A module for calculation of PCA with the NIPALS algorithm
A module for calculation of PCA and PLS with the NIPALS algorithm. Based on the R packages nipals and 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
pip install nipals
To run the all tests run:
Note, to combine the coverage data from all the tox environments run:
set PYTEST_ADDOPTS=--cov-append tox
- Fixed test that failed after last bug fix
- Fixed bug with selection of starting column for cross validation of PCA
- Fixed bug with cross validation of PCA
- Added cross validations
- Added calculation of distance to model with plots
- Added model overview plots
- Added R2X and R2Y to the PLS class
- Made plot color selectable also for scoreplots without classes
- Added a PLS class
- Improved plotting
- Fixed some problems with missing/infinite values
- First release on PyPI.
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|Filename, size & hash SHA256 hash help||File type||Python version||Upload date|
|nipals-0.4.3-py2.py3-none-any.whl (10.7 kB) Copy SHA256 hash SHA256||Wheel||py2.py3||Apr 24, 2018|
|nipals-0.4.3.tar.gz (193.7 kB) Copy SHA256 hash SHA256||Source||None||Apr 24, 2018|