.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/swissmetro/plot_b15panel_discrete.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_swissmetro_plot_b15panel_discrete.py: .. _plot_b15panel_discrete: Discrete mixture with panel data ================================ Example of a discrete mixture of logit models, also called latent class model. The datafile is organized as panel data. :author: Michel Bierlaire, EPFL :date: Mon Apr 10 11:53:06 2023 .. GENERATED FROM PYTHON SOURCE LINES 14-26 .. code-block:: default import biogeme.biogeme_logging as blog import biogeme.biogeme as bio from biogeme import models from biogeme.expressions import ( Beta, bioDraws, PanelLikelihoodTrajectory, MonteCarlo, log, ) .. GENERATED FROM PYTHON SOURCE LINES 27-28 See the data processing script: :ref:`swissmetro_panel`. .. GENERATED FROM PYTHON SOURCE LINES 28-45 .. code-block:: default from swissmetro_panel 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, ) logger = blog.get_screen_logger(level=blog.INFO) logger.info('Example b15panel_discrete.py') .. rst-class:: sphx-glr-script-out .. code-block:: none Example b15panel_discrete.py .. GENERATED FROM PYTHON SOURCE LINES 46-47 Parameters to be estimated. One version for each latent class. .. GENERATED FROM PYTHON SOURCE LINES 47-50 .. code-block:: default NUMBER_OF_CLASSES = 2 B_COST = [Beta(f'B_COST_class{i}', 0, None, None, 0) for i in range(NUMBER_OF_CLASSES)] .. GENERATED FROM PYTHON SOURCE LINES 51-53 Define a random parameter, normally distributed across individuals, designed to be used for Monte-Carlo simulation .. GENERATED FROM PYTHON SOURCE LINES 53-55 .. code-block:: default B_TIME = [Beta(f'B_TIME_class{i}', 0, None, None, 0) for i in range(NUMBER_OF_CLASSES)] .. GENERATED FROM PYTHON SOURCE LINES 56-57 It is advised not to use 0 as starting value for the following parameter. .. GENERATED FROM PYTHON SOURCE LINES 57-65 .. code-block:: default B_TIME_S = [ Beta(f'B_TIME_S_class{i}', 1, None, None, 0) for i in range(NUMBER_OF_CLASSES) ] B_TIME_RND = [ B_TIME[i] + B_TIME_S[i] * bioDraws(f'B_TIME_RND_class{i}', 'NORMAL_ANTI') for i in range(NUMBER_OF_CLASSES) ] .. GENERATED FROM PYTHON SOURCE LINES 66-67 We do the same for the constants, to address serial correlation. .. GENERATED FROM PYTHON SOURCE LINES 67-98 .. code-block:: default ASC_CAR = [ Beta(f'ASC_CAR_class{i}', 0, None, None, 