Data Analysis using Bootstrap-Coupled Estimation

Data Analysis using Bootstrap-Coupled ESTimation. Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by P values. An estimation plot has two key features: 1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution. 2. It presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes. Estimation plots are introduced in Ho et al (2018) .


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dabestr is a package for Data Analysis using Bootstrap-Coupled ESTimation.

About

Estimation statistics is a simple framework that avoids the pitfalls of significance testing. It uses familiar statistical concepts: means, mean differences, and error bars. More importantly, it focuses on the effect size of one's experiment/intervention, as opposed to a false dichotomy engendered by P values.

An estimation plot has two key features.

  1. It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution.

  2. It presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes.

Installation

Your version of R must be 3.5.0 or higher.

install.packages("dabestr")
# use the line below.
devtools::install_github("ACCLAB/dabestr")

Usage

library(dabestr)
 
# Performing unpaired (two independent groups) analysis.
unpaired_mean_diff <- dabest(iris, Species, Petal.Width,
                             idx = c("setosa", "versicolor", "virginica"),
                             paired = FALSE)
 
# Display the results in a user-friendly format.
unpaired_mean_diff
#> DABEST (Data Analysis with Bootstrap Estimation) v0.1.0
#> =======================================================
#> 
#> Variable: Petal.Width 
#> 
#> Unpaired mean difference of versicolor (n=50) minus setosa (n=50)
#>  1.08 [95CI  1.01; 1.14]
#> 
#> Unpaired mean difference of virginica (n=50) minus setosa (n=50)
#>  1.78 [95CI  1.69; 1.85]
#> 
#> 
#> 5000 bootstrap resamples.
#> All confidence intervals are bias-corrected and accelerated.
 
# Produce a Cumming estimation plot.
plot(unpaired_mean_diff)

How to Cite

Moving beyond P values: Everyday data analysis with estimation plots

Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang

dabest In Other Languages

dabestr is also available in Python and Matlab.

Bugs

Please open a new issue. Include a reproducible example (aka reprex) so anyone can copy-paste your code and move quickly towards helping you out!

Contributing

All contributions are welcome. Please fork this Github repo and open a pull request.

News

Reference manual

It appears you don't have a PDF plugin for this browser. You can click here to download the reference manual.

install.packages("dabestr")

0.2.0 by Joses W. Ho, 2 months ago


Browse source code at https://github.com/cran/dabestr


Authors: Joses W. Ho [cre, aut] , Tayfun Tumkaya [aut]


Documentation:   PDF Manual  


file LICENSE license


Imports cowplot, dplyr, ggplot2, forcats, ggforce, ggbeeswarm, rlang, simpleboot, stringr, tibble, tidyr

Depends on boot, magrittr

Suggests knitr, rmarkdown, tufte, testthat, vdiffr


See at CRAN