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., Nature Methods 2019, 1548-7105. . The free-to-view PDF is located at < https://rdcu.be/bHhJ4>.


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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")

0.2.2 by Joses W. Ho, 16 days ago


https://github.com/ACCLAB/dabestr


Report a bug at https://github.com/ACCLAB/dabestr/issues


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, ellipsis, ggplot2, forcats, ggforce, ggbeeswarm, rlang, simpleboot, stringr, tibble, tidyr

Depends on boot, magrittr

Suggests knitr, rmarkdown, tufte, testthat, vdiffr


See at CRAN