0) for i in range(NUMBER_OF_CLASSES) ] ASC_CAR_S = [ Beta(f'ASC_CAR_S_class{i}', 1, None, None, 0) for i in range(NUMBER_OF_CLASSES) ] ASC_CAR_RND = [ ASC_CAR[i] + ASC_CAR_S[i] * bioDraws(f'ASC_CAR_RND_class{i}', 'NORMAL_ANTI') for i in range(NUMBER_OF_CLASSES) ] ASC_TRAIN = [ Beta(f'ASC_TRAIN_class{i}', 0, None, None, 0) for i in range(NUMBER_OF_CLASSES) ] ASC_TRAIN_S = [ Beta(f'ASC_TRAIN_S_class{i}', 1, None, None, 0) for i in range(NUMBER_OF_CLASSES) ] ASC_TRAIN_RND = [ ASC_TRAIN[i] + ASC_TRAIN_S[i] * bioDraws(f'ASC_TRAIN_RND_class{i}', 'NORMAL_ANTI') for i in range(NUMBER_OF_CLASSES) ] ASC_SM = [Beta(f'ASC_SM_class{i}', 0, None, None, 1) for i in range(NUMBER_OF_CLASSES)] ASC_SM_S = [ Beta(f'ASC_SM_S_class{i}', 1, None, None, 0) for i in range(NUMBER_OF_CLASSES) ] ASC_SM_RND = [ ASC_SM[i] + ASC_SM_S[i] * bioDraws(f'ASC_SM_RND_class{i}', 'NORMAL_ANTI') for i in range(NUMBER_OF_CLASSES) ] .. GENERATED FROM PYTHON SOURCE LINES 99-100 Class membership probability. .. GENERATED FROM PYTHON SOURCE LINES 100-103 .. code-block:: default prob_class0 = Beta('prob_class0', 0.5, 0, 1, 0) prob_class1 = 1 - prob_class0 .. GENERATED FROM PYTHON SOURCE LINES 104-105 In class 0, it is assumed that the time coefficient is zero. .. GENERATED FROM PYTHON SOURCE LINES 105-107 .. code-block:: default B_TIME_RND[0] = 0 .. GENERATED FROM PYTHON SOURCE LINES 108-109 Utility functions. .. GENERATED FROM PYTHON SOURCE LINES 109-123 .. code-block:: default V1 = [ ASC_TRAIN_RND[i] + B_TIME_RND[i] * TRAIN_TT_SCALED + B_COST[i] * TRAIN_COST_SCALED for i in range(NUMBER_OF_CLASSES) ] V2 = [ ASC_SM_RND[i] + B_TIME_RND[i] * SM_TT_SCALED + B_COST[i] * SM_COST_SCALED for i in range(NUMBER_OF_CLASSES) ] V3 = [ ASC_CAR_RND[i] + B_TIME_RND[i] * CAR_TT_SCALED + B_COST[i] * CAR_CO_SCALED for i in range(NUMBER_OF_CLASSES) ] V = [{1: V1[i], 2: V2[i], 3: V3[i]} for i in range(NUMBER_OF_CLASSES)] .. GENERATED FROM PYTHON SOURCE LINES 124-125 Associate the availability conditions with the alternatives. .. GENERATED FROM PYTHON SOURCE LINES 125-127 .. code-block:: default av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP} .. GENERATED FROM PYTHON SOURCE LINES 128-130 The choice model is a discrete mixture of logit, with availability conditions We calculate the conditional probability for each class. .. GENERATED FROM PYTHON SOURCE LINES 130-135 .. code-block:: default