pvaluefunctions

P-value functions

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Accompanying paper

We published an accompanying paper to illustrate the use of p-value functions:

Infanger D, Schmidt-Trucksäss A. (2019): P value functions: An underused method to present research results and to promote quantitative reasoning. Statistics in Medicine. 38: 4189-4197. doi: 10.1002/sim.8293.

Recreation of the figures in the paper

The code and instructions to reproduce all graphics in our paper can be found in the following GitHub repository: https://github.com/DInfanger/pvalue_functions

Overview

This is the repository for the R-package pvaluefunctions. The package contains R functions to create graphics of p-value functions, confidence distributions, confidence densities, or the Surprisal value (S-value) (Greenland 2019).

Installation

You can install the package directly from CRAN by typing install.packages("pvaluefunctions"). After installation, load it in R using library(pvaluefunctions).

Dependencies

The function depends on the following R packages, which need to be installed beforehand:

Use the command install.packages(c("ggplot2", "scales", "zipfR", "pracma")) in R to install those packages.

Examples

For more examples and code, see the vignette.

References

Bender R, Berg G, Zeeb H. (2005): Tutorial: using confidence curves in medical research. Biom J. 47(2): 237-47.

Berrar D (2017): Confidence Curves: an alternative to null hypothesis significance testing for the comparison of classifiers. Mach Learn 106:911-949.

Fraser D. A. S. (2019): The p-value function and statistical inference. Am Stat, 73:sup1, 135-147.

Greenland S (2019): Valid P-Values Behave Exactly as They Should: Some Misleading Criticisms of P-Values and Their Resolution with S-Values. Am Stat, 73sup1, 106-114.

Infanger D, Schmidt-Trucksäss A. (2019): P value functions: An underused method to present research results and to promote quantitative reasoning. Stat Med, 38, 4189-4197. doi: 10.1002/sim.8293.

Poole C. (1987a): Beyond the confidence interval. Am J Public Health. 77(2): 195-9.

Poole C. (1987b) Confidence intervals exclude nothing. Am J Public Health. 77(4): 492-3.

Rosenthal R, Rubin DB. (1994): The counternull value of an effect size: A new statistic. Psychol Sci. 5(6): 329-34.

Schweder T, Hjort NL. (2016): Confidence, likelihood, probability: statistical inference with confidence distributions. New York, NY: Cambridge University Press.

Xie M, Singh K, Strawderman WE. (2011): Confidence Distributions and a Unifying Framework for Meta-Analysis. J Am Stat Assoc 106(493): 320-33. doi: 10.1198/jasa.2011.tm09803.

Xie Mg, Singh K. (2013): Confidence distribution, the frequentist distribution estimator of a parameter: A review. Internat Statist Rev. 81(1): 3-39.

Contact

Denis Infanger

Session info

#> R version 4.0.2 (2020-06-22)
#> Platform: x86_64-w64-mingw32/x64 (64-bit)
#> Running under: Windows 10 x64 (build 19041)
#> 
#> Matrix products: default
#> 
#> locale:
#> [1] LC_COLLATE=German_Switzerland.1252  LC_CTYPE=German_Switzerland.1252   
#> [3] LC_MONETARY=German_Switzerland.1252 LC_NUMERIC=C                       
#> [5] LC_TIME=German_Switzerland.1252    
#> 
#> attached base packages:
#> [1] stats     graphics  grDevices utils     datasets  methods   base     
#> 
#> other attached packages:
#> [1] pvaluefunctions_1.6.0
#> 
#> loaded via a namespace (and not attached):
#>  [1] knitr_1.29       magrittr_1.5     tidyselect_1.1.0 munsell_0.5.0   
#>  [5] colorspace_1.4-1 R6_2.4.1         rlang_0.4.6      dplyr_1.0.0     
#>  [9] stringr_1.4.0    tools_4.0.2      grid_4.0.2       gtable_0.3.0    
#> [13] xfun_0.15        htmltools_0.5.0  ellipsis_0.3.1   yaml_2.2.1      
#> [17] digest_0.6.25    tibble_3.0.1     lifecycle_0.2.0  crayon_1.3.4    
#> [21] farver_2.0.3     purrr_0.3.4      ggplot2_3.3.2    vctrs_0.3.1     
#> [25] glue_1.4.1       evaluate_0.14    rmarkdown_2.3    pracma_2.2.9    
#> [29] stringi_1.4.6    compiler_4.0.2   pillar_1.4.4     generics_0.0.2  
#> [33] scales_1.1.1     pkgconfig_2.0.3

License

License: GPL v3