.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/mass-univariate-statistics/sensor-anova.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_mass-univariate-statistics_sensor-anova.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_mass-univariate-statistics_sensor-anova.py:


.. _exa-mu-anova:

ANOVA
=====

.. currentmodule:: eelbrain

This example performs mass-univariate ANOVA using :class:`testnd.ANOVA`.
The example shows a 2 x 2 repeated measures ANOVA.
For specifying different ANOVA type models see the :class:`test.ANOVA` documentation.
The example uses simulated data meant to vaguely resemble data from an N400 experiment
(not intended as a physiologically realistic simulation).

.. contents:: Contents
   :local:

.. GENERATED FROM PYTHON SOURCE LINES 20-22

.. code-block:: Python

    from eelbrain import *








.. GENERATED FROM PYTHON SOURCE LINES 23-29

Simulated data
--------------
Use :func:`datasets.simulate_erp` to generate a dataset
simulating an N400 experiment, as in the :ref:`exa-cluster-based-mu` example.
Set ``short_long=True`` to simulate word length as a second variable,
resulting in a 2 x 2, *predictability* x *word length* design.

.. GENERATED FROM PYTHON SOURCE LINES 29-44

.. code-block:: Python

    s_datasets = []
    for subject in range(10):
        # generate data for one subject
        s_data = datasets.simulate_erp(seed=subject, short_long=True)
        # average across trials to get condition means
        s_data_agg = s_data.aggregate('predictability % length')
        # add the subject name as variable
        s_data_agg[:, 'subject'] = f'S{subject:02}'
        s_datasets.append(s_data_agg)

    data = combine(s_datasets)
    # Define subject as random factor (to treat it as random effect in the ANOVA)
    data['subject'].random = True
    data.head()






.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <figure><table border="1" frame="hsides" rules="none" cellpadding="2"><tr>
    <td style="text-align:left">#</td>
    <td style="text-align:left">n</td>
    <td style="text-align:left">cloze</td>
    <td style="text-align:left">predictability</td>
    <td style="text-align:left">n_chars</td>
    <td style="text-align:left">length</td>
    <td style="text-align:left">subject</td>
    </tr>
    <tr>
    <td style="text-align:left">0</td>
    <td style="text-align:left">20</td>
    <td style="text-align:left">0.87997</td>
    <td style="text-align:left">high</td>
    <td style="text-align:left">4.6</td>
    <td style="text-align:left">short</td>
    <td style="text-align:left">S00</td>
    </tr>
    <tr>
    <td style="text-align:left">1</td>
    <td style="text-align:left">20</td>
    <td style="text-align:left">0.88104</td>
    <td style="text-align:left">high</td>
    <td style="text-align:left">8.2</td>
    <td style="text-align:left">long</td>
    <td style="text-align:left">S00</td>
    </tr>
    <tr>
    <td style="text-align:left">2</td>
    <td style="text-align:left">20</td>
    <td style="text-align:left">0.17447</td>
    <td style="text-align:left">low</td>
    <td style="text-align:left">3.6</td>
    <td style="text-align:left">short</td>
    <td style="text-align:left">S00</td>
    </tr>
    <tr>
    <td style="text-align:left">3</td>
    <td style="text-align:left">20</td>
    <td style="text-align:left">0.17035</td>
    <td style="text-align:left">low</td>
    <td style="text-align:left">8.9</td>
    <td style="text-align:left">long</td>
    <td style="text-align:left">S00</td>
    </tr>
    <tr>
    <td style="text-align:left">4</td>
    <td style="text-align:left">20</td>
    <td style="text-align:left">0.88708</td>
    <td style="text-align:left">high</td>
    <td style="text-align:left">3.8</td>
    <td style="text-align:left">short</td>
    <td style="text-align:left">S01</td>
    </tr>
    <tr>
    <td style="text-align:left">5</td>
    <td style="text-align:left">20</td>
    <td style="text-align:left">0.90223</td>
    <td style="text-align:left">high</td>
    <td style="text-align:left">8.4</td>
    <td style="text-align:left">long</td>
    <td style="text-align:left">S01</td>
    </tr>
    <tr>
    <td style="text-align:left">6</td>
    <td style="text-align:left">20</td>
    <td style="text-align:left">0.11018</td>
    <td style="text-align:left">low</td>
    <td style="text-align:left">4.3</td>
    <td style="text-align:left">short</td>
    <td style="text-align:left">S01</td>
    </tr>
    <tr>
    <td style="text-align:left">7</td>
    <td style="text-align:left">20</td>
    <td style="text-align:left">0.16538</td>
    <td style="text-align:left">low</td>
    <td style="text-align:left">8.25</td>
    <td style="text-align:left">long</td>
    <td style="text-align:left">S01</td>
    </tr>
    <tr>
    <td style="text-align:left">8</td>
    <td style="text-align:left">20</td>
    <td style="text-align:left">0.90949</td>
    <td style="text-align:left">high</td>
    <td style="text-align:left">3.95</td>
    <td style="text-align:left">short</td>
    <td style="text-align:left">S02</td>
    </tr>
    <tr>
    <td style="text-align:left">9</td>
    <td style="text-align:left">20</td>
    <td style="text-align:left">0.89482</td>
    <td style="text-align:left">high</td>
    <td style="text-align:left">8.25</td>
    <td style="text-align:left">long</td>
    <td style="text-align:left">S02</td>
    </tr></table>
    <figcaption>NDVars: eeg</figcaption></figure>
    </div>
    <br />
    <br />

