A module for calculation of PCA with the NIPALS algorithm
Project description
A module for calculation of PCA and PLS with the NIPALS algorithm. Based on the R packages nipals and pcaMethods <https://doi.org/10.18129/B9.bioc.pcaMethods> as well as the statistical appendixes to “Introduction to Multi- and Megavariate Data Analysis using Projection Methods (PCA & PLS)” by Eriksson et. al. Tested to give same results as the above packages and Simca, with some rounding errors.
Free software: MIT license
Installation
pip install nipals
Documentation
Development
To run the all tests run:
tox
Note, to combine the coverage data from all the tox environments run:
Windows |
set PYTEST_ADDOPTS=--cov-append tox |
---|---|
Other |
PYTEST_ADDOPTS=--cov-append tox |
Changelog
0.4.1 (2018-04-09)
Fixed bug with cross validation of PCA
0.4.0 (2018-04-09)
Added cross validations
Added calculation of distance to model with plots
Added model overview plots
0.3.0 (2018-04-05)
Added R2X and R2Y to the PLS class
Made plot color selectable also for scoreplots without classes
0.2.0 (2018-03-29)
Added a PLS class
Improved plotting
Fixed some problems with missing/infinite values
0.1.0 (2018-03-14)
First release on PyPI.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for nipals-0.4.1-py2.py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 13fc12db632fc47b844fed5583d540a68ca6200fb84372dc115b928bfde57a17 |
|
MD5 | 4a69d3a6a2253a9ad68c64ad5ef521d3 |
|
BLAKE2b-256 | 5091fdb39386a7b4ea110f33f41d9a3811b7fc0e48af32f43969fe0673f1f2ba |