.. 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_bis.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_bis.py: 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. Compared to :ref:`plot_b15panel_discrete`, we integrate before the discrete mixture to show that it is equivalent. :author: Michel Bierlaire, EPFL :date: Mon Apr 10 11:55:26 2023 .. GENERATED FROM PYTHON SOURCE LINES 15-27 .. 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 28-29 See the data processing script: :ref:`swissmetro_panel`. .. GENERATED FROM PYTHON SOURCE LINES 29-46 .. 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_bis.py') .. rst-class:: sphx-glr-script-out .. code-block:: none Example b15panel_discrete_bis.py .. GENERATED FROM PYTHON SOURCE LINES 47-48 Parameters to be estimated. One version for each latent class. .. GENERATED FROM PYTHON SOURCE LINES 48-51 .. 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 52-54 Define a random parameter, normally distributed across individuals, designed to be used for Monte-Carlo simulation. .. GENERATED FROM PYTHON SOURCE LINES 54-56 .. 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 57-58 It is advised not to use 0 as starting value for the following parameter. .. GENERATED FROM PYTHON SOURCE LINES 58-66 .. 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 67-68 We do the same for the constants, to address serial correlation. .. GENERATED FROM PYTHON SOURCE LINES 68-99 .. 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 100-101 Class memebership probability. .. GENERATED FROM PYTHON SOURCE LINES 101-104 .. code-block:: default prob_class0 = Beta('prob_class0', 0.5, 0, 1, 0) prob_class1 = 1 - prob_class0 .. GENERATED FROM PYTHON SOURCE LINES 105-106 In class 0, it is assumed that the time coefficient is zero. .. GENERATED FROM PYTHON SOURCE LINES 106-108 .. code-block:: default B_TIME_RND[0] = 0 .. GENERATED FROM PYTHON SOURCE LINES 109-110 Utility functions. .. GENERATED FROM PYTHON SOURCE LINES 110-124 .. 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 125-126 Associate the availability conditions with the alternatives .. GENERATED FROM PYTHON SOURCE LINES 126-128 .. code-block:: default av = {1: TRAIN_AV_SP, 2: SM_AV, 3: CAR_AV_SP} .. GENERATED FROM PYTHON SOURCE LINES 129-131 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 131-136 .. code-block:: default prob = [ MonteCarlo(PanelLikelihoodTrajectory(models.logit(V[i], av, CHOICE))) for i in range(NUMBER_OF_CLASSES) ] .. GENERATED FROM PYTHON SOURCE LINES 137-138 Conditional to the random variables, likelihood for the individual. .. GENERATED FROM PYTHON SOURCE LINES 138-140 .. code-block:: default probIndiv = prob_class0 * prob[0] + prob_class1 * prob[1] .. GENERATED FROM PYTHON SOURCE LINES 141-142 We integrate over the random variables using Monte-Carlo. .. GENERATED FROM PYTHON SOURCE LINES 142-144 .. code-block:: default logprob = log(probIndiv) .. GENERATED FROM PYTHON SOURCE LINES 145-149 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 149-152 .. code-block:: default the_biogeme = bio.BIOGEME(database, logprob, parameter_file='few_draws.toml') the_biogeme.modelName = 'b15panel_discrete_bis' .. rst-class:: sphx-glr-script-out .. code-block:: none File few_draws.toml has been parsed. .. GENERATED FROM PYTHON SOURCE LINES 153-154 Estimate the parameters. .. GENERATED FROM PYTHON SOURCE LINES 154-156 .. 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_bis.iter Cannot read file __b15panel_discrete_bis.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 8.4e-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 8.4e-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 3.2e-09 3.6e+03 0.013 0.78 0.22 + 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 3.2e-09 3.6e+03 0.013 0.39 -0.54 - 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 7.6e-09 3.6e+03 0.025 0.39 0.76 + 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 1.7e-08 3.6e+03 0.0036 3.9 1 ++ 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 3.4e-08 3.6e+03 0.0062 39 1.5 ++ 15 1.9 4.5 -1.4 0.014 1.2 2.2 1 -0.19 0.25 -0.14 -0.02 -4 4.4 -6.2 6.7e-08 3.6e+03 0.014 39 0.59 + 16 1.9 4.5 -1.4 0.014 1.2 2.2 1 -0.19 0.25 -0.14 -0.02 -4 4.4 -6.2 6.7e-08 3.6e+03 0.014 20 -2.3 - 17 1.9 4.5 -1.4 0.014 1.2 2.2 1 -0.19 0.25 -0.14 -0.02 -4 4.4 -6.2 6.7e-08 3.6e+03 0.014 9.8 -3.9 - 18 1.9 4.5 -1.4 0.014 1.2 2.2 1 -0.19 0.25 -0.14 -0.02 -4 4.4 -6.2 6.7e-08 3.6e+03 0.014 4.9 -5.5 - 19 1.9 4.5 -1.4 0.014 1.2 2.2 1 -0.19 0.25 -0.14 -0.02 -4 4.4 -6.2 6.7e-08 3.6e+03 0.014 2.4 -5.5 - 20 1.9 4.5 -1.4 0.014 1.2 2.2 1 -0.19 0.25 -0.14 -0.02 -4 4.4 -6.2 6.7e-08 3.6e+03 0.014 1.2 -1.1 - 21 1.9 4.5 -1.4 0.014 1.2 2.2 1 -0.19 0.25 -0.14 -0.02 -4 4.4 -6.2 6.7e-08 3.6e+03 0.014 0.61 -0.42 - 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 1.3e-07 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 -0.0098 -0.2 -3.7 4.3 -6.1 2.7e-07 3.6e+03 0.00099 6.1 1.1 ++ 24 2.1 4 -2.3 0.4 0.87 2.1 0.52 0.23 0.016 0.034 -0.092 -3.8 4.4 -6.2 5.4e-07 3.6e+03 0.00072 61 1.2 ++ 25 2.1 3.9 -2.7 0.35 0.5 2.1 0.33 0.23 -0.19 -0.0048 -0.31 -3.7 4.3 -6.1 1.1e-06 3.6e+03 0.00096 6.1e+02 1.2 ++ 26 2.1 3.9 -2.7 0.35 0.5 2.1 0.33 0.23 -0.19 -0.0048 -0.31 -3.7 4.3 -6.1 1.1e-06 3.6e+03 0.00096 2.2 -5.1 - 27 2.1 3.9 -2.7 0.35 0.5 2.1 0.33 0.23 -0.19 -0.0048 -0.31 -3.7 4.3 -6.1 1.1e-06 3.6e+03 0.00096 1.1 -0.82 - 28 2.1 3.9 -2.7 0.35 0.5 2.1 0.33 0.23 -0.19 -0.0048 -0.31 -3.7 4.3 -6.1 1.1e-06 3.6e+03 0.00096 0.55 -0.2 - 29 2.1 4 -3.1 0.39 -0.047 2.1 0.067 0.25 -0.55 0.036 -0.065 -3.8 4.4 -6.3 1.9e-06 3.6e+03 0.0011 0.55 0.71 + 30 2.1 3.9 -3.6 0.32 0.11 2.1 -0.097 0.18 -0.73 -0.0032 -0.34 -3.8 4.3 -6.1 3.7e-06 3.6e+03 0.00066 5.5 1.2 ++ 31 2.1 4 -4.4 0.33 -0.24 2.1 0.2 0.22 -0.85 -0.0016 -0.42 -3.8 4.4 -6.2 7.5e-06 