prob = [ PanelLikelihoodTrajectory(models.logit(V[i], av, CHOICE)) for i in range(NUMBER_OF_CLASSES) ] .. GENERATED FROM PYTHON SOURCE LINES 136-137 Conditional to the random variables, likelihood for the individual. .. GENERATED FROM PYTHON SOURCE LINES 137-139 .. code-block:: default probIndiv = prob_class0 * prob[0] + prob_class1 * prob[1] .. GENERATED FROM PYTHON SOURCE LINES 140-141 We integrate over the random variables using Monte-Carlo. .. GENERATED FROM PYTHON SOURCE LINES 141-143 .. code-block:: default logprob = log(MonteCarlo(probIndiv)) .. GENERATED FROM PYTHON SOURCE LINES 144-148 Create the Biogeme object. As the objective is to illustrate the syntax, we calculate the Monte-Carlo approximation with a small number of draws. To achieve that, we provide a parameter file different from the default one. .. GENERATED FROM PYTHON SOURCE LINES 148-151 .. code-block:: default the_biogeme = bio.BIOGEME(database, logprob, parameter_file='few_draws.toml') the_biogeme.modelName = 'b15panel_discrete' .. rst-class:: sphx-glr-script-out .. code-block:: none File few_draws.toml has been parsed. .. GENERATED FROM PYTHON SOURCE LINES 152-153 Estimate the parameters. .. GENERATED FROM PYTHON SOURCE LINES 153-155 .. code-block:: default results = the_biogeme.estimate() .. rst-class:: sphx-glr-script-out .. code-block:: none *** Initial values of the parameters are obtained from the file __b15panel_discrete.iter Cannot read file __b15panel_discrete.iter. Statement is ignored. Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds Iter. ASC_CAR_S_class ASC_CAR_S_class ASC_CAR_class0 ASC_CAR_class1 ASC_SM_S_class0 ASC_SM_S_class1 ASC_TRAIN_S_cla ASC_TRAIN_S_cla ASC_TRAIN_class ASC_TRAIN_class B_COST_class0 B_COST_class1 B_TIME_S_class1 B_TIME_class1 prob_class0 Function Relgrad Radius Rho 0 1.3 1.2 -0.094 0.17 1.5 1.2 1.3 1.2 -0.67 -0.92 -0.47 -0.6 2 -1 0 4.1e+03 0.031 10 0.9 ++ 1 1.3 2 -0.11 0.12 1.6 1.6 1.3 1.7 -0.65 -1.4 -0.46 -2.1 2.5 -2.7 0 3.8e+03 0.02 1e+02 0.92 ++ 2 1.3 2 -0.11 0.12 1.6 1.6 1.3 1.7 -0.65 -1.4 -0.46 -2.1 2.5 -2.7 0 3.8e+03 0.02 50 -0.003 - 3 1.3 2 -0.11 0.12 1.6 1.6 1.3 1.7 -0.65 -1.4 -0.46 -2.1 2.5 -2.7 0 3.8e+03 0.02 25 -0.0064 - 4 1.3 2 -0.11 0.12 1.6 1.6 1.3 1.7 -0.65 -1.4 -0.46 -2.1 2.5 -2.7 0 3.8e+03 0.02 12 -0.012 - 5 1.3 2 -0.11 0.12 1.6 1.6 1.3 1.7 -0.65 -1.4 -0.46 -2.1 2.5 -2.7 0 3.8e+03 0.02 6.2 -0.019 - 6 1.3 2 -0.11 0.12 1.6 1.6 1.3 1.7 -0.65 -1.4 -0.46 -2.1 2.5 -2.7 0 3.8e+03 0.02 3.1 -0.019 - 7 1.3 2 -0.11 0.12 1.6 1.6 1.3 1.7 -0.65 -1.4 -0.46 -2.1 2.5 -2.7 0 3.8e+03 0.02 1.6 0.016 - 8 1.4 3.1 -0.24 0.43 1.6 1.5 1.4 2.6 -0.54 -0.65 -0.066 -3.6 3.5 -4.2 8.7e-10 3.7e+03 0.032 1.6 0.18 + 9 1.4 3.1 -0.24 0.43 1.6 1.5 1.4 2.6 -0.54 -0.65 -0.066 -3.6 3.5 -4.2 8.7e-10 3.7e+03 0.032 0.78 -0.049 - 10 1.4 3.5 -0.26 0.22 1.6 1.6 1.4 2.3 -0.53 -0.81 -0.048 -2.8 3.4 -4.8 9.6e-10 3.6e+03 0.012 0.78 0.21 + 11 1.4 3.5 -0.26 0.22 1.6 1.6 1.4 2.3 -0.53 -0.81 -0.048 -2.8 3.4 -4.8 9.6e-10 3.6e+03 0.012 0.39 -0.24 - 12 1.4 3.3 -0.28 0.35 1.6 1.6 1.4 2.3 -0.51 -0.63 -0.034 -3.2 3.5 -4.9 4.2e-09 3.6e+03 0.025 0.39 0.65 + 13 1.5 3.3 -0.32 0.32 1.5 1.7 1.4 2.1 -0.47 -0.46 -0.0055 -3.1 3.7 -5.3 7.8e-09 3.6e+03 0.0036 3.9 0.99 ++ 14 1.7 3.7 -1 0.23 1.2 1.9 1.3 1.4 0.26 -0.12 -0.27 -3.4 4 -5.7 1.6e-08 3.6e+03 0.0062 39 1.5 ++ 15 1.9 4.5 -1.4 0.014 1.2 2.2 1 -0.2 0.25 -0.14 -0.02 -4 4.4 -6.2 3.1e-08 3.6e+03 0.014 39 0.59 + 16 1.9 4.5 -1.4 0.014 1.2 2.2 1 -0.2 0.25 -0.14 -0.02 -4 4.4 -6.2 3.1e-08 3.6e+03 0.014 20 -2.3 - 17 1.9 4.5 -1.4 0.014 1.2 2.2 1 -0.2 0.25 -0.14 -0.02 -4 4.4 -6.2 3.1e-08 3.6e+03 0.014 9.8 -3.9 - 18 1.9 4.5 -1.4 0.014 1.2 2.2 1 -0.2 0.25 -0.14 -0.02 -4 4.4 -6.2 3.1e-08 3.6e+03 0.014 4.9 -5.5 - 19 1.9 4.5 -1.4 0.014 1.2 2.2 1 -0.2 0.25 -0.14 -0.02 -4 4.4 -6.2 3.1e-08 3.6e+03 0.014 2.4 -5.6 - 20 1.9 4.5 -1.4 0.014 1.2 2.2 1 -0.2 0.25 -0.14 -0.02 -4 4.4 -6.2 3.1e-08 3.6e+03 0.014 1.2 -1.2 - 21 1.9 4.5 -1.4 0.014 1.2 2.2 1 -0.2 0.25 -0.14 -0.02 -4 4.4 -6.2 3.1e-08 3.6e+03 0.014 0.61 -0.41 - 22 1.9 3.8 -1.4 0.38 1.2 2.2 0.98 0.24 0.26 0.016 -0.084 -3.9 4.4 -6.2 6.3e-08 3.6e+03 0.007 0.61 0.67 + 23 2 3.9 -1.7 0.34 1.1 2 0.73 0.22 0.25 -0.0093 -0.2 -3.7 4.3 -6.1 1.3e-07 3.6e+03 0.00099 6.1 1.1 ++ 24 2.1 4 -2.3 0.4 0.87 2.1 0.51 0.23 0.011 0.033 -0.091 -3.8 4.4 -6.2 2.5e-07 3.6e+03 0.00076 61 1.2 ++ 25 2.1 3.9 -2.7 0.35 0.5 2.1 0.33 0.23 -0.2 -0.0038 -0.31 -3.7 4.3 -6.1 5e-07 3.6e+03 0.00092 6.1e+02 1.2 ++ 26 2.1 3.9 -2.7 0.35 0.5 2.1 0.33 0.23 -0.2 -0.0038 -0.31 -3.7 4.3 -6.1 5e-07 3.6e+03 0.00092 3.1e+02 -0.085 - 27 2.1 3.9 -2.7 0.35 0.5 2.1 0.33 0.23 -0.2 -0.0038 -0.31 -3.7 4.3 -6.1 5e-07 3.6e+03 0.00092 1.5e+02 -0.1 - 28 2.1 3.9 -2.7 0.35 0.5 2.1 0.33 0.23 -0.2 -0.0038 -0.31 -3.7 4.3 -6.1 5e-07 3.6e+03 0.00092 76 -0.13 - 29 2.1 3.9 -2.7 