.. 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 ` to download the full example code. .. rst-class:: sphx-glr-example-title .. _sphx_glr_auto_examples_assisted_plot_b00logit.py: Base model ========== Logit model. Michel Bierlaire, EPFL Fri Jul 25 2025, 16:51:33 .. GENERATED FROM PYTHON SOURCE LINES 11-32 .. code-block:: Python from IPython.core.display_functions import display from biogeme.biogeme import BIOGEME from biogeme.data.swissmetro import ( CAR_AV_SP, CAR_CO_SCALED, CAR_TT_SCALED, CHOICE, SM_AV, SM_COST_SCALED, SM_TT_SCALED, TRAIN_AV_SP, TRAIN_COST_SCALED, TRAIN_TT_SCALED, read_data, ) from biogeme.expressions import Beta from biogeme.models import loglogit from biogeme.results_processing import get_pandas_estimated_parameters .. GENERATED FROM PYTHON SOURCE LINES 33-34 Parameters to be estimated. .. GENERATED FROM PYTHON SOURCE LINES 34-39 .. code-block:: Python 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:: Python v_train = asc_train + b_time * TRAIN_TT_SCALED + b_cost * TRAIN_COST_SCALED v_swissmetro = b_time * SM_TT_SCALED + b_cost * SM_COST_SCALED v_car = 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:: Python v = {1: v_train, 2: v_swissmetro, 3: v_car} .. GENERATED FROM PYTHON SOURCE LINES 50-51 Associate the availability conditions with the alternatives. .. GENERATED FROM PYTHON SOURCE LINES 51-53 .. code-block:: Python 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:: Python log_probability = loglogit(v, av, CHOICE) .. GENERATED FROM PYTHON SOURCE LINES 59-60 Read the data .. GENERATED FROM PYTHON SOURCE LINES 60-62 .. code-block:: Python database = read_data() .. GENERATED FROM PYTHON SOURCE LINES 63-64 Create the Biogeme object. .. GENERATED FROM PYTHON SOURCE LINES 64-69 .. code-block:: Python the_biogeme = BIOGEME( database, log_probability, generate_html=False, generate_yaml=False ) the_biogeme.model_name = 'b00logit' .. GENERATED FROM PYTHON SOURCE LINES 70-71 Calculate the null log likelihood for reporting. .. GENERATED FROM PYTHON SOURCE LINES 71-73 .. code-block:: Python the_biogeme.calculate_null_loglikelihood(av) .. rst-class:: sphx-glr-script-out .. code-block:: none -11093.627345287434 .. GENERATED FROM PYTHON SOURCE LINES 74-75 Estimate the parameters .. GENERATED FROM PYTHON SOURCE LINES 75-77 .. code-block:: Python results = the_biogeme.estimate() .. GENERATED FROM PYTHON SOURCE LINES 78-80 .. code-block:: Python print(results.short_summary()) .. rst-class:: sphx-glr-script-out .. code-block:: none Results for model b00logit Nbr of parameters: 4 Sample size: 10719 Excluded data: 9 Null log likelihood: -11093.63 Final log likelihood: -8670.163 Likelihood ratio test (null): 4846.928 Rho square (null): 0.218 Rho bar square (null): 0.218 Akaike Information Criterion: 17348.33 Bayesian Information Criterion: 17377.45 .. GENERATED FROM PYTHON SOURCE LINES 81-82 Get the results in a pandas table .. GENERATED FROM PYTHON SOURCE LINES 82-84 .. code-block:: Python pandas_results = get_pandas_estimated_parameters(estimation_results=results) display(pandas_results) .. rst-class:: sphx-glr-script-out .. code-block:: none Name Value Robust std err. Robust t-stat. Robust p-value 0 asc_train -0.652239 0.054394 -11.991022 0.000000 1 b_time -1.278941 0.065598 -19.496759 0.000000 2 b_cost -0.789790 0.050965 -15.496743 0.000000 3 asc_car 0.016228 0.037088 0.437556 0.661708 .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.905 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-jupyter :download:`Download Jupyter notebook: plot_b00logit.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_b00logit.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_b00logit.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_