.. GENERATED FROM PYTHON SOURCE LINES 45-46

Re-reference the EEG data (i.e., subtract the mean of the two mastoid channels):

.. GENERATED FROM PYTHON SOURCE LINES 46-48

.. code-block:: Python

    data['eeg'] -= data['eeg'].mean(sensor=['M1', 'M2'])








.. GENERATED FROM PYTHON SOURCE LINES 49-53

Plot the data by condition to illustrate the effect. 
Note 2 effects in the simulation: 
an early ~130 ms peak determined by word `length`, simulating increased visual processing for longer words;
and a later "N400" `predictability` peak, simulated to be larger for more surprising (less predictable) words.

.. GENERATED FROM PYTHON SOURCE LINES 53-56

.. code-block:: Python


    p = plot.TopoButterfly('eeg', 'predictability % length', t=.400, data=data, axh=2, w=8, clip='circle')




.. image-sg:: /auto_examples/mass-univariate-statistics/images/sphx_glr_sensor-anova_001.png
   :alt: sensor anova
   :srcset: /auto_examples/mass-univariate-statistics/images/sphx_glr_sensor-anova_001.png
   :class: sphx-glr-single-img





.. GENERATED FROM PYTHON SOURCE LINES 57-63

Spatio-temporal test
--------------------
:class:`testnd.ANOVA` provides an interface for mass-univariate ANOVA.
The ANOVA function determines whether to perform a repeated measures or fixed effects ANOVA based on the model.
Here, ``'predictability * length * subject'`` is a repeated measures ANOVA because we defined ``data['subject']`` as random effect above.
Changing the model to ``'predictability * length'`` would perform a fixed effects ANOVA.

.. GENERATED FROM PYTHON SOURCE LINES 63-73

.. code-block:: Python

    result = testnd.ANOVA(
        'eeg', 
        'predictability * length * subject',
        data=data,
        pmin=0.05,  # Use uncorrected p = 0.05 as threshold for forming clusters
        tstart=0.050,  # Find clusters in the time window from 100 ...
        tstop=0.600,  # ... to 600 ms
        samples=1000,  # smaller number of permutations to speed up the example; use 10'000 when the exact p-value matters
    )





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    Permutation test:  90%|████████▉ | 897/1000 [00:17<00:02, 50.38 permutations/s]
    Permutation test:  90%|█████████ | 903/1000 [00:17<00:01, 50.21 permutations/s]
    Permutation test:  91%|█████████ | 910/1000 [00:17<00:01, 53.92 permutations/s]
    Permutation test:  92%|█████████▏| 916/1000 [00:17<00:01, 52.73 permutations/s]
    Permutation test:  92%|█████████▏| 922/1000 [00:17<00:01, 52.49 permutations/s]
    Permutation test:  93%|█████████▎| 928/1000 [00:17<00:01, 51.81 permutations/s]
    Permutation test:  93%|█████████▎| 934/1000 [00:18<00:01, 51.38 permutations/s]
    Permutation test:  94%|█████████▍| 940/1000 [00:18<00:01, 51.11 permutations/s]
    Permutation test:  95%|█████████▍| 946/1000 [00:18<00:01, 50.59 permutations/s]
    Permutation test:  95%|█████████▌| 952/1000 [00:18<00:00, 50.61 permutations/s]
    Permutation test:  96%|█████████▌| 958/1000 [00:18<00:00, 50.48 permutations/s]
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    Permutation test:  98%|█████████▊| 976/1000 [00:18<00:00, 50.59 permutations/s]
    Permutation test:  98%|█████████▊| 982/1000 [00:19<00:00, 50.47 permutations/s]
    Permutation test:  99%|█████████▉| 988/1000 [00:19<00:00, 50.31 permutations/s]
    Permutation test:  99%|█████████▉| 994/1000 [00:19<00:00, 50.18 permutations/s]
    Permutation test: 100%|██████████| 1000/1000 [00:19<00:00, 50.09 permutations/s]
    Permutation test: 100%|██████████| 1000/1000 [00:19<00:00, 51.56 permutations/s]




