pCalibrate (version 0.2-1)

pCalibrate-package: pCalibrate

Description

Implements transformations of one- and two-sided p-values to minimum Bayes factors. The minimum Bayes factor is the smallest possible Bayes factor for the point null hypothesis against the alternative within the specified class of alternatives.

The function pCalibrate() provides minimum Bayes factors for two-sided p-values which consider the p-value as the data and are directly based on the distribution of the p-value under the null hypothesis and the alternative. For one- and two-sided p-values from z-tests, zCalibrate() implements minimum Bayes factors for different classes of alternatives. The function tCalibrate() provides the same functionality for one- and two-sided p-values from t-tests. The functions FCalibrate() and LRCalibrate() transform two-sided p-values from the F-test or likelihood ratio test, respectively, to minimum Bayes factors.

Details

Package: pCalibrate

Type: Package

Title: Bayesian Calibrations of p-Values

Version: 0.2-1

Date: 2020-03-19

Author: Manuela Ott [aut, cre], Leonhard Held [aut]

Maintainer: Manuela Ott <[e-mail]>

Depends: exact2x2, MCMCpack

License: GPL (>=2)

References

Held, L. and Ott, M. (2018). On p-values and Bayes factors. Annual Review of Statistics and Its Application, 5, 393--419.

Examples

# NOT RUN {
pCalibrate(p=c(0.05, 0.01, 0.001), type="exploratory")
zCalibrate(p=c(0.05, 0.01, 0.005), type="one.sided", 
           alternative="simple")
zCalibrate(p=c(0.05, 0.01, 0.005),  type="two.sided", 
           alternative="normal")
tCalibrate(p=c(0.05, 0.01, 0.005), n=c(10, 20, 50), type="two.sided", 
           alternative="normal")
FCalibrate(p=c(0.05, 0.01, 0.005), n=20, d=c(2, 5, 10), 
           alternative="chi.squared")
LRCalibrate(p=c(0.05, 0.01, 0.005), df=2, alternative="simple")
# }