0.35 0.5 2.1 0.33 0.23 -0.2 -0.0038 -0.31 -3.7 4.3 -6.1 5e-07 3.6e+03 0.00092 38 -0.18 - 30 2.1 3.9 -2.7 0.35 0.5 2.1 0.33 0.23 -0.2 -0.0038 -0.31 -3.7 4.3 -6.1 5e-07 3.6e+03 0.00092 19 -0.26 - 31 2.1 3.9 -2.7 0.35 0.5 2.1 0.33 0.23 -0.2 -0.0038 -0.31 -3.7 4.3 -6.1 5e-07 3.6e+03 0.00092 9.5 -0.32 - 32 2.1 3.9 -2.7 0.35 0.5 2.1 0.33 0.23 -0.2 -0.0038 -0.31 -3.7 4.3 -6.1 5e-07 3.6e+03 0.00092 4.8 -0.37 - 33 2.1 3.9 -2.7 0.35 0.5 2.1 0.33 0.23 -0.2 -0.0038 -0.31 -3.7 4.3 -6.1 5e-07 3.6e+03 0.00092 2.4 -0.37 - 34 2.1 3.9 -2.7 0.35 0.5 2.1 0.33 0.23 -0.2 -0.0038 -0.31 -3.7 4.3 -6.1 5e-07 3.6e+03 0.00092 1.2 -0.25 - 35 2.1 4 -3.6 0.41 -0.7 2.1 -0.26 0.23 -0.9 0.051 -0.002 -3.8 4.5 -6.3 1e-06 3.6e+03 0.0017 1.2 0.12 + 36 2 3.8 -4.1 0.34 0.5 2.1 -0.23 0.25 -0.54 0.022 -0.81 -3.7 4.4 -6.2 2.1e-06 3.6e+03 0.0053 1.2 0.14 + 37 2 3.8 -4.1 0.34 0.5 2.1 -0.23 0.25 -0.54 0.022 -0.81 -3.7 4.4 -6.2 2.1e-06 3.6e+03 0.0053 0.6 -0.33 - 38 2 4 -4.2 0.27 0.49 2.1 -0.18 0.2 -0.72 -0.039 -0.21 -3.8 4.4 -6.1 4.3e-06 3.6e+03 0.00061 0.6 0.88 + 39 2 4 -4.4 0.27 -0.11 2.1 0.03 0.18 -0.77 -0.026 -0.57 -3.8 4.3 -6.1 8.5e-06 3.6e+03 0.00073 6 0.94 ++ 40 1.8 4 -5.5 0.31 0.81 2.1 -0.2 0.25 -1.2 -0.0055 -0.42 -3.8 4.4 -6.2 1.7e-05 3.6e+03 0.0005 6 0.67 + 41 1.8 4 -5.5 0.31 0.81 2.1 -0.2 0.25 -1.2 -0.0055 -0.42 -3.8 4.4 -6.2 1.7e-05 3.6e+03 0.0005 3 -0.36 - 42 1.8 4 -5.5 0.31 0.81 2.1 -0.2 0.25 -1.2 -0.0055 -0.42 -3.8 4.4 -6.2 1.7e-05 3.6e+03 0.0005 1.5 0.035 - 43 1.6 4 -6.3 0.25 -0.68 2.1 0.07 0.2 -1.1 -0.034 -0.84 -3.8 4.3 -6.1 3.3e-05 3.6e+03 0.0012 1.5 0.5 + 44 1.6 4 -6.3 0.25 -0.68 2.1 0.07 0.2 -1.1 -0.034 -0.84 -3.8 4.3 -6.1 3.3e-05 3.6e+03 0.0012 0.75 -0.22 - 45 1.4 4 -6.7 0.3 0.066 2.1 -0.045 0.18 -1.5 -0.029 -0.38 -3.8 4.4 -6.2 5e-05 3.6e+03 0.00074 0.75 0.35 + 46 0.86 4 -7.5 0.26 -0.56 2 0.16 0.22 -2.3 -0.0077 -1.1 -3.8 4.4 -6.1 0.00011 3.6e+03 0.0011 0.75 0.8 + 47 0.17 4 -8.2 0.34 -0.21 1.9 0.45 0.22 -2.9 0.053 -0.75 -3.9 4.3 -6.1 0.00029 3.6e+03 0.0018 0.75 0.79 + 48 0.066 3.9 -8.6 0.39 0.43 2 0.1 0.25 -2.6 0.0051 -1.5 -3.9 4.3 -6.1 0.00063 3.6e+03 0.0034 0.75 0.3 + 49 0.066 3.9 -8.6 0.39 0.43 2 0.1 0.25 -2.6 0.0051 -1.5 -3.9 4.3 -6.1 0.00063 3.6e+03 0.0034 0.37 -0.13 - 50 0.067 3.8 -8.7 0.49 0.43 2 0.093 0.079 -2.6 0.043 -1.1 -4 4.4 -6.1 0.0016 3.6e+03 0.0028 3.7 1.2 ++ 51 0.002 3.7 -12 0.55 -2.2 2 -0.15 -0.11 -3.4 0.089 -1.6 -3.9 4.4 -6.1 0.0034 3.6e+03 