-0.11]
## plot Bayes factor function and support interval
plot(si10)
Pawel, S., Ly, A., and Wagenmakers, E.-J. (2023). Evidential Calibration of Confidence Intervals. The American Statistician. doi:10.1080/00031305.2023.2216239
- Wagenmakers, E.-J., Gronau, Q. F., Dablander, F., and Etz, A. (2020). The support interval. Erkenntnis. doi:10.1007/s10670-019-00209-z