3.6e+03 0.00027 55 1.2 ++ 32 2.1 4 -5.3 0.29 0.48 2.1 -0.36 0.22 -1.1 -0.019 -0.48 -3.8 4.3 -6.1 1.5e-05 3.6e+03 0.0004 55 0.89 + 33 2.1 4 -5.3 0.29 0.48 2.1 -0.36 0.22 -1.1 -0.019 -0.48 -3.8 4.3 -6.1 1.5e-05 3.6e+03 0.0004 20 -3.7 - 34 2.1 4 -5.3 0.29 0.48 2.1 -0.36 0.22 -1.1 -0.019 -0.48 -3.8 4.3 -6.1 1.5e-05 3.6e+03 0.0004 10 -0.79 - 35 2.1 4 -5.3 0.29 0.48 2.1 -0.36 0.22 -1.1 -0.019 -0.48 -3.8 4.3 -6.1 1.5e-05 3.6e+03 0.0004 5 -0.4 - 36 2.1 4 -5.3 0.29 0.48 2.1 -0.36 0.22 -1.1 -0.019 -0.48 -3.8 4.3 -6.1 1.5e-05 3.6e+03 0.0004 2.5 -0.19 - 37 2.1 4 -5.3 0.29 0.48 2.1 -0.36 0.22 -1.1 -0.019 -0.48 -3.8 4.3 -6.1 1.5e-05 3.6e+03 0.0004 1.3 0.01 - 38 2 4.1 -6 0.28 -0.78 2.1 0.32 0.23 -1.2 -0.0021 -0.81 -3.9 4.4 -6.2 3.1e-05 3.6e+03 0.0016 1.3 0.56 + 39 1.7 4 -6.9 0.23 0.48 2.1 0.04 0.069 -1.6 -0.044 -0.38 -3.8 4.3 -6.1 6.3e-05 3.6e+03 0.0012 1.3 0.36 + 40 1.4 4 -7.3 0.35 -0.78 2.1 0.014 0.2 -2.1 0.019 -1.3 -3.8 4.4 -6.2 0.00015 3.6e+03 0.0013 1.3 0.48 + 41 1.4 4 -7.3 0.35 -0.78 2.1 0.014 0.2 -2.1 0.019 -1.3 -3.8 4.4 -6.2 0.00015 3.6e+03 0.0013 0.63 0.022 - 42 1.4 3.9 -7.5 0.44 -0.82 2 0.029 0.13 -2.2 0.05 -0.71 -3.9 4.3 -6.1 0.00043 3.6e+03 0.0016 6.3 1.1 ++ 43 1.4 3.9 -7.5 0.44 -0.82 2 0.029 0.13 -2.2 0.05 -0.71 -3.9 4.3 -6.1 0.00043 3.6e+03 0.0016 3.1 -0.6 - 44 1.4 3.9 -7.5 0.44 -0.82 2 0.029 0.13 -2.2 0.05 -0.71 -3.9 4.3 -6.1 0.00043 3.6e+03 0.0016 1.6 -0.78 - 45 1.4 3.9 -7.5 0.44 -0.82 2 0.029 0.13 -2.2 0.05 -0.71 -3.9 4.3 -6.1 0.00043 3.6e+03 0.0016 0.78 -0.054 - 46 1.1 3.7 -8.3 0.48 -0.13 2 0.37 0.091 -2.8 0.02 -1.4 -3.9 4.3 -6 0.0011 3.6e+03 0.002 7.8 0.96 ++ 47 1.1 3.7 -8.3 0.48 -0.13 2 0.37 0.091 -2.8 0.02 -1.4 -3.9 4.3 -6 0.0011 3.6e+03 0.002 3.9 -0.61 - 48 1.1 3.7 -8.3 0.48 -0.13 2 0.37 0.091 -2.8 0.02 -1.4 -3.9 4.3 -6 0.0011 3.6e+03 0.002 2 -0.33 - 49 -0.67 4.1 -10 0.49 1.2 2.1 -1.4 -0.17 -3.1 0.13 -0.92 -4 4.7 -6.6 0.0022 3.6e+03 0.0036 2 0.2 + 50 -0.67 4.1 -10 0.49 1.2 2.1 -1.4 -0.17 -3.1 0.13 -0.92 -4 4.7 -6.6 0.0022 3.6e+03 0.0036 0.98 -0.56 - 51 -0.63 3.3 -10 0.67 0.9 2.1 -1.2 -0.99 -2.9 0.066 -1.9 -3.7 4.1 -5.9 0.0053 3.6e+03 0.0052 0.98 0.35 + 52 -0.63 3.3 -10 0.67 0.9 2.1 -1.2 -0.99 -2.9 0.066 -1.9 -3.7 4.1 -5.9 0.0053 3.6e+03 0.0052 0.49 -0.11 - 53 -0.66 3.6 -10 0.56 0.91 2.1 -1.2 -0.86 -2.9 -0.12 -1.4 -3.9 4.1 -5.9 0.0099 3.6e+03 0.0013 4.9 1 ++ 54 0.27 3.6 -12 0.67 0.52 2.1 -1.9 -0.85 -2.8 -0.068 -1.6 -3.8 4.2 -6 0.018 3.6e+03 0.00041 49 1.4 ++ 55 0.13 3.5 -13 0.74 0.38 2.1 -2.1 -0.92 -2 -0.12 -1.6 -3.8 4.2 -6 0.033 3.6e+03 0.001 4.9e+02 1.4 ++ 56 0.13 3.5 -13 0.74 0.38 2.1 -2.1 -0.92 -2 -0.12 -1.6 -3.8 4.2 -6 0.033 3.6e+03 0.001 24 -24 - 57 0.13 3.5 -13 0.74 0.38 2.1 -2.1 -0.92 -2 -0.12 -1.6 -3.8 4.2 -6 0.033 3.6e+03 0.001 12 -3.6 - 58 0.13 3.5 -13 0.74 0.38 2.1 -2.1 -0.92 -2 -0.12 -1.6 -3.8 4.2 -6 0.033 3.6e+03 0.001 5.9 -5.1 - 59 0.13 3.5 -13 0.74 0.38 2.1 -2.1 -0.92 -2 -0.12 -1.6 -3.8 4.2 -6 0.033 3.6e+03 0.001 2.9 -4.9 - 60 0.13 3.5 -13 0.74 0.38 2.1 -2.1 -0.92 -2 -0.12 -1.6 -3.8 4.2 -6 0.033 3.6e+03 0.001 1.5 -2.5 - 61 0.1 3 -13 1.2 0.4 2.3 -3.6 -1.1 -0.94 -0.31 -1.6 -3.6 4.3 -6.1 0.076 3.6e+03 0.0047 1.5 0.54 + 62 0.1 3 -13 1.2 0.4 2.3 -3.6 -1.1 -0.94 -0.31 -1.6 -3.6 4.3 -6.1 0.076 3.6e+03 0.0047 0.74 -1.6 - 63 0.09 3 -13 0.52 0.47 1.9 -3.6 -1.8 -0.47 -0.87 -1.8 -3.8 3.7 -5.3 0.074 3.6e+03 0.0078 0.74 0.44 + 64 -0.15 3 -14 0.77 0.64 2 -3.3 -1.8 -0.55 -0.68 -1.8 -3.5 3.9 -5.6 0.073 3.6e+03 0.0027 7.4 1 ++ 65 -0.72 3 -17 0.79 0.64 2 -3.5 -1.8 -0.71 -0.67 -2.2 -3.5 3.9 -5.6 0.075 3.6e+03 0.00018 74 1.2 ++ 66 -0.32 3 -21 0.8 0.57 2 -3.7 -1.8 -0.8 -0.67 -2.6 -3.5 3.9 -5.6 0.076 3.6e+03 0.0001 7.4e+02 1.2 ++ 67 -0.35 3 -21 0.8 0.58 2 -3.6 -1.8 -0.77 -0.67 -2.6 -3.5 3.9 -5.6 0.076 3.6e+03 4.8e-05 7.4e+03 1.2 ++ 68 -0.36 3 -21 0.8 0.6 2 -3.7 -1.8 -0.78 -0.67 -2.6 -3.5 3.9 -5.6 0.076 3.6e+03 5.8e-05 7.4e+04 1 ++ 69 -0.36 3 -21 0.8 0.59 2 -3.6 -1.8 -0.78 -0.67 -2.6 -3.5 3.9 -5.6 0.076 3.6e+03 4.3e-05 7.4e+05 1 ++ 70 -0.36 3 -21 0.8 0.61 2 -3.7 -1.8 -0.79 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 5e-05 7.4e+06 1 ++ 71 -0.36 2.9 -22 0.8 0.6 2 -3.7 -1.8 -0.79 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 9.1e-05 7.4e+07 1 ++ 72 -0.36 2.9 -22 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 3.8e-05 7.4e+08 1 ++ 73 -0.36 3 -22 0.8 0.61 2 -3.7 -1.8 -0.79 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 2.8e-05 7.4e+09 1 ++ 74 -0.36 2.9 -23 0.8 0.61 2 -3.8 -1.8 -0.82 -0.67 -2.8 -3.5 3.9 -5.6 0.076 3.6e+03 2.7e-05 7.4e+10 1 ++ 75 -0.36 2.9 -23 0.8 0.6 2 -3.7 -1.8 -0.81 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 5.5e-05 7.4e+11 1.1 ++ 76 -0.36 2.9 -23 0.8 0.61 2 -3.7 -1.8 -0.8 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 1.6e-05 7.4e+12 0.98 ++ 77 -0.36 2.9 -23 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 1.4e-05 7.4e+13 1 ++ 78 -0.36 2.9 -23 0.8 0.61 2 -3.7 -1.8 -0.8 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 1.2e-05 7.4e+14 1 ++ 79 -0.36 2.9 -23 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 1.3e-05 7.4e+15 1 ++ 80 -0.35 2.9 -23 0.8 0.61 2 -3.7 -1.8 -0.8 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 1.1e-05 7.4e+16 1 ++ 81 -0.35 2.9 -23 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 1.2e-05 7.4e+17 1 ++ 82 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.8 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 1.1e-05 7.4e+18 1 ++ 83 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 1.2e-05 7.4e+19 1 ++ 84 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 1e-05 7.4e+20 1 ++ 85 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 1.1e-05 7.4e+21 1 ++ 86 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 9.5e-06 7.4e+22 1 ++ 87 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 1e-05 7.4e+23 1 ++ 88 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 