dabestr is a package for 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.
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It presents all datapoints as a swarmplot, which orders each point to display the underlying distribution.
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It presents the effect size as a bootstrap 95% confidence interval on a separate but aligned axes.
Your version of R must be 3.5.0 or higher.
install.packages("dabestr")
# To install the latest development version on Github,
# use the line below.
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)
Moving beyond P values: Everyday data analysis with estimation plots
Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang
dabestr is also available in Python and Matlab.
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!
All contributions are welcome. Please fork this Github repo and open a pull request.