.. _sphx_glr_auto_examples_swissmetro:

Biogeme examples for the Swissmetro data
****************************************

You find here several examples of models that can be estimated and simulated with Biogeme.





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    <div class="sphx-glr-thumbnails">

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    <div class="sphx-glr-thumbcontainer" tooltip="This script scans the current directory for Python files whose names start with plot_ and automatically generates corresponding SLURM .run submission scripts for each of them.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_generate_jed_run_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_generate_jed_run.py`

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      <div class="sphx-glr-thumbnail-title">Generate SLURM run scripts for Biogeme experiments.</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip=" Three alternatives:">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b01a_logit_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b01a_logit.py`

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      <div class="sphx-glr-thumbnail-title">1a. Estimation of a logit model</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Same model as b01logit, using LinearUtility, segmentations">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b01b_logit_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b01b_logit.py`

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      <div class="sphx-glr-thumbnail-title">1b. Illustration of additional features of Biogeme</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Same model as b01logit, estimated using the quick_estimate, that skips the calculation of the second orders statistics.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b01c_logit_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b01c_logit.py`

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      <div class="sphx-glr-thumbnail-title">1c. Illustration of the quick_estimate of Biogeme</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of simulation with a logit model">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b01d_logit_simul_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b01d_logit_simul.py`

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      <div class="sphx-glr-thumbnail-title">1d. Simulation of a logit model</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Estimation of a logit model with several algorithms.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b01e_logit_all_algos_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b01e_logit_all_algos.py`

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      <div class="sphx-glr-thumbnail-title">1e. Logit model with several algorithms</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip=" Example of a logit model with Weighted Exogenous Sample Maximum  Likelihood (WESML).">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b02_weight_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b02_weight.py`

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      <div class="sphx-glr-thumbnail-title">2. Estimation with weights: WESML</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Although normalizing the scale to 1 is a common practice in random utility models, it is sometimes preferable to normalize another parameter. For instance, normalizing the cost coefficient to -1 sets the units of the utility function as currency units (CHF here), and the estimated coefficients are easily interpreted as willingness to pay. In that case, the scale must be estimated.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b03_scale_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b03_scale.py`

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      <div class="sphx-glr-thumbnail-title">3. Moneymetric and heteroscedastic specification</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of the out-of-sample validation of a logit model.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b04_validation_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b04_validation.py`

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      <div class="sphx-glr-thumbnail-title">4. Out-of-sample validation</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of a normal mixture of logit models, using Monte-Carlo integration.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b05a_normal_mixture_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b05a_normal_mixture.py`

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      <div class="sphx-glr-thumbnail-title">5a. Mixture of logit models with Monte-Carlo integration</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of a normal mixture of logit models, using numerical integration.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b05b_normal_mixture_integral_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b05b_normal_mixture_integral.py`

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      <div class="sphx-glr-thumbnail-title">5b. Mixture of logit models with numerical integration</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Simulation of the mixture model, with estimation of the integration error.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b05c_normal_mixture_simul_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b05c_normal_mixture_simul.py`

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      <div class="sphx-glr-thumbnail-title">5c. Simulation of a mixture model</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of the use of different algorithms to estimate the model.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b05d_normal_mixture_all_algos_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b05d_normal_mixture_all_algos.py`

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      <div class="sphx-glr-thumbnail-title">Mixture of logit</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of a uniform mixture of logit models, using Monte-Carlo integration.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b06a_unif_mixture_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b06a_unif_mixture.py`

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      <div class="sphx-glr-thumbnail-title">6a. Mixture of logit models with uniform distribution</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of a uniform mixture of logit models, using Monte-Carlo  integration.  The mixing distribution is uniform.  The draws are from  the Modified Hypercube Latin Square.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b06b_unif_mixture_MHLS_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b06b_unif_mixture_MHLS.py`

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      <div class="sphx-glr-thumbnail-title">6b. Mixture of logit models with uniform MLHS draws</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="6c. Mixture of logit models with uniform distribution and numerical integration ===============================================================================">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b06c_unif_mixture_integral_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b06c_unif_mixture_integral.py`

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


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of a discrete mixture of logit (or latent class model).">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b07_discrete_mixture_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b07_discrete_mixture.py`

