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://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D>.

dabestr

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.

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.

The dabestr package powers estimationstats.com, allowing everyone access to high-quality estimation plots.

Installation

# Install it from CRAN
install.packages("dabestr")

# Or the development version from GitHub:
# install.packages("devtools")
devtools::install_github(repo = "ACCLAB/dabestr", ref = "dev")

Usage

library(dabestr)

data("non_proportional_data")

dabest_obj.mean_diff <- load(
  data = non_proportional_data,
  x = Group,
  y = Measurement,
  idx = c("Control 1", "Test 1")
) %>%
  mean_diff()

dabest_plot(dabest_obj.mean_diff, TRUE)

Please refer to the official tutorial for more useful code snippets.

Citation

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

Paywalled publisher site; Free-to-view PDF

Contributing

Please report any bugs on the Github issue tracker.

All contributions are welcome; please read the Guidelines for contributing first.

We also have a Code of Conduct to foster an inclusive and productive space.

Acknowledgements

We would like to thank alpha testers from the Claridge-Chang lab: Sangyu Xu, Xianyuan Zhang, Farhan Mohammad, Jurga Mituzaitė, and Stanislav Ott.

DABEST in other languages

DABEST is also available in Python (DABEST-python) and Matlab (DABEST-Matlab).