:orphan:

.. _examples:

********
Examples
********

.. currentmodule:: eelbrain



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^^^^^^^^^^^^
Data objects
^^^^^^^^^^^^

Objects commonly used to represent data:
:class:`Var` and :class:`Factor` for univariate variables,
:class:`NDVar` for n-dimensional data, and
:class:`Dataset` to group multiple variables describing the same cases.



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    <div class="sphx-glr-thumbcontainer" tooltip="Data are represented with three primary data-objects:">

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  .. image:: /auto_examples/datasets/images/thumb/sphx_glr_intro_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_datasets_intro.py`

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


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    <div class="sphx-glr-thumbcontainer" tooltip="Load and prepare an example dataset:">

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  .. image:: /auto_examples/datasets/images/thumb/sphx_glr_dataset-basics_thumb.png
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  :ref:`sphx_glr_auto_examples_datasets_dataset-basics.py`

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      <div class="sphx-glr-thumbnail-title">Dataset basics</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Shows how to combine information from two datasets describing the same cases, but not necessarily in the same order.">

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  .. image:: /auto_examples/datasets/images/thumb/sphx_glr_align_thumb.png
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  :ref:`sphx_glr_auto_examples_datasets_align.py`

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      <div class="sphx-glr-thumbnail-title">Align datasets</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Shows how to initialize an NDVar with the structure of EEG data from (randomly generate) data arrays. The data is intended for illustrating EEG analysis techniques and meant to vaguely resemble data from an N400 experiment, but it is not meant to be a physiologically realistic simulation.">

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  .. image:: /auto_examples/datasets/images/thumb/sphx_glr_ndvar-creating_thumb.png
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  :ref:`sphx_glr_auto_examples_datasets_ndvar-creating.py`

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      <div class="sphx-glr-thumbnail-title">Creating NDVars</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Dataset provide a means to collect data for statistical analysis. A Dataset is similar to a dataframe in R or pandas, but can hold mass-univariate measurements in NDVar.">

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  .. image:: /auto_examples/datasets/images/thumb/sphx_glr_group-level-analysis_thumb.png
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  :ref:`sphx_glr_auto_examples_datasets_group-level-analysis.py`

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      <div class="sphx-glr-thumbnail-title">Group level analysis</div>
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^^^^^^^^^^^^^^^^^^^^^
Univariate Statistics
^^^^^^^^^^^^^^^^^^^^^



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    <div class="sphx-glr-thumbcontainer" tooltip="Illustrates how to inspect coding of regression models.">

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  .. image:: /auto_examples/statistics/images/thumb/sphx_glr_models_thumb.png
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  :ref:`sphx_glr_auto_examples_statistics_models.py`

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      <div class="sphx-glr-thumbnail-title">Model coding</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Repeated measures analysis of variance for univariate data.">

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  .. image:: /auto_examples/statistics/images/thumb/sphx_glr_RMANOVA_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_statistics_RMANOVA.py`

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      <div class="sphx-glr-thumbnail-title">Repeated measures ANOVA</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Analysis of covariance for univariate data.">

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  .. image:: /auto_examples/statistics/images/thumb/sphx_glr_ANCOVA_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_statistics_ANCOVA.py`

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


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

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      <div class="sphx-glr-thumbnail-title">Permutation statistics</div>
    </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>
    </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
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  :ref:`sphx_glr_auto_examples_mass-univariate-statistics_sensor-anova.py`

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      <div class="sphx-glr-thumbnail-title">ANOVA</div>
    </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
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  :ref:`sphx_glr_auto_examples_mass-univariate-statistics_compare-topographies.py`

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

Examples illustrating the use of the :mod:`plot` functionality.



