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.
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.
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) %>% mean_diff() # Display the results in a user-friendly format. unpaired_mean_diff #> DABEST (Data Analysis with Bootstrap Estimation in R) v0.3.0 #> ============================================================ #> #> Good morning! #> The current time is 11:10 AM on Monday July 13, 2020. #> #> Dataset : iris #> X Variable : Species #> Y 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)
You will find more useful code snippets in this vignette.
How to cite
Moving beyond P values: Everyday data analysis with estimation plots
Joses Ho, Tayfun Tumkaya, Sameer Aryal, Hyungwon Choi, Adam Claridge-Chang
Nature Methods 2019, 1548-7105. 10.1038/s41592-019-0470-3
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