.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/latent/plot_b07problem.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_latent_plot_b07problem.py: Illustration of a common estimation problem =========================================== This file is the same as b02one_latent_ordered.py, where the starting values for the sigma have been changed in order to illustrate a common issue with the estimation of such models. We set the starting value of a scale parameter (SIGMA_STAR_Envir02) to a small value: 0.01. The resulting likelihood is so close to zero that taking the log generates a numerical issue. Make sure to set large initial values for scale parameters. :author: Michel Bierlaire, EPFL :date: Thu Apr 13 18:19:27 2023 .. GENERATED FROM PYTHON SOURCE LINES 20-51 .. code-block:: Python import sys import biogeme.biogeme_logging as blog from biogeme.models import piecewise_formula import biogeme.biogeme as bio from biogeme.expressions import Beta, log, Elem, bioNormalCdf from biogeme.data.optima import ( read_data, age_65_more, ScaledIncome, moreThanOneCar, moreThanOneBike, individualHouse, male, haveChildren, haveGA, highEducation, Envir01, Envir02, Envir03, Mobil11, Mobil14, Mobil16, Mobil17, ) logger = blog.get_screen_logger(level=blog.INFO) logger.info('Example b07problem.py') .. rst-class:: sphx-glr-script-out .. code-block:: none Example b07problem.py .. GENERATED FROM PYTHON SOURCE LINES 52-53 Parameters to be estimated .. GENERATED FROM PYTHON SOURCE LINES 53-66 .. code-block:: Python coef_intercept = Beta('coef_intercept', 0.0, None, None, 0) coef_age_65_more = Beta('coef_age_65_more', 0.0, None, None, 0) coef_haveGA = Beta('coef_haveGA', 0.0, None, None, 0) coef_moreThanOneCar = Beta('coef_moreThanOneCar', 0.0, None, None, 0) coef_moreThanOneBike = Beta('coef_moreThanOneBike', 0.0, None, None, 0) coef_individualHouse = Beta('coef_individualHouse', 0.0, None, None, 0) coef_male = Beta('coef_male', 0.0, None, None, 0) coef_haveChildren = Beta('coef_haveChildren', 0.0, None, None, 0) coef_highEducation = Beta('coef_highEducation', 0.0, None, None, 0) thresholds = [None, 4, 6, 8, 10, None] formula_income = piecewise_formula(variable=ScaledIncome, thresholds=thresholds) .. GENERATED FROM PYTHON SOURCE LINES 67-68 Latent variable: structural equation. .. GENERATED FROM PYTHON SOURCE LINES 68-81 .. code-block:: Python CARLOVERS = ( coef_intercept + coef_age_65_more * age_65_more + formula_income + coef_moreThanOneCar * moreThanOneCar + coef_moreThanOneBike * moreThanOneBike + coef_individualHouse * individualHouse + coef_male * male + coef_haveChildren * haveChildren + coef_haveGA * haveGA + coef_highEducation * highEducation ) .. GENERATED FROM PYTHON SOURCE LINES 82-83 Measurement equations .. GENERATED FROM PYTHON SOURCE LINES 85-86 Intercepts. .. GENERATED FROM PYTHON SOURCE LINES 86-94 .. code-block:: Python INTER_Envir01 = Beta('INTER_Envir01', 0, None, None, 1) INTER_Envir02 = Beta('INTER_Envir02', 0, None, None, 0) INTER_Envir03 = Beta('INTER_Envir03', 0, None, None, 0) INTER_Mobil11 = Beta('INTER_Mobil11', 0, None, None, 0) INTER_Mobil14 = Beta('INTER_Mobil14', 0, None, None, 0) INTER_Mobil16 = Beta('INTER_Mobil16', 0, None, None, 0) INTER_Mobil17 = Beta('INTER_Mobil17', 0, None, None, 0) .. GENERATED FROM PYTHON SOURCE LINES 95-96 Coefficients. .. GENERATED FROM PYTHON SOURCE LINES 96-104 .. code-block:: Python