Biogeme examples for the Swissmetro data

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

sphx_glr_auto_examples_swissmetro_generate_jed_run.py

Generate SLURM run scripts for Biogeme experiments.

1a. Estimation of a logit model

1a. Estimation of a logit model

1b. Illustration of additional features of Biogeme

1b. Illustration of additional features of Biogeme

1c. Illustration of the quick_estimate of Biogeme

1c. Illustration of the quick_estimate of Biogeme

1d. Simulation of a logit model

1d. Simulation of a logit model

1e. Logit model with several algorithms

1e. Logit model with several algorithms

2. Estimation with weights: WESML

2. Estimation with weights: WESML

3. Moneymetric and heteroscedastic specification

3. Moneymetric and heteroscedastic specification

4. Out-of-sample validation

4. Out-of-sample validation

5a. Mixture of logit models with Monte-Carlo integration

5a. Mixture of logit models with Monte-Carlo integration

5b. Mixture of logit models with numerical integration

5b. Mixture of logit models with numerical integration

5c. Simulation of a mixture model

5c. Simulation of a mixture model

Mixture of logit

Mixture of logit

6a. Mixture of logit models with uniform distribution

6a. Mixture of logit models with uniform distribution

6b. Mixture of logit models with uniform MLHS draws

6b. Mixture of logit models with uniform MLHS draws

sphx_glr_auto_examples_swissmetro_plot_b06c_unif_mixture_integral.py

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7. Latent class model

7. Latent class model

8. Box-Cox transforms

8. Box-Cox transforms

9. Nested logit model

9. Nested logit model

10. Nested logit model normalized from bottom

10. Nested logit model normalized from bottom

11a. Cross-nested logit

11a. Cross-nested logit

11b. Simulation of a cross-nested logit model

11b. Simulation of a cross-nested logit model

11c. Cross-nested logit with a sparse structure

11c. Cross-nested logit with a sparse structure

12. Mixture of logit with panel data

12. Mixture of logit with panel data

13. Simulation of panel model

13. Simulation of panel model

14. Nested logit with corrections for endogeneous sampling

14. Nested logit with corrections for endogeneous sampling

15a. Discrete mixture with panel data

15a. Discrete mixture with panel data

15b. Discrete mixture with panel data

15b. Discrete mixture with panel data

16. Discrete mixture with panel data

16. Discrete mixture with panel data

17a. Mixture with lognormal distribution

17a. Mixture with lognormal distribution

17b. Mixture with lognormal distribution and numerical integration

17b. Mixture with lognormal distribution and numerical integration

18a. Ordinal logit model

18a. Ordinal logit model

18b. Ordinal probit model

18b. Ordinal probit model

19. Calculation of individual level parameters

19. Calculation of individual level parameters

20. Estimation of several models

20. Estimation of several models

21a. Assisted specification

21a. Assisted specification

21b. Specification of a catalog of models

21b. Specification of a catalog of models

21c. Re-estimate the Pareto optimal models

21c. Re-estimate the Pareto optimal models

Assisted specification

Assisted specification

Specification of a catalog of models

Specification of a catalog of models

Re-estimate the Pareto optimal models

Re-estimate the Pareto optimal models

23a. Binary logit model

23a. Binary logit model

23b. Binary probit model

23b. Binary probit model

24. Mixture of logit with Halton draws

24. Mixture of logit with Halton draws

25. Triangular mixture of logit

25. Triangular mixture of logit

26. Triangular mixture with panel data

26. Triangular mixture with panel data

Data preparation for Swissmetro (binary choice)

Data preparation for Swissmetro (binary choice)

Data preparation for Swissmetro

Data preparation for Swissmetro

Panel data preparation for Swissmetro

Panel data preparation for Swissmetro