.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/plot_massunivnormodols.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_auto_examples_plot_massunivnormodols.py>`
        to download the full example code.

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_auto_examples_plot_massunivnormodols.py:


=================================
Fitting Multiple Normative Models
=================================

This script demonstrates the process of fitting multiple normative models to data and visualizing the results.

We first generate synthetic data for multiple variables across a range of ages. Then, we fit a MassUnivNormodOLS model to the data, which is capable of handling multiple variables simultaneously. Finally, we visualize the results, including centiles and z-scores, for each variable.

.. GENERATED FROM PYTHON SOURCE LINES 13-15

Imports
-------

.. GENERATED FROM PYTHON SOURCE LINES 15-21

.. code-block:: Python

    import numpy as np
    import matplotlib.pyplot as plt
    from sknormod import MassUnivNormodOLS
    from sknormod.datasets import make_gaussian
    from sknormod.plotting import plot_scatter_with_lines








.. GENERATED FROM PYTHON SOURCE LINES 22-25

Generate synthetic data
-----------------------
We generate synthetic data for multiple variables across a range of ages.

.. GENERATED FROM PYTHON SOURCE LINES 25-35

.. code-block:: Python

    n_subj = 500  # Number of subjects
    n_cols = 9    # Number of variables

    age = np.linspace(0, 100, n_subj) 
    X = np.column_stack((age, age**2))  # Age and age squared as features
    # Generate synthetic Y data for each variable
    Y = np.column_stack([make_gaussian(n_subj, 
            interpolate_mu = np.random.normal(loc=100, scale=2, size=3))[1] 
                         for _ in range(n_cols)])  








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Fit and get z-scores and centiles
---------------------------------

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.. code-block:: Python


    # Fit MassUnivNormodOLS model
    mass_normod = MassUnivNormodOLS()
    mass_normod.fit(X, Y)

    # Calculate z-scores and centiles
    Z = mass_normod.transform_to_z(X, Y)  # Calculate z-scores for each variable
    # Calculate centiles for each variable
    mass_estimated_centiles = mass_normod.predict_distr_p(X, 
                                np.linspace(0.1, 0.9, 9), range(n_cols))  








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Plotting
--------
Visualize the results for each variable

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.. code-block:: Python

    fig, axes = plt.subplots(3, 3, figsize=(15, 15))  
    for i_col, ax in enumerate(axes.flat):
        plt.sca(ax)  
        plot_scatter_with_lines(age, 
                                Y[:, i_col], 
                                lines=mass_estimated_centiles[i_col], 
                                c=Z[:, i_col])  
    plt.tight_layout()  
    plt.show()  



.. image-sg:: /auto_examples/images/sphx_glr_plot_massunivnormodols_001.png
   :alt: plot massunivnormodols
   :srcset: /auto_examples/images/sphx_glr_plot_massunivnormodols_001.png
   :class: sphx-glr-single-img






.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.461 seconds)


.. _sphx_glr_download_auto_examples_plot_massunivnormodols.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_massunivnormodols.ipynb <plot_massunivnormodols.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_massunivnormodols.py <plot_massunivnormodols.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_