B_Envir01_F1 = Beta('B_Envir01_F1', -1, None, None, 1) B_Envir02_F1 = Beta('B_Envir02_F1', -1, None, None, 0) B_Envir03_F1 = Beta('B_Envir03_F1', 1, None, None, 0) B_Mobil11_F1 = Beta('B_Mobil11_F1', 1, None, None, 0) B_Mobil14_F1 = Beta('B_Mobil14_F1', 1, None, None, 0) B_Mobil16_F1 = Beta('B_Mobil16_F1', 1, None, None, 0) B_Mobil17_F1 = Beta('B_Mobil17_F1', 1, None, None, 0) .. GENERATED FROM PYTHON SOURCE LINES 105-106 Linear models. .. GENERATED FROM PYTHON SOURCE LINES 106-114 .. code-block:: Python MODEL_Envir01 = INTER_Envir01 + B_Envir01_F1 * CARLOVERS MODEL_Envir02 = INTER_Envir02 + B_Envir02_F1 * CARLOVERS MODEL_Envir03 = INTER_Envir03 + B_Envir03_F1 * CARLOVERS MODEL_Mobil11 = INTER_Mobil11 + B_Mobil11_F1 * CARLOVERS MODEL_Mobil14 = INTER_Mobil14 + B_Mobil14_F1 * CARLOVERS MODEL_Mobil16 = INTER_Mobil16 + B_Mobil16_F1 * CARLOVERS MODEL_Mobil17 = INTER_Mobil17 + B_Mobil17_F1 * CARLOVERS .. GENERATED FROM PYTHON SOURCE LINES 115-116 Scale parameters. .. GENERATED FROM PYTHON SOURCE LINES 116-124 .. code-block:: Python SIGMA_STAR_Envir01 = Beta('SIGMA_STAR_Envir01', 1, 1.0e-5, None, 1) SIGMA_STAR_Envir02 = Beta('SIGMA_STAR_Envir02', 0.01, 1.0e-5, None, 0) SIGMA_STAR_Envir03 = Beta('SIGMA_STAR_Envir03', 1, 1.0e-5, None, 0) SIGMA_STAR_Mobil11 = Beta('SIGMA_STAR_Mobil11', 1, 1.0e-5, None, 0) SIGMA_STAR_Mobil14 = Beta('SIGMA_STAR_Mobil14', 1, 1.0e-5, None, 0) SIGMA_STAR_Mobil16 = Beta('SIGMA_STAR_Mobil16', 1, 1.0e-5, None, 0) SIGMA_STAR_Mobil17 = Beta('SIGMA_STAR_Mobil17', 1, 1.0e-5, None, 0) .. GENERATED FROM PYTHON SOURCE LINES 125-126 Symmetric thresholds. .. GENERATED FROM PYTHON SOURCE LINES 126-133 .. code-block:: Python delta_1 = Beta('delta_1', 0.1, 1.0e-5, None, 0) delta_2 = Beta('delta_2', 0.2, 1.0e-5, None, 0) tau_1 = -delta_1 - delta_2 tau_2 = -delta_1 tau_3 = delta_1 tau_4 = delta_1 + delta_2 .. GENERATED FROM PYTHON SOURCE LINES 134-135 Ordered probit models. .. GENERATED FROM PYTHON SOURCE LINES 135-265 .. code-block:: Python Envir01_tau_1 = (tau_1 - MODEL_Envir01) / SIGMA_STAR_Envir01 Envir01_tau_2 = (tau_2 - MODEL_Envir01) / SIGMA_STAR_Envir01 Envir01_tau_3 = (tau_3 - MODEL_Envir01) / SIGMA_STAR_Envir01 Envir01_tau_4 = (tau_4 - MODEL_Envir01) / SIGMA_STAR_Envir01 IndEnvir01 = { 1: bioNormalCdf(Envir01_tau_1), 2: bioNormalCdf(Envir01_tau_2) - bioNormalCdf(Envir01_tau_1), 3: bioNormalCdf(Envir01_tau_3) - bioNormalCdf(Envir01_tau_2), 4: bioNormalCdf(Envir01_tau_4) - bioNormalCdf(Envir01_tau_3), 5: 1 - bioNormalCdf(Envir01_tau_4), 6: 1.0, -1: 1.0, -2: 1.0, } P_Envir01 = Elem(IndEnvir01, Envir01) Envir02_tau_1 = (tau_1 - MODEL_Envir02) / SIGMA_STAR_Envir02 Envir02_tau_2 = (tau_2 - MODEL_Envir02) / SIGMA_STAR_Envir02 Envir02_tau_3 = (tau_3 - MODEL_Envir02) / SIGMA_STAR_Envir02 Envir02_tau_4 = (tau_4 - MODEL_Envir02) / SIGMA_STAR_Envir02 IndEnvir02 = { 1: bioNormalCdf(Envir02_tau_1), 2: bioNormalCdf(Envir02_tau_2) - bioNormalCdf(Envir02_tau_1), 3: bioNormalCdf(Envir02_tau_3) - bioNormalCdf(Envir02_tau_2), 4: bioNormalCdf(Envir02_tau_4) - bioNormalCdf(Envir02_tau_3), 5: 1 - bioNormalCdf(Envir02_tau_4), 6: 1.0, -1: 1.0, -2: 1.0, } P_Envir02 = Elem(IndEnvir02, Envir02) Envir03_tau_1 = (tau_1 - MODEL_Envir03) / SIGMA_STAR_Envir03 Envir03_tau_2 = (tau_2 - MODEL_Envir03) / SIGMA_STAR_Envir03 Envir03_tau_3 = (tau_3 - MODEL_Envir03) / SIGMA_STAR_Envir03 Envir03_tau_4 = (tau_4 - MODEL_Envir03) / SIGMA_STAR_Envir03 