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    <div class="sphx-glr-thumbcontainer" tooltip="Much of the functionality is shared with plot.Barplot.">

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  .. image:: /auto_examples/plots/images/thumb/sphx_glr_boxplot_thumb.png
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  :ref:`sphx_glr_auto_examples_plots_boxplot.py`

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


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    <div class="sphx-glr-thumbcontainer" tooltip="UTS-statistics (plot.UTSStat)">

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  .. image:: /auto_examples/plots/images/thumb/sphx_glr_utsstat_thumb.png
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  :ref:`sphx_glr_auto_examples_plots_utsstat.py`

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      <div class="sphx-glr-thumbnail-title">UTS-statistics (plot.UTSStat)</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="In general, colors for categorial variables are set as {cell: color} dictionaries. For factors, cells are strings, and for interactions they are tuples of strings. Colors can be set to any color understood by matplotlib.">

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  .. image:: /auto_examples/plots/images/thumb/sphx_glr_colors_thumb.png
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  :ref:`sphx_glr_auto_examples_plots_colors.py`

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


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    <div class="sphx-glr-thumbcontainer" tooltip="Eelbrain adds some colormaps to matplotlib&#x27;s standard colormaps. In general, the colormaps come with an *-a (&quot;alpha&quot;) version which uses transparency for low values instead of a solid color. For example, to use the polar map with transparency, use polar-a.">

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  .. image:: /auto_examples/plots/images/thumb/sphx_glr_colormaps_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_plots_colormaps.py`

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      <div class="sphx-glr-thumbnail-title">Eelbrain Colormaps</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="With the exception of plot.brain plots, Eelbrain&#x27;s plots are all based  on matplotlib. A lot of fine control over the plots can be achieved  through two means:">

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  .. image:: /auto_examples/plots/images/thumb/sphx_glr_customizing_thumb.png
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  :ref:`sphx_glr_auto_examples_plots_customizing.py`

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      <div class="sphx-glr-thumbnail-title">Customizing plots</div>
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^^^^^^^^^^^^^^^^^^^^^^^^^^^
Temporal Response Functions
^^^^^^^^^^^^^^^^^^^^^^^^^^^



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    <div class="sphx-glr-thumbcontainer" tooltip="A Temporal Response Function (TRF) is a linear stimulus-response model. The predicted response is a linear convolution of the stimulus with the TRF. A simple illustration of this is that every impulse in the stimulus is associated with a corresponding response, shaped like the TRF:">

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  .. image:: /auto_examples/temporal-response-functions/images/thumb/sphx_glr_trf_intro_thumb.png
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  :ref:`sphx_glr_auto_examples_temporal-response-functions_trf_intro.py`

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      <div class="sphx-glr-thumbnail-title">Introduction to Temporal Response Functions (TRFs)</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Estimate a TRF, starting with a -raw.fif EEG file and stimulus .wav files.">

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  .. image:: /auto_examples/temporal-response-functions/images/thumb/sphx_glr_alice-trf_thumb.png
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  :ref:`sphx_glr_auto_examples_temporal-response-functions_alice-trf.py`

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      <div class="sphx-glr-thumbnail-title">TRF for Alice EEG Dataset</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="EEG speech envelope TRF">

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  .. image:: /auto_examples/temporal-response-functions/images/thumb/sphx_glr_mtrf_thumb.png
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  :ref:`sphx_glr_auto_examples_temporal-response-functions_mtrf.py`

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      <div class="sphx-glr-thumbnail-title">EEG speech envelope TRF</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="The boosting algorithm can use two different forms of cross-validation: cross-validation as stopping criterion (always on), and cross-validation for model evaluation (optional). This requires paertitioning the data into different segments. The eelbrain.plot.preview_partitions function is for exploring the effect of different parameters on the way the data is split.">

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  .. image:: /auto_examples/temporal-response-functions/images/thumb/sphx_glr_partitions_thumb.png
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  :ref:`sphx_glr_auto_examples_temporal-response-functions_partitions.py`

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      <div class="sphx-glr-thumbnail-title">Data partitions for boosting</div>
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    <div class="sphx-glr-thumbcontainer" tooltip="The example uses simulated data meant to vaguely resemble data from an N400 experiment, but not intended as a physiologically realistic simulation.">

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  .. image:: /auto_examples/temporal-response-functions/images/thumb/sphx_glr_epoch_impulse_thumb.png
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  :ref:`sphx_glr_auto_examples_temporal-response-functions_epoch_impulse.py`

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      <div class="sphx-glr-thumbnail-title">Impulse predictors for epochs</div>
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.. toctree::
   :hidden:
   :includehidden:


   /auto_examples/datasets/index.rst
   /auto_examples/statistics/index.rst
   /auto_examples/mass-univariate-statistics/index.rst
   /auto_examples/plots/index.rst
   /auto_examples/temporal-response-functions/index.rst



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