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      <div class="sphx-glr-thumbnail-title">7. Latent class model</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of a logit model, with a Box-Cox transform of variables.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b08_boxcox_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b08_boxcox.py`

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      <div class="sphx-glr-thumbnail-title">8. Box-Cox transforms</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of a nested logit model.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b09_nested_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b09_nested.py`

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      <div class="sphx-glr-thumbnail-title">9. Nested logit model</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of a nested logit model where the normalization is done at the  bottom level.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b10_nested_bottom_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b10_nested_bottom.py`

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      <div class="sphx-glr-thumbnail-title">10. Nested logit model normalized from bottom</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip=" Example of a cross-nested logit model with two nests:">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b11a_cnl_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b11a_cnl.py`

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      <div class="sphx-glr-thumbnail-title">11a. Cross-nested logit</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Illustration of the application of an estimated CNL model.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b11b_cnl_simul_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b11b_cnl_simul.py`

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      <div class="sphx-glr-thumbnail-title">11b. Simulation of a cross-nested logit model</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip=" Example of a cross-nested logit model with two nests:">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b11c_cnl_sparse_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b11c_cnl_sparse.py`

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      <div class="sphx-glr-thumbnail-title">11c. Cross-nested logit with a sparse structure</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of a mixture of logit models, using Monte-Carlo integration.  The datafile is organized as panel data.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b12_panel_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b12_panel.py`

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      <div class="sphx-glr-thumbnail-title">12. Mixture of logit with panel data</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Calculates each contribution to the log likelihood function using simulation. We also calculate the individual parameters.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b13_panel_simul_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b13_panel_simul.py`

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      <div class="sphx-glr-thumbnail-title">13. Simulation of panel model</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="The sample is said to be endogenous if the probability for an individual to be in the sample depends on the choice that has been made. In that case, the ESML estimator is not appropriate anymore, and corrections need to be made. See Bierlaire, Bolduc, McFadden (2008).">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b14_nested_endogenous_sampling_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b14_nested_endogenous_sampling.py`

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      <div class="sphx-glr-thumbnail-title">14. Nested logit with corrections for endogeneous sampling</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of a discrete mixture of logit models, also called latent  class model.  The datafile is organized as panel data.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b15a_panel_discrete_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b15a_panel_discrete.py`

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      <div class="sphx-glr-thumbnail-title">15a. Discrete mixture with panel data</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of a discrete mixture of logit models, also called latent  class model.  The datafile is organized as panel data.  Compared to  plot_b15a_panel_discrete, we integrate before the discrete  mixture to show that it is equivalent.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b15b_panel_discrete_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b15b_panel_discrete.py`

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      <div class="sphx-glr-thumbnail-title">15b. Discrete mixture with panel data</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of a discrete mixture of logit models, also called latent class model. The class membership model includes socio-economic variables. The datafile is organized as panel data.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b16_panel_discrete_socio_eco_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b16_panel_discrete_socio_eco.py`

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      <div class="sphx-glr-thumbnail-title">16. Discrete mixture with panel data</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of a mixture of logit models, using Monte-Carlo integration. The mixing distribution is distributed as a log normal.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b17a_lognormal_mixture_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b17a_lognormal_mixture.py`

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      <div class="sphx-glr-thumbnail-title">17a. Mixture with lognormal distribution</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of a mixture of logit models.  The mixing distribution is distributed as a log normal.  Compared to plot_b17a_lognormal_mixture, the integration is performed using numerical integration instead of Monte-Carlo approximation.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b17b_lognormal_mixture_integral_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b17b_lognormal_mixture_integral.py`

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      <div class="sphx-glr-thumbnail-title">17b. Mixture with lognormal distribution and numerical integration</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of an ordinal logit model.  This is just to illustrate the syntax, as the data are not ordered.  But the example assume, for the sake of it, that the alternatives are ordered as 1-&gt;2-&gt;3">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b18a_ordinal_logit_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b18a_ordinal_logit.py`

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      <div class="sphx-glr-thumbnail-title">18a. Ordinal logit model</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of an ordinal probit model.  This is just to illustrate the syntax, as the data are not ordered.  But the example assume, for the sake of it, that the alternatives are ordered as 1-&gt;2-&gt;3">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b18b_ordinal_probit_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b18b_ordinal_probit.py`