0.0079 3.7 0.73 + 52 0.002 3.7 -12 0.55 -2.2 2 -0.15 -0.11 -3.4 0.089 -1.6 -3.9 4.4 -6.1 0.0034 3.6e+03 0.0079 1.9 -3.8 - 53 0.002 3.7 -12 0.55 -2.2 2 -0.15 -0.11 -3.4 0.089 -1.6 -3.9 4.4 -6.1 0.0034 3.6e+03 0.0079 0.93 -0.18 - 54 -0.0018 3.6 -12 0.6 -1.7 2.1 0.024 -1 -3 0.075 -1.1 -3.9 4.4 -6.2 0.0068 3.6e+03 0.0033 0.93 0.55 + 55 -0.0018 3.6 -12 0.6 -1.7 2.1 0.024 -1 -3 0.075 -1.1 -3.9 4.4 -6.2 0.0068 3.6e+03 0.0033 0.47 -0.7 - 56 -0.0017 3.5 -12 0.64 -1.7 2.2 0.052 -0.58 -2.9 -0.13 -1.5 -4 4.1 -5.8 0.013 3.6e+03 0.0056 0.47 0.3 + 57 -0.0011 3.5 -12 0.7 -1.7 2.1 0.084 -1 -2.8 -0.1 -1.6 -3.9 4.2 -6 0.022 3.6e+03 0.0015 4.7 0.96 ++ 58 -0.0011 3.5 -12 0.7 -1.7 2.1 0.084 -1 -2.8 -0.1 -1.6 -3.9 4.2 -6 0.022 3.6e+03 0.0015 2.3 -3.9 - 59 -0.0011 3.5 -12 0.7 -1.7 2.1 0.084 -1 -2.8 -0.1 -1.6 -3.9 4.2 -6 0.022 3.6e+03 0.0015 1.2 -1.5 - 60 -0.0011 3.5 -12 0.7 -1.7 2.1 0.084 -1 -2.8 -0.1 -1.6 -3.9 4.2 -6 0.022 3.6e+03 0.0015 0.58 -0.3 - 61 -0.0011 3.4 -12 0.73 -2 2.1 -0.08 -0.65 -2.2 -0.071 -1.7 -3.8 4.1 -5.9 0.034 3.6e+03 0.0023 0.58 0.67 + 62 -0.0019 3.5 -12 0.84 -2.4 2.2 -0.43 -1.2 -1.8 -0.21 -1.7 -3.9 4.3 -6.1 0.052 3.6e+03 0.0023 0.58 0.76 + 63 -0.0019 3.5 -12 0.84 -2.4 2.2 -0.43 -1.2 -1.8 -0.21 -1.7 -3.9 4.3 -6.1 0.052 3.6e+03 0.0023 0.29 -0.12 - 64 -0.0031 3.4 -12 0.8 -2.5 1.9 -0.45 -1.5 -1.6 -0.34 -1.8 -3.6 4.2 -5.8 0.059 3.6e+03 0.031 0.29 0.5 + 65 -0.0035 3.1 -12 0.81 -2.6 2 -0.44 -1.8 -1.6 -0.29 -1.8 -3.6 4 -6 0.06 3.6e+03 0.014 0.29 0.55 + 66 -0.0069 3 -12 0.84 -2.6 2.1 -0.34 -2 -1.5 -0.58 -1.8 -3.6 3.8 -5.8 0.067 3.6e+03 0.0014 2.9 0.99 ++ 67 -0.0069 3 -12 0.84 -2.6 2.1 -0.34 -2 -1.5 -0.58 -1.8 -3.6 3.8 -5.8 0.067 3.6e+03 0.0014 1.5 -0.87 - 68 -0.0069 3 -12 0.84 -2.6 2.1 -0.34 -2 -1.5 -0.58 -1.8 -3.6 3.8 -5.8 0.067 3.6e+03 0.0014 0.73 -0.31 - 69 -0.023 3 -12 0.88 -3 2.1 0.39 -2 -0.86 -0.71 -1.9 -3.5 3.8 -5.9 0.078 3.6e+03 0.0027 0.73 0.19 + 70 -0.056 2.9 -13 0.83 -2.8 2.1 0.67 -1.9 -1.2 -0.63 -1.9 -3.5 3.8 -5.8 0.073 3.6e+03 0.00064 7.3 0.92 ++ 71 -0.056 2.9 -13 0.83 -2.8 2.1 0.67 -1.9 -1.2 -0.63 -1.9 -3.5 3.8 -5.8 0.073 3.6e+03 0.00064 3.6 -1.5 - 72 -0.056 2.9 -13 0.83 -2.8 2.1 0.67 -1.9 -1.2 -0.63 -1.9 -3.5 3.8 -5.8 0.073 3.6e+03 0.00064 1.8 -1.7 - 73 -0.056 2.9 -13 0.83 -2.8 2.1 0.67 -1.9 -1.2 -0.63 -1.9 -3.5 3.8 -5.8 0.073 3.6e+03 0.00064 0.91 -1.3 - 74 -0.056 2.9 -13 0.83 -2.8 2.1 0.67 -1.9 -1.2 -0.63 -1.9 -3.5 3.8 -5.8 