pvaluefunctions

Repository for the 'pvaluefunctions R package

https://github.com/dinfanger/pvaluefunctions

Repository

Repository for the 'pvaluefunctions R package

README.Rmd

---
title: pvaluefunctions
output:
  github_document:
    toc: true
    toc_depth: 3
---



```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE, 
  comment = "#>",
  fig.path = "man/figures/README-"
)
```

# *P*-value functions 

[![CRAN\_Status\_Badge](https://cranlogs.r-pkg.org:443/badges/grand-total/pvaluefunctions)](https://cran.r-project.org/package=pvaluefunctions)
[![downloads](https://cranlogs.r-pkg.org/badges/pvaluefunctions)](https://cran.r-project.org/package=pvaluefunctions)
[![total downloads](https://cranlogs.r-pkg.org/badges/grand-total/pvaluefunctions)](https://cranlogs.r-pkg.org/badges/grand-total/pvaluefunctions)
[![Rdoc](https://www.rdocumentation.org/packages/pvaluefunctions)](https://www.rdocumentation.org/packages/pvaluefunctions)

## Accompanying paper

We published an [accompanying paper](https://doi.org/10.1002/sim.8293) 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`](https://cran.r-project.org/package=pvaluefunctions). The package contains R functions to create graphics of *p*-value functions, confidence distributions, confidence densities, or the [Surprisal value (S-value)](http://www.umsl.edu/~fraundorfp/egsurpri.html) (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)`.

```{r source_github, message = FALSE, warning = FALSE, echo = FALSE, eval = FALSE}
library(devtools)

# Load main function
source_url("https://raw.githubusercontent.com/DInfanger/pvaluefunctions/master/R/confidence_distributions.R")

```

## Dependencies

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

* [ggplot2](https://cran.r-project.org/package=ggplot2)
* [scales](https://cran.r-project.org/package=scales)
* [zipfR](https://cran.r-project.org/package=zipfR)
* [pracma](https://cran.r-project.org/package=pracma)
* [gsl](https://cran.r-project.org/package=gsl)

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

## Examples

For more examples and code, see the [vignette](https://CRAN.R-project.org/package=pvaluefunctions/vignettes/pvaluefun.html).

```{r ttest_pval, echo = FALSE, message = FALSE, warning = FALSE, fig.width = 9, fig.height = 7, out.width = "70%", fig.align='left', dev = "png", dpi = 600}

# devtools::install_github("DInfanger/pvaluefunctions")

library(pvaluefunctions)

#-----------------------------------------------------------------------------
# Create p-value function
#-----------------------------------------------------------------------------

res <- conf_dist(
  estimate = c(-1.58)
  , df = c(17.77647)
  , tstat = c(-1.860813)
  , type = "ttest"
  , plot_type = "p_val"
  , n_values = 1e4L
  # , est_names = c("")
  , conf_level = c(0.95, 0.90, 0.80)
  , null_values = c(0)
  , trans = "identity"
  , alternative = "two_sided"
  , log_yaxis = FALSE
  , cut_logyaxis = 0.05
  , xlab = "Mean difference (group 1 - group 2)"
  , together = FALSE
  , plot_p_limit = 1 - 0.999
  , plot_counternull = TRUE
  , inverted = FALSE
  , title = expression(italic(P)~"-value function for a"~italic(t)~"-test")
  , plot = TRUE
)
```

```{r ttest_sval, echo = FALSE, message = FALSE, warning = FALSE, fig.width = 9, fig.height = 7, out.width = "70%", fig.align='left', dev = "png", dpi = 600}

#-----------------------------------------------------------------------------
# Create s-value function
#-----------------------------------------------------------------------------

res <- conf_dist(
  estimate = c(-1.58)
  , df = c(17.77647)
  , tstat = c(-1.860813)
  , type = "ttest"
  , plot_type = "s_val"
  , n_values = 1e4L
  # , est_names = c("")
  , conf_level = c(0.95, 0.90, 0.80)
  , null_values = c(0)
  , trans = "identity"
  , alternative = "two_sided"
  , log_yaxis = FALSE
  , cut_logyaxis = 0.05
  , xlab = "Mean difference (group 1 - group 2)"
  , together = FALSE
  , col = "#C977A2"
  , plot_p_limit = 1 - 0.999
  , plot_counternull = TRUE
  , inverted = FALSE
  , title = expression(italic(S)~"-value function for a"~italic(t)~"-test")
)
```


```{r benderfig1, echo = FALSE, message = FALSE, warning = FALSE, fig.width = 9, fig.height = 7, out.width = "70%", fig.align='left', dev = "png", dpi = 600}
rse_fun <- function(x){
  100*(1 - exp(x))
}

rse_fun_inv <- function(x){
  log(1 - (x/100))
}

res <- conf_dist(
  estimate = c(log(0.72))
  , stderr = (0.187618)
  , type = "coxreg"
  , plot_type = "p_val"
  , n_values = 1e4L
  , est_names = c("RSE")
  , conf_level = c(0.95, 0.8, 0.5)
  , null_values = rse_fun_inv(c(0))
  , trans = "rse_fun"
  , alternative = "two_sided"
  , log_yaxis = FALSE
  , cut_logyaxis = 0.05
  , xlab = "Relative survival effect (1 - HR%)"
  , xlim = rse_fun_inv(c(-30, 60))
  , together = FALSE
  , plot_p_limit = 1 - 0.999
  , plot_counternull = TRUE
  , inverted = TRUE
  , title = "Figure 1 in Bender et al. (2005)"
)
```

## 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.

Rafi Z, Greenland S. (2020): Semantic and cognitive tools to aid statistical science: replace confidence and significance by compatibility and surprise. *BMC Med Res Methodol.* 20, 244. doi: 10.1186/s12874-020-01105-9.

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](https://dsbg.unibas.ch/de/personen/denis-infanger/)

## Session info

```{r session_info, include=TRUE, echo=FALSE}
sessionInfo()
```

## License

[![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0)

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