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