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      <div class="sphx-glr-thumbnail-title">18b. Ordinal probit model</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Calculation of the individual level parameters for the model defined in plot_b05normal_mixture.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b19_individual_level_parameters_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b19_individual_level_parameters.py`

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      <div class="sphx-glr-thumbnail-title">19. Calculation of individual level parameters</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of the estimation of several specifications of the model.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b20_multiple_models_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b20_multiple_models.py`

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      <div class="sphx-glr-thumbnail-title">20. Estimation of several models</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip=" Example of the estimation of several versions of the model using  assisted specification algorithm. The catalog of specifications is  defined in plot_b21b_multiple_models_spec. All specifications  are estimated. Have a look at plot_b22a_multiple_models for an  example where the number of specifications is too high to be  enumerated.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b21a_multiple_models_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b21a_multiple_models.py`

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      <div class="sphx-glr-thumbnail-title">21a. Assisted specification</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Specification of the catalogs used by the assisted specification algorithm. Note that this script does not perform any estimation. It is imported by other scripts: plot_b21a_multiple_models, plot_b21c_process_pareto.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b21b_multiple_models_spec_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b21b_multiple_models_spec.py`

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      <div class="sphx-glr-thumbnail-title">21b. Specification of a catalog of models</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="The assisted specification algorithm generates a file containing the pareto optimal specification. This script is designed to re-estimate the Pareto optimal models. The catalog of specifications is defined in plot_b21b_multiple_models_spec .">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b21c_process_pareto_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b21c_process_pareto.py`

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      <div class="sphx-glr-thumbnail-title">21c. Re-estimate the Pareto optimal models</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of the estimation of several versions of the model using assisted specification algorithm. The catalog of specifications is defined in plot_b22multiple_models_spec . Compared to plot_b21multiple_models, the number fo specifications exceeds the maximum limit, so a heuristic is applied.  See Bierlaire and Ortelli, 2023 for a detailed description of the use of the assisted specification algorithm.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b22a_multiple_models_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b22a_multiple_models.py`

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


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    <div class="sphx-glr-thumbcontainer" tooltip="Specification of the Catalog of expressions for the assisted specification algorithm. Note that this script does not perform any estimation. It is imported by other scripts: plot_b22multiple_models, plot_b22process_pareto.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b22b_multiple_models_spec_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b22b_multiple_models_spec.py`

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      <div class="sphx-glr-thumbnail-title">Specification of a catalog of models</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="The assisted specification algorithm generates a file containing the pareto optimal specification. This script is designed to re-estimate the Pareto optimal models. The catalog of specifications is defined in plot_b22multiple_models_spec .">

.. only:: html

  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b22c_process_pareto_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b22c_process_pareto.py`

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      <div class="sphx-glr-thumbnail-title">Re-estimate the Pareto optimal models</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of a binary logit model. Two alternatives: Train and Car. All observations such that the Swissmetro was chosen haven been removed from the sample.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b23a_binary_logit_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b23a_binary_logit.py`

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      <div class="sphx-glr-thumbnail-title">23a. Binary logit model</div>
    </div>


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    <div class="sphx-glr-thumbcontainer" tooltip="Example of a binary probit model. Two alternatives: Train and Car. All observations such that the Swissmetro was chosen haven been removed from the sample.">

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  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b23b_binary_probit_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b23b_binary_probit.py`

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      <div class="sphx-glr-thumbnail-title">23b. Binary probit model</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Example of a mixture of logit models, using quasi Monte-Carlo integration with Halton draws (base 5). The mixing distribution is normal.">

.. only:: html

  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b24_halton_mixture_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b24_halton_mixture.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">24. Mixture of logit with Halton draws</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip=" Example of a mixture of logit models, using Monte-Carlo integration.  The mixing distribution is specified by the user. Here, a triangular  distribution.">

.. only:: html

  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b25_triangular_mixture_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b25_triangular_mixture.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">25. Triangular mixture of logit</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip=" Example of a mixture of logit models, using Monte-Carlo integration.  The mixing distribution is user-defined (triangular, here).  The datafile is organized as panel data.">

.. only:: html

  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_plot_b26_triangular_panel_mixture_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_plot_b26_triangular_panel_mixture.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">26. Triangular mixture with panel data</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Data preparation for Swissmetro, and definition of the variables. The data is designed to estimate binary logit models. All observations such that Swissmetro was chosen are removed.">

