biogeme 2.6a [Mon Apr 17 15:32:48 CEST 2017]
Michel Bierlaire, EPFL
This file has automatically been generated.
Tue Apr 18 19:51:27 2017
Tip: click on the columns headers to sort a table [Credits]
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Example of a Network GEV model. It is actually a nested logit model for a transportation mode choice with 3 alternatives:
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- Train
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- Car
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- Swissmetro, an hypothetical high-speed train
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Alternatives Train and Car are grouped in the same nest, as their error terms are expected to share unobserved attributes associated with existing alternatives. It is the same model as in file 09nested.mod
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| Model: | Network GEV model |
| Number of estimated parameters: | 5 |
| Number of observations: | 6768 |
| Number of individuals: | 6768 |
| Null log likelihood: | -6964.663 |
| Init log likelihood: | -6964.663 |
| Final log likelihood: | -5236.900 |
| Likelihood ratio test: | 3455.526 |
| Rho-square: | 0.248 |
| Adjusted rho-square: | 0.247 |
| Final gradient norm: | +2.505e-04 |
| Diagnostic: | Normal termination. Obj: 6.05545e-06 Const: 6.05545e-06 |
| Iterations: | 15 |
| Run time: | 00:03 |
| Variance-covariance: | from finite difference hessian |
| Sample file: | ../swissmetro.dat |
Utility parameters
| Name | Value | Std err | t-test | p-value | | Robust Std err | Robust t-test | p-value | |
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| ASC_CAR | -0.167 | 0.0371 | -4.50 | 0.00 | | 0.0545 | -3.07 | 0.00 | |
| ASC_SM | 0.00 | fixed | | | | | | | |
| ASC_TRAIN | -0.512 | 0.0452 | -11.33 | 0.00 | | 0.0791 | -6.47 | 0.00 | |
| B_COST | -0.857 | 0.0463 | -18.51 | 0.00 | | 0.0600 | -14.27 | 0.00 | |
| B_TIME | -0.899 | 0.0570 | -15.77 | 0.00 | | 0.107 | -8.39 | 0.00 | |
Model parameters
| Name | Value | Std err | t-test 0 | p-value | t-test 1 | p-value | | Robust Std err | Robust t-test 0 | p-value | Robust t-test 1 | p-value | |
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| A1_TRAIN | 1.00 | fixed | | | | | | | | | | | |
| A2_SM | 1.00 | fixed | | | | | | | | | | | |
| A3_Car | 1.00 | fixed | | | | | | | | | | | |
| EXISTING | 2.05 | 0.118 | 17.45 | 0.00 | 8.96 | 0.00 | | 0.164 | 12.51 | 0.00 | 6.42 | 0.00 | |
| FUTURE | 1.00 | fixed | | | | | | | | | | | |
| EXISTING_A1_TRAIN | 1.00 | fixed | | | | | | | | | | | |
| EXISTING_A3_Car | 1.00 | fixed | | | | | | | | | | | |
| FUTURE_A2_SM | 1.00 | fixed | | | | | | | | | | | |
| __ROOT_EXISTING | 1.00 | fixed | | | | | | | | | | | |
| __ROOT_FUTURE | 1.00 | fixed | | | | | | | | | | | |
Utility functions
| Id | Name | Availability | Specification |
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| 1 | A1_TRAIN | TRAIN_AV_SP | ASC_TRAIN * one + B_TIME * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED |
| 2 | A2_SM | SM_AV | ASC_SM * one + B_TIME * SM_TT_SCALED + B_COST * SM_COST_SCALED |
| 3 | A3_Car | CAR_AV_SP | ASC_CAR * one + B_TIME * CAR_TT_SCALED + B_COST * CAR_CO_SCALED |
Correlation of coefficients
| Coefficient1 | Coefficient2 | Covariance | Correlation | t-test | p-value | | Rob. cov. | Rob. corr. | Rob. t-test | p-value | |
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B_COST | B_TIME | 0.00109 | 0.411 | 0.74 | 0.46 | * | 0.00268 | 0.417 | 0.43 | 0.67 | * |
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ASC_TRAIN | B_TIME | -0.00121 | -0.471 | 4.40 | 0.00 | | -0.00656 | -0.774 | 2.20 | 0.03 | |
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ASC_TRAIN | B_COST | 0.000322 | 0.154 | 5.79 | 0.00 | | -0.000263 | -0.0554 | 3.38 | 0.00 | |
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ASC_CAR | B_TIME | -0.00124 | -0.585 | 8.68 | 0.00 | | -0.00483 | -0.828 | 4.71 | 0.00 | |
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ASC_CAR | ASC_TRAIN | 0.00121 | 0.721 | 10.90 | 0.00 | | 0.00368 | 0.852 | 7.95 | 0.00 | |
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ASC_CAR | B_COST | 5.56e-05 | 0.0324 | 11.81 | 0.00 | | -0.000412 | -0.126 | 8.01 | 0.00 | |
Smallest singular value of the hessian: 2.0462