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dabest 2024.3.29

DABEST-Python

Recent Version Update
On 22 March 2024, we officially released DABEST Version Ondeh
(v2024.03.29). This new version provides several new features and
includes performance improvements.

New Paired Proportion Plot: This feature builds upon the
existing proportional analysis capabilities by introducing advanced
aesthetics and clearer visualization of changes in proportions
between different groups, inspired by the informative nature of
Sankey Diagrams. It’s particularly useful for studies that require
detailed examination of how proportions shift in paired
observations.

Customizable Swarm Plot: Enhancements allow for tailored swarm
plot aesthetics, notably the adjustment of swarm sides to produce
asymmetric swarm plots. This customization enhances data
representation, making visual distinctions more pronounced and
interpretations clearer.

Standardized Delta-delta Effect Size: We added a new metric akin
to a Hedges’ g for delta-delta effect size, which allows comparisons
between delta-delta effects generated from metrics with different
units.

Miscellaneous Improvements: This version also encompasses a
broad range of miscellaneous enhancements, including bug fixes,
Bootstrapping speed improvements, new templates for raising issues,
and updated unit tests. These improvements are designed to
streamline the user experience, increase the software’s stability,
and expand its versatility. By addressing user feedback and
identified issues, DABEST continues to refine its functionality and
reliability.

Contents

About
Installation
Usage
How to cite
Bugs
Contributing
Acknowledgements
Testing
DABEST in other languages

About
DABEST is a package for Data Analysis using
Bootstrap-Coupled ESTimation.
Estimation
statistics are a
simple framework that avoids the
pitfalls of significance
testing. It employs familiar statistical concepts such as means, mean
differences, and error bars. More importantly, it focuses on the effect
size of one’s experiment or intervention, rather than succumbing to a
false dichotomy engendered by P values.
An estimation plot comprises two key features.

It presents all data points as a swarm plot, ordering each point to
display the underlying distribution.

It illustrates the effect size as a bootstrap 95% confidence
interval on a separate but aligned axis.

DABEST powers estimationstats.com,
allowing everyone access to high-quality estimation plots.
Installation
This package is tested on Python 3.8 and onwards. It is highly
recommended to download the Anaconda
distribution of Python in order to
obtain the dependencies easily.
You can install this package via pip.
To install, at the command line run
pip install dabest

You can also
clone this repo
locally.
Then, navigate to the cloned repo in the command line and run
pip install .

Usage
import pandas as pd
import dabest

# Load the iris dataset. This step requires internet access.
iris = pd.read_csv("https://github.com/mwaskom/seaborn-data/raw/master/iris.csv")

# Load the above data into `dabest`.
iris_dabest = dabest.load(data=iris, x="species", y="petal_width",
idx=("setosa", "versicolor", "virginica"))

# Produce a Cumming estimation plot.
iris_dabest.mean_diff.plot();

Please refer to the official
tutorial for more useful code
snippets.
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
Paywalled publisher
site; Free-to-view
PDF
Bugs
Please report any bugs on the issue
page.
Contributing
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.
A wish list for new features
If you have any specific comments and ideas for new features that you
would like to share with us, please read the Guidelines for
contributing, create a new issue using Feature request
template or create a new post in our Google
Group.
Acknowledgements
We would like to thank alpha testers from the Claridge-Chang
lab: Sangyu
Xu, Xianyuan
Zhang, Farhan
Mohammad, Jurga Mituzaitė, and
Stanislav Ott.
Testing
To test DABEST, you need to install
pytest and
nbdev.

Run pytest in the root directory of the source distribution. This
runs the test suite in the folder dabest/tests/mpl_image_tests.
Run nbdev_test in the root directory of the source distribution.
This runs the value assertion tests in the folder dabest/tests

The test suite ensures that the bootstrapping functions and the plotting
functions perform as expected.
For detailed information, please refer to the test
folder
DABEST in other languages
DABEST is also available in R
(dabestr) and Matlab
(DABEST-Matlab).