Skip to main content

Implimentation of "A Robust Nonparametric Measure of Effect Size Based on an Analog of Cohens d...", R. Wilcox (2018). https://dx.doi.org/10.22237/jmasm/1551905677

Project description

shiftES

Implimentation of Wilcox shift effect size with command line interface, "A Robust Nonparametric Measure of Effect Size Based on an Analog of Cohen's d...", R. Wilcox (2018). https://dx.doi.org/10.22237/jmasm/1551905677

Run from command line, shift_effectsize.py -h for help.

Input files should be structured with a column of values per sample, e.g.

Samp1 Samp2 Samp3
4.3   7.5   3.4
4.5   8.3   2.4

Files can be comma/tab separated values, or a Excel .xlsx. File type will be automatically detected.

To test every sample against every other sample, with 95% confidence intervals, use:
shift_effectsize.py input_file_name.csv ALL ALL -o shiftes_results.csv --ci

Results will be saved in a table saved as shiftes_results.csv.

The given effect size is Ω, which ranges between -1 and +1 and is described in Wilcox's paper. As a guide: small 0.1; medium 0.3; large 0.4

To use within Python from shiftES import effectsize, effectsize_ci, difference_dist and see inline documentation.

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

shiftES-1.0.7.tar.gz (6.4 kB view hashes)

Uploaded Source

Built Distribution

shiftES-1.0.7-py3-none-any.whl (9.8 kB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page