.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/indicators/plot_b02estimation.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_indicators_plot_b02estimation.py>`
        to download the full example code.

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

.. _sphx_glr_auto_examples_indicators_plot_b02estimation.py:


Estimation and simulation of a nested logit model
=================================================

 We estimate a nested logit model and we perform simulation using the
 estimated model.

:author: Michel Bierlaire, EPFL
:date: Wed Apr 12 21:05:16 2023

.. GENERATED FROM PYTHON SOURCE LINES 13-21

.. code-block:: Python


    from IPython.core.display_functions import display

    from biogeme import models
    import biogeme.biogeme as bio
    from biogeme.data.optima import read_data
    from scenarios import scenario








.. GENERATED FROM PYTHON SOURCE LINES 22-24

Obtain the specification for the default scenario.
The definition of the scenarios is available in :ref:`scenarios`.

.. GENERATED FROM PYTHON SOURCE LINES 24-26

.. code-block:: Python

    V, nests, Choice, _ = scenario()








.. GENERATED FROM PYTHON SOURCE LINES 27-29

The choice model is a nested logit, with availability conditions
For estimation, we need the log of the probability.

.. GENERATED FROM PYTHON SOURCE LINES 29-31

.. code-block:: Python

    logprob = models.lognested(util=V, availability=None, nests=nests, choice=Choice)








.. GENERATED FROM PYTHON SOURCE LINES 32-33

Get the database

.. GENERATED FROM PYTHON SOURCE LINES 33-34

.. code-block:: Python

    database = read_data()







.. GENERATED FROM PYTHON SOURCE LINES 35-36

Create the Biogeme object for estimation.

.. GENERATED FROM PYTHON SOURCE LINES 36-39

.. code-block:: Python

    the_biogeme = bio.BIOGEME(database, logprob)
    the_biogeme.modelName = 'b02estimation'








.. GENERATED FROM PYTHON SOURCE LINES 40-41

Estimate the parameters. Perform bootstrapping.

.. GENERATED FROM PYTHON SOURCE LINES 41-44

.. code-block:: Python

    the_biogeme.bootstrap_samples = 100
    results = the_biogeme.estimate(run_bootstrap=True)





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

 .. code-block:: none


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.. GENERATED FROM PYTHON SOURCE LINES 45-46

Get the results in a pandas table

.. GENERATED FROM PYTHON SOURCE LINES 46-50

.. code-block:: Python

    pandas_results = results.get_estimated_parameters()
    display(pandas_results)






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

 .. code-block:: none

                             Value  Rob. Std err  Rob. t-test  Rob. p-value
    ASC_CAR               0.261239      0.100102     2.609731  9.061333e-03
    ASC_SM                0.059004      0.216071     0.273076  7.847944e-01
    BETA_COST            -0.715995      0.138326    -5.176125  2.265416e-07
    BETA_DIST_FEMALE     -0.830051      0.192377    -4.314718  1.598070e-05
    BETA_DIST_MALE       -0.685047      0.160237    -4.275212  1.909554e-05
    BETA_DIST_UNREPORTED -0.700981      0.194954    -3.595632  3.236051e-04
    BETA_TIME_FULLTIME   -1.596798      0.332759    -4.798660  1.597306e-06
    BETA_TIME_OTHER      -0.577377      0.296434    -1.947740  5.144604e-02
    mu_nocar              1.530883      0.305456     5.011799  5.392354e-07




.. GENERATED FROM PYTHON SOURCE LINES 51-52

Simulation

.. GENERATED FROM PYTHON SOURCE LINES 52-57

.. code-block:: Python

    simulated_choices = logprob.get_value_c(
        betas=results.get_beta_values(), database=database
    )
    display(simulated_choices)





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

 .. code-block:: none

    [-0.65046923 -1.4335904  -0.13265185 ... -0.37245933 -0.29545036
     -0.26736437]




.. GENERATED FROM PYTHON SOURCE LINES 58-65

.. code-block:: Python

    loglikelihood = logprob.get_value_c(
        betas=results.get_beta_values(),
        database=database,
        aggregation=True,
    )
    print(f'Final log likelihood:     {results.data.logLike}')
    print(f'Simulated log likelihood: {loglikelihood}')




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

 .. code-block:: none

    Final log likelihood:     -1298.4984028081865
    Simulated log likelihood: -1298.4984028081865





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

   **Total running time of the script:** (0 minutes 31.239 seconds)


.. _sphx_glr_download_auto_examples_indicators_plot_b02estimation.py:

.. only:: html

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

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

      :download:`Download Jupyter notebook: plot_b02estimation.ipynb <plot_b02estimation.ipynb>`

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

      :download:`Download Python source code: plot_b02estimation.py <plot_b02estimation.py>`

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

      :download:`Download zipped: plot_b02estimation.zip <plot_b02estimation.zip>`


.. only:: html

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

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