.. only:: html

  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_swissmetro_binary_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_swissmetro_binary.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Data preparation for Swissmetro (binary choice)</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Data preparation and definition of the variables.">

.. only:: html

  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_swissmetro_data_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_swissmetro_data.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Data preparation for Swissmetro</div>
    </div>


.. raw:: html

    <div class="sphx-glr-thumbcontainer" tooltip="Data preparation for Swissmetro, and definition of the variables in panel configuration">

.. only:: html

  .. image:: /auto_examples/swissmetro/images/thumb/sphx_glr_swissmetro_panel_thumb.png
    :alt:

  :ref:`sphx_glr_auto_examples_swissmetro_swissmetro_panel.py`

.. raw:: html

      <div class="sphx-glr-thumbnail-title">Panel data preparation for Swissmetro</div>
    </div>


.. thumbnail-parent-div-close

.. raw:: html

    </div>


.. toctree::
   :hidden:

   /auto_examples/swissmetro/generate_jed_run
   /auto_examples/swissmetro/plot_b01a_logit
   /auto_examples/swissmetro/plot_b01b_logit
   /auto_examples/swissmetro/plot_b01c_logit
   /auto_examples/swissmetro/plot_b01d_logit_simul
   /auto_examples/swissmetro/plot_b01e_logit_all_algos
   /auto_examples/swissmetro/plot_b02_weight
   /auto_examples/swissmetro/plot_b03_scale
   /auto_examples/swissmetro/plot_b04_validation
   /auto_examples/swissmetro/plot_b05a_normal_mixture
   /auto_examples/swissmetro/plot_b05b_normal_mixture_integral
   /auto_examples/swissmetro/plot_b05c_normal_mixture_simul
   /auto_examples/swissmetro/plot_b05d_normal_mixture_all_algos
   /auto_examples/swissmetro/plot_b06a_unif_mixture
   /auto_examples/swissmetro/plot_b06b_unif_mixture_MHLS
   /auto_examples/swissmetro/plot_b06c_unif_mixture_integral
   /auto_examples/swissmetro/plot_b07_discrete_mixture
   /auto_examples/swissmetro/plot_b08_boxcox
   /auto_examples/swissmetro/plot_b09_nested
   /auto_examples/swissmetro/plot_b10_nested_bottom
   /auto_examples/swissmetro/plot_b11a_cnl
   /auto_examples/swissmetro/plot_b11b_cnl_simul
   /auto_examples/swissmetro/plot_b11c_cnl_sparse
   /auto_examples/swissmetro/plot_b12_panel
   /auto_examples/swissmetro/plot_b13_panel_simul
   /auto_examples/swissmetro/plot_b14_nested_endogenous_sampling
   /auto_examples/swissmetro/plot_b15a_panel_discrete
   /auto_examples/swissmetro/plot_b15b_panel_discrete
   /auto_examples/swissmetro/plot_b16_panel_discrete_socio_eco
   /auto_examples/swissmetro/plot_b17a_lognormal_mixture
   /auto_examples/swissmetro/plot_b17b_lognormal_mixture_integral
   /auto_examples/swissmetro/plot_b18a_ordinal_logit
   /auto_examples/swissmetro/plot_b18b_ordinal_probit
   /auto_examples/swissmetro/plot_b19_individual_level_parameters
   /auto_examples/swissmetro/plot_b20_multiple_models
   /auto_examples/swissmetro/plot_b21a_multiple_models
   /auto_examples/swissmetro/plot_b21b_multiple_models_spec
   /auto_examples/swissmetro/plot_b21c_process_pareto
   /auto_examples/swissmetro/plot_b22a_multiple_models
   /auto_examples/swissmetro/plot_b22b_multiple_models_spec
   /auto_examples/swissmetro/plot_b22c_process_pareto
   /auto_examples/swissmetro/plot_b23a_binary_logit
   /auto_examples/swissmetro/plot_b23b_binary_probit
   /auto_examples/swissmetro/plot_b24_halton_mixture
   /auto_examples/swissmetro/plot_b25_triangular_mixture
   /auto_examples/swissmetro/plot_b26_triangular_panel_mixture
   /auto_examples/swissmetro/swissmetro_binary
   /auto_examples/swissmetro/swissmetro_data
   /auto_examples/swissmetro/swissmetro_panel