dabestr is a package for Data Analysis using Bootstrap-Coupled ESTimation in R.
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.
Currently, this package is only available on Github. A CRAN version is forthcoming.
Your version of R must be 3.5.0 or higher.
# install.packages("dabestr") ## Does not work; awaiting CRAN acceptance. devtools::install_github("ACCLAB/dabestr")
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
Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang
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All contributions are welcome. Please fork this Github repo and open a pull request.