.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/assisted/plot_b00logit.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_assisted_plot_b00logit.py>`
        to download the full example code

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

.. _sphx_glr_auto_examples_assisted_plot_b00logit.py:


Base model
==========

Logit model.

:author: Michel Bierlaire, EPFL
:date: Thu Jul 13 16:18:10 2023

.. GENERATED FROM PYTHON SOURCE LINES 12-16

.. code-block:: default

    import biogeme.biogeme as bio
    from biogeme import models
    from biogeme.expressions import Beta








.. GENERATED FROM PYTHON SOURCE LINES 17-18

See :ref:`swissmetro_data`

.. GENERATED FROM PYTHON SOURCE LINES 18-32

.. code-block:: default

    from swissmetro_data import (
        database,
        CHOICE,
        SM_AV,
        CAR_AV_SP,
        TRAIN_AV_SP,
        TRAIN_TT_SCALED,
        TRAIN_COST_SCALED,
        SM_TT_SCALED,
        SM_COST_SCALED,
        CAR_TT_SCALED,
        CAR_CO_SCALED,
    )








.. GENERATED FROM PYTHON SOURCE LINES 33-34

Parameters to be estimated.

.. GENERATED FROM PYTHON SOURCE LINES 34-39

.. code-block:: default

    ASC_CAR = Beta('ASC_CAR', 0, None, None, 0)
    ASC_TRAIN = Beta('ASC_TRAIN', 0, None, None, 0)
    B_TIME = Beta('B_TIME', 0, None, None, 0)
    B_COST = Beta('B_COST', 0, None, None, 0)








.. GENERATED FROM PYTHON SOURCE LINES 40-41

Definition of the utility functions.

.. GENERATED FROM PYTHON SOURCE LINES 41-45

.. code-block:: default

    V1 = ASC_TRAIN + B_TIME * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED
    V2 = B_TIME * SM_TT_SCALED + B_COST * SM_COST_SCALED
    V3 = ASC_CAR + B_TIME * CAR_TT_SCALED + B_COST * CAR_CO_SCALED








.. GENERATED FROM PYTHON SOURCE LINES 46-47

Associate utility functions with the numbering of alternatives.

.. GENERATED FROM PYTHON SOURCE LINES 47-49

.. code-block:: default

    V = {1: V1, 2: V2, 3: V3}








.. GENERATED FROM PYTHON SOURCE LINES 50-51

Associate the availability conditions with the alternatives.

.. GENERATED FROM PYTHON SOURCE LINES 51-53

.. code-block:: default

    av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP}








.. GENERATED FROM PYTHON SOURCE LINES 54-56

Definition of the model. This is the contribution of each
observation to the log likelihood function.

.. GENERATED FROM PYTHON SOURCE LINES 56-58

.. code-block:: default

    logprob = models.loglogit(V, av, CHOICE)








.. GENERATED FROM PYTHON SOURCE LINES 59-60

Create the Biogeme object.

.. GENERATED FROM PYTHON SOURCE LINES 60-65

.. code-block:: default

    the_biogeme = bio.BIOGEME(database, logprob)
    the_biogeme.modelName = 'b00logit'
    the_biogeme.generate_html = False
    the_biogeme.generate_pickle = False





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

 .. code-block:: none

    File biogeme.toml has been created




.. GENERATED FROM PYTHON SOURCE LINES 66-67

Calculate the null log likelihood for reporting.

.. GENERATED FROM PYTHON SOURCE LINES 67-69

.. code-block:: default

    the_biogeme.calculateNullLoglikelihood(av)





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

 .. code-block:: none


    -6964.662979191462



.. GENERATED FROM PYTHON SOURCE LINES 70-71

Estimate the parameters

.. GENERATED FROM PYTHON SOURCE LINES 71-73

.. code-block:: default

    results = the_biogeme.estimate()








.. GENERATED FROM PYTHON SOURCE LINES 74-76

.. code-block:: default

    print(results.short_summary())





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

 .. code-block:: none

    Results for model b00logit
    Nbr of parameters:              4
    Sample size:                    6768
    Excluded data:                  3960
    Null log likelihood:            -6964.663
    Final log likelihood:           -5331.252
    Likelihood ratio test (null):           3266.822
    Rho square (null):                      0.235
    Rho bar square (null):                  0.234
    Akaike Information Criterion:   10670.5
    Bayesian Information Criterion: 10697.78





.. GENERATED FROM PYTHON SOURCE LINES 77-78

Get the results in a pandas table

.. GENERATED FROM PYTHON SOURCE LINES 78-80

.. code-block:: default

    pandas_results = results.getEstimatedParameters()
    pandas_results





.. raw:: html

    <div class="output_subarea output_html rendered_html output_result">
    <div>
    <style scoped>
        .dataframe tbody tr th:only-of-type {
            vertical-align: middle;
        }

        .dataframe tbody tr th {
            vertical-align: top;
        }

        .dataframe thead th {
            text-align: right;
        }
    </style>
    <table border="1" class="dataframe">
      <thead>
        <tr style="text-align: right;">
          <th></th>
          <th>Value</th>
          <th>Rob. Std err</th>
          <th>Rob. t-test</th>
          <th>Rob. p-value</th>
        </tr>
      </thead>
      <tbody>
        <tr>
          <th>ASC_CAR</th>
          <td>-0.154633</td>
          <td>0.058163</td>
          <td>-2.658590</td>
          <td>0.007847</td>
        </tr>
        <tr>
          <th>ASC_TRAIN</th>
          <td>-0.701187</td>
          <td>0.082562</td>
          <td>-8.492857</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>B_COST</th>
          <td>-1.083790</td>
          <td>0.068225</td>
          <td>-15.885521</td>
          <td>0.000000</td>
        </tr>
        <tr>
          <th>B_TIME</th>
          <td>-1.277859</td>
          <td>0.104254</td>
          <td>-12.257120</td>
          <td>0.000000</td>
        </tr>
      </tbody>
    </table>
    </div>
    </div>
    <br />
    <br />


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

   **Total running time of the script:** (0 minutes 0.312 seconds)


.. _sphx_glr_download_auto_examples_assisted_plot_b00logit.py:

.. only:: html

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




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

      :download:`Download Python source code: plot_b00logit.py <plot_b00logit.py>`

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

      :download:`Download Jupyter notebook: plot_b00logit.ipynb <plot_b00logit.ipynb>`


.. only:: html

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

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