IndEnvir03 = { 1: bioNormalCdf(Envir03_tau_1), 2: bioNormalCdf(Envir03_tau_2) - bioNormalCdf(Envir03_tau_1), 3: bioNormalCdf(Envir03_tau_3) - bioNormalCdf(Envir03_tau_2), 4: bioNormalCdf(Envir03_tau_4) - bioNormalCdf(Envir03_tau_3), 5: 1 - bioNormalCdf(Envir03_tau_4), 6: 1.0, -1: 1.0, -2: 1.0, } P_Envir03 = Elem(IndEnvir03, Envir03) Mobil11_tau_1 = (tau_1 - MODEL_Mobil11) / SIGMA_STAR_Mobil11 Mobil11_tau_2 = (tau_2 - MODEL_Mobil11) / SIGMA_STAR_Mobil11 Mobil11_tau_3 = (tau_3 - MODEL_Mobil11) / SIGMA_STAR_Mobil11 Mobil11_tau_4 = (tau_4 - MODEL_Mobil11) / SIGMA_STAR_Mobil11 IndMobil11 = { 1: bioNormalCdf(Mobil11_tau_1), 2: bioNormalCdf(Mobil11_tau_2) - bioNormalCdf(Mobil11_tau_1), 3: bioNormalCdf(Mobil11_tau_3) - bioNormalCdf(Mobil11_tau_2), 4: bioNormalCdf(Mobil11_tau_4) - bioNormalCdf(Mobil11_tau_3), 5: 1 - bioNormalCdf(Mobil11_tau_4), 6: 1.0, -1: 1.0, -2: 1.0, } P_Mobil11 = Elem(IndMobil11, Mobil11) Mobil14_tau_1 = (tau_1 - MODEL_Mobil14) / SIGMA_STAR_Mobil14 Mobil14_tau_2 = (tau_2 - MODEL_Mobil14) / SIGMA_STAR_Mobil14 Mobil14_tau_3 = (tau_3 - MODEL_Mobil14) / SIGMA_STAR_Mobil14 Mobil14_tau_4 = (tau_4 - MODEL_Mobil14) / SIGMA_STAR_Mobil14 IndMobil14 = { 1: bioNormalCdf(Mobil14_tau_1), 2: bioNormalCdf(Mobil14_tau_2) - bioNormalCdf(Mobil14_tau_1), 3: bioNormalCdf(Mobil14_tau_3) - bioNormalCdf(Mobil14_tau_2), 4: bioNormalCdf(Mobil14_tau_4) - bioNormalCdf(Mobil14_tau_3), 5: 1 - bioNormalCdf(Mobil14_tau_4), 6: 1.0, -1: 1.0, -2: 1.0, } P_Mobil14 = Elem(IndMobil14, Mobil14) Mobil16_tau_1 = (tau_1 - MODEL_Mobil16) / SIGMA_STAR_Mobil16 Mobil16_tau_2 = (tau_2 - MODEL_Mobil16) / SIGMA_STAR_Mobil16 Mobil16_tau_3 = (tau_3 - MODEL_Mobil16) / SIGMA_STAR_Mobil16 Mobil16_tau_4 = (tau_4 - MODEL_Mobil16) / SIGMA_STAR_Mobil16 IndMobil16 = { 1: bioNormalCdf(Mobil16_tau_1), 2: bioNormalCdf(Mobil16_tau_2) - bioNormalCdf(Mobil16_tau_1), 3: bioNormalCdf(Mobil16_tau_3) - bioNormalCdf(Mobil16_tau_2), 4: bioNormalCdf(Mobil16_tau_4) - bioNormalCdf(Mobil16_tau_3), 5: 1 - bioNormalCdf(Mobil16_tau_4), 6: 1.0, -1: 1.0, -2: 1.0, } P_Mobil16 = Elem(IndMobil16, Mobil16) Mobil17_tau_1 = (tau_1 - MODEL_Mobil17) / SIGMA_STAR_Mobil17 Mobil17_tau_2 = (tau_2 - MODEL_Mobil17) / SIGMA_STAR_Mobil17 Mobil17_tau_3 = (tau_3 - MODEL_Mobil17) / SIGMA_STAR_Mobil17 Mobil17_tau_4 = (tau_4 - MODEL_Mobil17) / SIGMA_STAR_Mobil17 IndMobil17 = { 1: bioNormalCdf(Mobil17_tau_1), 2: bioNormalCdf(Mobil17_tau_2) - bioNormalCdf(Mobil17_tau_1), 3: bioNormalCdf(Mobil17_tau_3) - bioNormalCdf(Mobil17_tau_2), 4: bioNormalCdf(Mobil17_tau_4) - bioNormalCdf(Mobil17_tau_3), 5: 1 - bioNormalCdf(Mobil17_tau_4), 6: 1.0, -1: 1.0, -2: 1.0, } P_Mobil17 = Elem(IndMobil17, Mobil17) loglike = ( log(P_Envir01) + log(P_Envir02) + log(P_Envir03) + log(P_Mobil11) + log(P_Mobil14) + log(P_Mobil16) + log(P_Mobil17) ) .. GENERATED FROM PYTHON SOURCE LINES 266-267 Read the data .. GENERATED FROM PYTHON SOURCE LINES 267-269 .. code-block:: Python database = read_data() .. GENERATED FROM PYTHON SOURCE LINES 270-271 Create the Biogeme object .. GENERATED FROM PYTHON SOURCE LINES 271-274 .. code-block:: Python the_biogeme = bio.BIOGEME(database, loglike) the_biogeme.modelName = 'b07problem' .. rst-class:: sphx-glr-script-out .. code-block:: none Biogeme parameters read from biogeme.toml. .. GENERATED FROM PYTHON SOURCE LINES 275-276 Estimate the parameters. As the estimation will fail, we are catching the exception .. GENERATED FROM PYTHON SOURCE LINES 276-282 .. code-block:: Python try: results = the_biogeme.estimate() except ValueError as e: print( 'Impossible to estimate the model. There must be an issue with the initial values of the parameters.' ) .. rst-class:: sphx-glr-script-out .. code-block:: none As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" *** Initial values of the parameters are obtained from the file __b07problem.iter Cannot read file __b07problem.iter. Statement is ignored. As the model is not too complex, we activate the calculation of second derivatives. If you want to change it, change the name of the algorithm in the TOML file from "automatic" to "simple_bounds" Optimization algorithm: hybrid Newton/BFGS with simple bounds [simple_bounds] ** Optimization: Newton with trust region for simple bounds The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan Iter. Function Relgrad Radius Rho 0 inf 1.8e+308 0.5 0 - 1 inf 1.8e+308 0.25 0 - 2 inf 1.8e+308 0.12 0 - 3 inf 1.8e+308 0.062 0 - 4 inf 1.8e+308 0.031 0 - 5 inf 1.8e+308 0.016 0 - 6 inf 1.8e+308 0.0078 0 - 7 inf 1.8e+308 0.0039 0 - 8 inf 1.8e+308 0.002 0 - 9 inf 1.8e+308 0.00098 0 - 10 inf 1.8e+308 0.00049 0 - 11 inf 1.8e+308 0.00024 0 - 12 inf 1.8e+308 0.00012 0 - 13 inf 1.8e+308 6.1e-05 0 - 14 inf 1.8e+308 3.1e-05 0 - 15 inf 1.8e+308 1.5e-05 0 - 16 inf 1.8e+308 7.6e-06 0 - 17 inf 1.8e+308 3.8e-06 0 - 18 inf 1.8e+308 1.9e-06 0 - 19 inf 1.8e+308 9.5e-07 0 - 20 inf 1.8e+308 4.8e-07 0 - 21 inf 1.8e+308 2.4e-07 0 - 22 inf 1.8e+308 1.2e-07 0 - 23 inf 1.8e+308 6e-08 0 - 24 inf 1.8e+308 3e-08 0 - 25 inf 1.8e+308 1.5e-08 0 - The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan Numerical problems in calculating the analytical hessian. Finite differences is tried instead. The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan /Users/bierlair/venv312/lib/python3.12/site-packages/biogeme/tools/derivatives.py:92: RuntimeWarning: invalid value encountered in subtract h[:, i] = (gp - g).flatten() / s The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=1e-07, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=1e-07, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=1e-07, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=1e-07, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=1e-07, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=1e-07, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=inf, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan The norm of the gradient at B_Envir02_F1=-1, B_Envir03_F1=1, B_Mobil11_F1=1, B_Mobil14_F1=1, B_Mobil16_F1=1, B_Mobil17_F1=1, INTER_Envir02=0, INTER_Envir03=0, INTER_Mobil11=0, INTER_Mobil14=0, INTER_Mobil16=0, INTER_Mobil17=0, SIGMA_STAR_Envir02=0.01, SIGMA_STAR_Envir03=1, SIGMA_STAR_Mobil11=1 is nan: g=nan, nan, nan, nan, nan, nan, inf, nan, nan, nan, nan, nan, inf, nan, nan Numerical problems with finite difference hessian as well. Impossible to estimate the model. There must be an issue with the initial values of the parameters. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 1.902 seconds) .. _sphx_glr_download_auto_examples_latent_plot_b07problem.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_b07problem.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_b07problem.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_b07problem.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_