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PCA (principal component analysis)
PCR (principal component regression)
PLSR (partial least squares regression)
- PLSR1 for single variable responses
- PLSR2 for multivariate responses
matrix correlation coefficients RV, RV2 and SMI.

Unlike scikit-learn, which is an excellent python machine learning package focusing on classification, regression, clustering and predicition, hoggorm rather aims at understanding and interpretation of the variance in the data. hoggorm also contains tools for prediction. The complementary package hoggormplot can be used for visualization of results of models trained with hoggorm.

.. _scikit-learn: https://scikit-learn.org/stable/ .. _hoggormplot: https://github.com/olivertomic/hoggormPlot

Examples

.. |ColabCancer| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/olivertomic/hoggorm/blob/master/examples/PCA/PCA_on_cancer_data.ipynb :alt: Open in Colab

.. |BinderCancer| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/olivertomic/hoggorm/master?filepath=examples/PCA/PCA_on_cancer_data.ipynb :alt: Open in Binder

.. |BinderSensory| image:: https://mybinder.org/badge_logo.svg :target: https://mybinder.org/v2/gh/olivertomic/hoggorm/master?filepath=examples%2FPCR%2FPCR_on_sensory_and_fluorescence_data.ipynb :alt: Open in Binder

.. |ColabSensory| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/olivertomic/hoggorm/blob/master/examples/RV_%26_RV2/RV_and_RV2_on_sensory_and_fluorescence_data.ipynb :alt: Open In Colab

.. |ColabPCRCheese| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/olivertomic/hoggorm/blob/master/examples/PCR/PCR_on_sensory_and_fluorescence_data.ipynb :alt: Open In Colab

.. |ColabPLSR2Cheese| image:: https://colab.research.google.com/assets/colab-badge.svg :target: https://colab.research.google.com/github/olivertomic/hoggorm/blob/master/examples/PLSR/PLSR_on_sensory_and_fluorescence_data.ipynb :alt: Open In Colab

Below are links to some Jupyter notebooks that illustrate how to use hoggorm and hoggormplot with the methods mentioned above. All examples are also found in the examples_ folder.

Jupyter notebooks with examples of how to use hoggorm
- for PCA_ - PCA on cancer data_ on men in OECD countries |ColabCancer| |BinderCancer| - PCA on NIR spectroscopy data_ measured on gasoline - PCA on sensory data_ measured on cheese
- for PCR_ - PCR on sensory and fluorescence spectroscopy data_ measured on cheese |ColabPCRCheese|
- for PLSR1_ for univariate response (one response variable) - PLSR1 on NIR spectroscopy and octane data_ measured on gasoline
- for PLSR2_ for multivariate response (multiple response variables) - PLSR2 on sensory and fluorescence spectroscopy data_ measured on cheese |ColabPLSR2Cheese|
- for matrix correlation coefficients RV and RV2_ - RV and RV2 coefficient on sensory and fluorescence spectroscopy data_ measured on cheese |ColabSensory| |BinderSensory|
- for the SMI_ (similarity of matrix index) - SMI on sensory data and fluorescence data_ - SMI on pseudo-random numbers_

Requirements

Make sure that Python 3.5 or higher is installed. A convenient way to install Python and many useful packages for scientific computing is to use the Anaconda distribution_.

.. _Anaconda distribution: https://www.anaconda.com/download/

numpy >= 1.9

Installation

Using pip

.. image:: https://pepy.tech/badge/hoggorm :target: https://pepy.tech/project/hoggorm :alt: PyPI Downloads

.. image:: https://pepy.tech/badge/hoggorm/month :target: https://pepy.tech/project/hoggorm/month :alt: PyPI Downloads

.. image:: https://pepy.tech/badge/hoggorm/week :target: https://pepy.tech/project/hoggorm/week :alt: PyPI Downloads

Install hoggorm easily from the command line from the PyPI - the Python Packaging Index_.

.. _PyPI - the Python Packaging Index: https://pypi.python.org/pypi

.. code-block:: bash

pip install hoggorm

Using conda

.. image:: https://img.shields.io/conda/dn/conda-forge/hoggorm.svg :target: https://anaconda.org/conda-forge/hoggorm :alt: Conda Downloads

.. image:: https://img.shields.io/conda/vn/conda-forge/hoggorm.svg :target: https://anaconda.org/conda-forge/hoggorm :alt: Conda Version

You can install using the conda package manager by running

.. code-block:: bash

conda install -c conda-forge hoggorm

Documentation

.. image:: https://readthedocs.org/projects/hoggorm/badge/?version=latest

Documentation at Read the Docs_
Jupyter notebooks with examples_ of how to use Hoggorm together with the complementary plotting package hoggormplot_.

.. _Read the Docs: https://hoggorm.readthedocs.io/en/latest/ .. _examples: https://github.com/olivertomic/hoggorm/tree/master/examples .. _hoggormplot: https://github.com/olivertomic/hoggormPlot

Citing hoggorm

If you use hoggorm in a report or scientific publication, we would appreciate citations to the following paper:

.. image:: https://joss.theoj.org/papers/10.21105/joss.00980/status.svg :target: https://doi.org/10.21105/joss.00980

Tomic et al., (2019). hoggorm: a python library for explorative multivariate statistics. Journal of Open Source Software, 4(39), 980, https://doi.org/10.21105/joss.00980

Bibtex entry:

.. code-block:: bash

@article{hoggorm,
  title={hoggorm: a python library for explorative multivariate statistics},
  author={Tomic, Oliver and Graff, Thomas and Liland, Kristian Hovde and N{\ae}s, Tormod},
  journal={The Journal of Open Source Software},
  volume={4},
  number={39},
  year={2019},
  doi={10.21105/joss.00980},
  url={http://joss.theoj.org/papers/10.21105/joss.00980}
}

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