9e-06 7.4e+24 1 ++ 89 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.8 -3.5 3.9 -5.6 0.076 3.6e+03 9.7e-06 7.4e+25 1 ++ 90 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 8.6e-06 7.4e+26 1 ++ 91 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.8 -3.5 3.9 -5.6 0.076 3.6e+03 9.2e-06 7.4e+27 1 ++ 92 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 8.2e-06 7.4e+28 1 ++ 93 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.8 -3.5 3.9 -5.6 0.076 3.6e+03 8.7e-06 7.4e+29 1 ++ 94 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 7.8e-06 7.4e+30 1 ++ 95 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.8 -3.5 3.9 -5.6 0.076 3.6e+03 8.3e-06 7.4e+31 1 ++ 96 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 7.4e-06 7.4e+32 1 ++ 97 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.8 -3.5 3.9 -5.6 0.076 3.6e+03 7.8e-06 7.4e+33 1 ++ 98 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.7 -3.5 3.9 -5.6 0.076 3.6e+03 7.1e-06 7.4e+34 1 ++ 99 -0.35 2.9 -24 0.8 0.61 2 -3.7 -1.8 -0.81 -0.67 -2.8 -3.5 3.9 -5.6 0.076 3.6e+03 7.4e-06 7.4e+35 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_bis.html Results saved in file b15panel_discrete_bis.pickle .. GENERATED FROM PYTHON SOURCE LINES 157-159 .. code-block:: default print(results.short_summary()) .. rst-class:: sphx-glr-script-out .. code-block:: none Results for model b15panel_discrete_bis Nbr of parameters: 15 Sample size: 752 Observations: 6768 Excluded data: 3960 Final log likelihood: -3579.247 Akaike Information Criterion: 7188.494 Bayesian Information Criterion: 7257.835 .. GENERATED FROM PYTHON SOURCE LINES 160-162 .. 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 -0.347191 0.838490 -0.414068 6.788246e-01
ASC_CAR_S_class1 2.948644 0.336060 8.774169 0.000000e+00
ASC_CAR_class0 -24.064217 10.386551 -2.316863 2.051119e-02
ASC_CAR_class1 0.798903 0.249408 3.203197 1.359111e-03
ASC_SM_S_class0 0.611774 0.545488 1.121518 2.620675e-01
ASC_SM_S_class1 2.025701 0.251123 8.066581 6.661338e-16
ASC_TRAIN_S_class0 -3.734526 1.206602 -3.095076 1.967628e-03
ASC_TRAIN_S_class1 -1.773845 0.307962 -5.759947 8.414033e-09
ASC_TRAIN_class0 -0.813031 0.576496 -1.410299 1.584513e-01
ASC_TRAIN_class1 -0.671540 0.264429 -2.539583 1.109846e-02
B_COST_class0 -2.755227 1.757661 -1.567553 1.169855e-01
B_COST_class1 -3.513843 0.362804 -9.685245 0.000000e+00
B_TIME_S_class1 3.925849 0.238421 16.466049 0.000000e+00
B_TIME_class1 -5.628915 0.323817 -17.383012 0.000000e+00
prob_class0 0.076362 0.021218 3.598868 3.196055e-04


.. rst-class:: sphx-glr-timing **Total running time of the script:** (10 minutes 38.548 seconds) .. _sphx_glr_download_auto_examples_swissmetro_plot_b15panel_discrete_bis.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_bis.py ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_b15panel_discrete_bis.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_