0.073 3.6e+03 0.00064 0.45 0.032 - 75 -0.059 2.9 -13 0.82 -3.2 2 0.96 -1.9 -0.72 -0.66 -2.1 -3.5 3.7 -5.7 0.076 3.6e+03 0.00045 4.5 1.1 ++ 76 -0.059 2.9 -13 0.82 -3.2 2 0.96 -1.9 -0.72 -0.66 -2.1 -3.5 3.7 -5.7 0.076 3.6e+03 0.00045 2.3 -3.8 - 77 -0.059 2.9 -13 0.82 -3.2 2 0.96 -1.9 -0.72 -0.66 -2.1 -3.5 3.7 -5.7 0.076 3.6e+03 0.00045 1.1 -0.22 - 78 -0.059 2.9 -13 0.82 -3.2 2 0.96 -1.9 -0.72 -0.66 -2.1 -3.5 3.7 -5.7 0.076 3.6e+03 0.00045 0.57 -0.11 - 79 -0.062 2.9 -13 0.81 -3 2 1.5 -2 -0.78 -0.7 -2 -3.5 3.7 -5.7 0.074 3.6e+03 0.0004 0.57 0.35 + 80 -0.2 2.9 -14 0.81 -2.7 2.1 1.4 -1.9 -0.42 -0.69 -2.2 -3.5 3.7 -5.7 0.075 3.6e+03 0.002 0.57 0.57 + 81 -0.29 2.9 -14 0.81 -2.9 2 1.6 -1.9 -0.49 -0.68 -2.3 -3.5 3.7 -5.7 0.075 3.6e+03 0.00026 5.7 1.1 ++ 82 -0.5 2.9 -17 0.81 -3.1 2 1.6 -1.9 -0.62 -0.68 -2.6 -3.5 3.7 -5.7 0.076 3.6e+03 0.00011 57 1.2 ++ 83 -0.33 2.9 -18 0.82 -3.3 2 1.5 -1.9 -0.8 -0.67 -2.8 -3.5 3.7 -5.7 0.076 3.6e+03 9.2e-05 57 0.8 + 84 -0.31 2.9 -19 0.82 -3.2 2 1.5 -1.9 -0.75 -0.67 -2.7 -3.5 3.7 -5.7 0.076 3.6e+03 5.8e-05 5.7e+02 1.1 ++ 85 -0.27 2.9 -19 0.82 -3.2 2 1.5 -1.9 -0.71 -0.68 -2.7 -3.5 3.7 -5.7 0.076 3.6e+03 3.4e-05 5.7e+03 1.1 ++ 86 -0.25 2.9 -19 0.82 -3.2 2 1.5 -1.9 -0.76 -0.67 -2.8 -3.5 3.7 -5.7 0.076 3.6e+03 4.7e-05 5.7e+04 0.97 ++ 87 -0.23 2.9 -19 0.82 -3.2 2 1.5 -1.9 -0.73 -0.68 -2.7 -3.5 3.7 -5.7 0.076 3.6e+03 3.1e-05 5.7e+05 1.1 ++ 88 -0.22 2.9 -19 0.82 -3.3 2 1.5 -1.9 -0.76 -0.67 -2.8 -3.5 3.7 -5.7 0.076 3.6e+03 3.9e-05 5.7e+06 0.99 ++ 89 -0.2 2.9 -19 0.82 -3.2 2 1.5 -1.9 -0.74 -0.67 -2.7 -3.5 3.7 -5.7 0.076 3.6e+03 2.8e-05 5.7e+07 1.1 ++ 90 -0.19 2.9 -20 0.82 -3.3 2 1.5 -1.9 -0.77 -0.67 -2.8 -3.5 3.7 -5.7 0.076 3.6e+03 3.4e-05 5.7e+08 1 ++ 91 -0.18 2.9 -20 0.82 -3.2 2 1.5 -1.9 -0.75 -0.67 -2.8 -3.5 3.7 -5.7 0.076 3.6e+03 2.5e-05 5.7e+09 1 ++ 92 -0.17 2.9 -20 0.82 -3.3 2 1.5 -1.9 -0.77 -0.67 -2.8 -3.5 3.7 -5.7 0.076 3.6e+03 3e-05 5.7e+10 1 ++ 93 -0.16 2.9 -20 0.82 -3.3 2 1.5 -1.9 -0.75 -0.67 -2.8 -3.5 3.7 -5.7 0.076 3.6e+03 2.3e-05 5.7e+11 1 ++ 94 -0.15 2.9 -20 0.82 -3.3 2 1.5 -1.9 -0.78 -0.67 -2.8 -3.5 3.7 -5.7 0.076 3.6e+03 2.6e-05 5.7e+12 1 ++ 95 -0.14 2.9 -20 0.82 -3.3 2 1.5 -1.9 -0.76 -0.67 -2.8 -3.5 3.7 -5.7 0.076 3.6e+03 2.1e-05 5.7e+13 1 ++ 96 -0.13 2.9 -20 0.82 -3.3 2 1.5 -1.9 -0.78 -0.67 -2.8 -3.5 3.7 -5.7 0.076 3.6e+03 2.3e-05 5.7e+14 1 ++ 97 -0.13 2.9 -20 0.82 -3.3 2 1.5 -1.9 -0.76 -0.67 -2.8 -3.5 