.. _sphx_glr_auto_examples_mass-univariate-statistics:

.. _examples-mass-univariate-statistics:

^^^^^^^^^^^^^^^^^^^^^^^^^^
Mass-Univariate Statistics
^^^^^^^^^^^^^^^^^^^^^^^^^^

With :class:`NDVar` ("n-dimensional variables"), mass-univariate statistics
can be evaluated analogous to univariate statistics.



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    <div class="sphx-glr-thumbcontainer" tooltip="Eelbrain implents three methods for estimating null-distributions in permutation tests: max-statistic, cluster-mass and TFCE. This example illustrates these using a simple t-test. For simulating more complex datasets see other examples in this section.">

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  .. image:: /auto_examples/mass-univariate-statistics/images/thumb/sphx_glr_permutation-statistics_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_mass-univariate-statistics_permutation-statistics.py`

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      <div class="sphx-glr-thumbnail-title">Permutation statistics</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example show a cluster-based permutation test for a simple design (two conditions). The example uses simulated data meant to vaguely resemble data from an N400 experiment (not intended as a physiologically realistic simulation).">

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  .. image:: /auto_examples/mass-univariate-statistics/images/thumb/sphx_glr_sensor-ttest_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_mass-univariate-statistics_sensor-ttest.py`

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      <div class="sphx-glr-thumbnail-title">T-test</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example performs mass-univariate ANOVA using testnd.ANOVA. The example shows a 2 x 2 repeated measures ANOVA. For specifying different ANOVA type models see the test.ANOVA documentation. The example uses simulated data meant to vaguely resemble data from an N400 experiment (not intended as a physiologically realistic simulation).">

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  .. image:: /auto_examples/mass-univariate-statistics/images/thumb/sphx_glr_sensor-anova_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_mass-univariate-statistics_sensor-anova.py`

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      <div class="sphx-glr-thumbnail-title">ANOVA</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="Multiple regression designs apply to at least two scenarios.">

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  .. image:: /auto_examples/mass-univariate-statistics/images/thumb/sphx_glr_sensor-lm_thumb.png
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  :ref:`sphx_glr_auto_examples_mass-univariate-statistics_sensor-lm.py`

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      <div class="sphx-glr-thumbnail-title">Multiple regression</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="When trials are associated with continuous predictor variables, averaging is often a poor solution that loses part of the data. In such cases, a two-stage design can be employed that allows using the continuous predictor variable to test hypotheses at the group level. A two-stage analysis involves:">

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  .. image:: /auto_examples/mass-univariate-statistics/images/thumb/sphx_glr_sensor-two-stage_thumb.png
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  :ref:`sphx_glr_auto_examples_mass-univariate-statistics_sensor-two-stage.py`

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      <div class="sphx-glr-thumbnail-title">Two-stage test</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="This example shows how to compare EEG topographies, based on the method described by McCarthy &amp; Wood [1]_. We have also applied this method to source localized data, to distinguish true localization differences from apparent differences due to the sensitivity of souce estimate extent to amplitude [2]_.">

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  .. image:: /auto_examples/mass-univariate-statistics/images/thumb/sphx_glr_compare-topographies_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_mass-univariate-statistics_compare-topographies.py`

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      <div class="sphx-glr-thumbnail-title">Compare topographies</div>
    </div>


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    </div>


.. toctree::
   :hidden:

   /auto_examples/mass-univariate-statistics/permutation-statistics
   /auto_examples/mass-univariate-statistics/sensor-ttest
   /auto_examples/mass-univariate-statistics/sensor-anova
   /auto_examples/mass-univariate-statistics/sensor-lm
   /auto_examples/mass-univariate-statistics/sensor-two-stage
   /auto_examples/mass-univariate-statistics/compare-topographies