.. GENERATED FROM PYTHON SOURCE LINES 74-78

The default visualization shows *F*-values over time.
A yellow line indicates the F-value corresponding - the ``pmin=0.05`` parameter.
In the butterfly plots, line segments that are part of a significant cluster are shown in color.
In the topographic map, significant clusters are shown by outline.

.. GENERATED FROM PYTHON SOURCE LINES 78-82

.. code-block:: Python


    p = plot.TopoButterfly(result, t=.400, axh=2, w=8, clip='circle', vmax=100)
    _ = p.plot_colorbar()




.. rst-class:: sphx-glr-horizontal


    *

      .. image-sg:: /auto_examples/mass-univariate-statistics/images/sphx_glr_sensor-anova_002.png
         :alt: sensor anova
         :srcset: /auto_examples/mass-univariate-statistics/images/sphx_glr_sensor-anova_002.png
         :class: sphx-glr-multi-img

    *

      .. image-sg:: /auto_examples/mass-univariate-statistics/images/sphx_glr_sensor-anova_003.png
         :alt: sensor anova
         :srcset: /auto_examples/mass-univariate-statistics/images/sphx_glr_sensor-anova_003.png
         :class: sphx-glr-multi-img





.. GENERATED FROM PYTHON SOURCE LINES 83-84

The corresponding *F*-maps can be accessed on the `result` object:

.. GENERATED FROM PYTHON SOURCE LINES 84-87

.. code-block:: Python


    result.f





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    [<NDVar 'predictability': 140 time, 65 sensor>, <NDVar 'length': 140 time, 65 sensor>, <NDVar 'predictability x length': 140 time, 65 sensor>]



.. GENERATED FROM PYTHON SOURCE LINES 88-89

Corresponding cluster statistics:

.. GENERATED FROM PYTHON SOURCE LINES 89-91

.. code-block:: Python


    result.find_clusters(0.05)





.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <figure><table border="1" frame="hsides" rules="none" cellpadding="2"><tr>
    <td style="text-align:left">#</td>
    <td style="text-align:left">id</td>
    <td style="text-align:left">tstart</td>
    <td style="text-align:left">tstop</td>
    <td style="text-align:left">duration</td>
    <td style="text-align:left">n_sensors</td>
    <td style="text-align:left">v</td>
    <td style="text-align:left">p</td>
    <td style="text-align:left">sig</td>
    <td style="text-align:left">effect</td>
    </tr>
    <tr>
    <td style="text-align:left">0</td>
    <td style="text-align:left">3</td>
    <td style="text-align:left">0.315</td>
    <td style="text-align:left">0.455</td>
    <td style="text-align:left">0.14</td>
    <td style="text-align:left">61</td>
    <td style="text-align:left">11800</td>
    <td style="text-align:left">0</td>
    <td style="text-align:left">***</td>
    <td style="text-align:left">predictability</td>
    </tr>
    <tr>
    <td style="text-align:left">1</td>
    <td style="text-align:left">2</td>
    <td style="text-align:left">0.075</td>
    <td style="text-align:left">0.185</td>
    <td style="text-align:left">0.11</td>
    <td style="text-align:left">53</td>
    <td style="text-align:left">12739</td>
    <td style="text-align:left">0</td>
    <td style="text-align:left">***</td>
    <td style="text-align:left">length</td>
    </tr></table></figure>
    </div>
    <br />
    <br />


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

   **Total running time of the script:** (0 minutes 23.424 seconds)


.. _sphx_glr_download_auto_examples_mass-univariate-statistics_sensor-anova.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: sensor-anova.ipynb <sensor-anova.ipynb>`

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

      :download:`Download Python source code: sensor-anova.py <sensor-anova.py>`

    .. container:: sphx-glr-download sphx-glr-download-zip

      :download:`Download zipped: sensor-anova.zip <sensor-anova.zip>`


.. only:: html

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

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