3.7 -5.7 0.076 3.6e+03 1.9e-05 5.7e+15 1 ++ 98 -0.12 2.9 -20 0.82 -3.3 2 1.5 -1.9 -0.78 -0.67 -2.8 -3.5 3.7 -5.7 0.076 3.6e+03 2.1e-05 5.7e+16 1 ++ 99 -0.12 2.9 -20 0.82 -3.3 2 1.5 -1.9 -0.77 -0.67 -2.8 -3.5 3.7 -5.7 0.076 3.6e+03 1.7e-05 5.7e+17 1 ++ It seems that the optimization algorithm did not converge. Therefore, the results may not correspond to the maximum likelihood estimator. Check the specification of the model, or the criteria for convergence of the algorithm. Results saved in file b15panel_discrete.html Results saved in file b15panel_discrete.pickle .. GENERATED FROM PYTHON SOURCE LINES 156-158 .. code-block:: default print(results.short_summary()) .. rst-class:: sphx-glr-script-out .. code-block:: none Results for model b15panel_discrete Nbr of parameters: 15 Sample size: 752 Observations: 6768 Excluded data: 3960 Final log likelihood: -3578.193 Akaike Information Criterion: 7186.386 Bayesian Information Criterion: 7255.727 .. GENERATED FROM PYTHON SOURCE LINES 159-161 .. code-block:: default pandas_results = results.getEstimatedParameters() pandas_results .. raw:: html
Value Rob. Std err Rob. t-test Rob. p-value
ASC_CAR_S_class0 -0.118619 2.031099 -0.058401 9.534290e-01
ASC_CAR_S_class1 2.909162 0.408106 7.128448 1.014966e-12
ASC_CAR_class0 -20.269515 9.674318 -2.095188 3.615427e-02
ASC_CAR_class1 0.817420 0.292525 2.794361 5.200245e-03
ASC_SM_S_class0 -3.273308 1.434160 -2.282387 2.246648e-02
ASC_SM_S_class1 2.018813 0.269743 7.484219 7.194245e-14
ASC_TRAIN_S_class0 1.529683 0.645907 2.368271 1.787143e-02
ASC_TRAIN_S_class1 -1.892145 0.279812 -6.762206 1.359068e-11
ASC_TRAIN_class0 -0.766823 1.289227 -0.594793 5.519821e-01
ASC_TRAIN_class1 -0.674255 0.271821 -2.480514 1.311932e-02
B_COST_class0 -2.807446 2.112216 -1.329148 1.837993e-01
B_COST_class1 -3.500704 0.383020 -9.139750 0.000000e+00
B_TIME_S_class1 3.724659 0.225306 16.531531 0.000000e+00
B_TIME_class1 -5.687841 0.321505 -17.691281 0.000000e+00
prob_class0 0.076134 0.024084 3.161264 1.570860e-03


.. rst-class:: sphx-glr-timing **Total running time of the script:** (11 minutes 58.663 seconds) .. _sphx_glr_download_auto_examples_swissmetro_plot_b15panel_discrete.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_b15panel_discrete.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_b15panel_discrete.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_