This file has automatically been generated.
Tue Apr 18 19:03:13 2017
Tip: click on the columns headers to sort a table [Credits]
| Example of a logit model for a transportation mode choice with 3 alternatives: |
| - Train |
| - Car |
| - Swissmetro, an hypothetical high-speed train |
| The time coefficient is normally distributed. This is an example of a mixture of logit model. |
| The syntax for a distributed coefficient is B_TIME [ B_TIME_S ], where |
| B_TIME is the mean and B_TIME_S squared is the variance. |
| The square brackets are associated with a normal distribution. |
| Model: | Mixed Logit |
| Number of Hess-Train draws: | 500 |
| 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: | -5214.787 |
| Likelihood ratio test: | 3499.751 |
| Rho-square: | 0.251 |
| Adjusted rho-square: | 0.251 |
| Final gradient norm: | +8.813e-04 |
| Diagnostic: | Normal termination. Obj: 6.05545e-06 Const: 6.05545e-06 |
| Iterations: | 18 |
| Run time: | 01:16 |
| Variance-covariance: | from finite difference hessian |
| Sample file: | ../swissmetro.dat |
| Name | Value | Std err | t-test | p-value | Robust Std err | Robust t-test | p-value | ||
|---|---|---|---|---|---|---|---|---|---|
| ASC_CAR | 0.137 | 0.0516 | 2.66 | 0.01 | 0.0517 | 2.65 | 0.01 | ||
| ASC_SM | 0.00 | fixed | |||||||
| ASC_TRAIN | -0.402 | 0.0635 | -6.33 | 0.00 | 0.0659 | -6.10 | 0.00 | ||
| B_COST | -1.29 | 0.0631 | -20.38 | 0.00 | 0.0863 | -14.89 | 0.00 | ||
| B_TIME | -2.26 | 0.119 | -18.98 | 0.00 | 0.117 | -19.29 | 0.00 | ||
| B_TIME_S | 1.66 | 0.139 | 11.98 | 0.00 | 0.132 | 12.57 | 0.00 |
| Id | Name | Availability | Specification |
|---|---|---|---|
| 1 | A1_TRAIN | TRAIN_AV_SP | ASC_TRAIN * one + B_TIME [ B_TIME_S ] * TRAIN_TT_SCALED + B_COST * TRAIN_COST_SCALED |
| 2 | A2_SM | SM_AV | ASC_SM * one + B_TIME [ B_TIME_S ] * SM_TT_SCALED + B_COST * SM_COST_SCALED |
| 3 | A3_Car | CAR_AV_SP | ASC_CAR * one + B_TIME [ B_TIME_S ] * CAR_TT_SCALED + B_COST * CAR_CO_SCALED |
| Name | Value | Std err | t-test | Robust Std err | Robust t-test |
|---|---|---|---|---|---|
| B_TIME_B_TIME_S | 2.75 | 0.460 | 5.99 |
| Coefficient1 | Coefficient2 | Covariance | Correlation | t-test | p-value | Rob. cov. | Rob. corr. | Rob. t-test | p-value | ||
|---|---|---|---|---|---|---|---|---|---|---|---|
| ASC_TRAIN | B_COST | -0.000137 | -0.0343 | 9.71 | 0.00 | -0.000303 | -0.0533 | 7.94 | 0.00 | ||
| B_COST | B_TIME | 0.00239 | 0.318 | 8.43 | 0.00 | 0.00373 | 0.369 | 8.33 | 0.00 | ||
| ASC_CAR | ASC_TRAIN | 0.00204 | 0.623 | 10.56 | 0.00 | 0.00223 | 0.655 | 10.67 | 0.00 | ||
| ASC_TRAIN | B_TIME | -0.00456 | -0.603 | 11.24 | 0.00 | -0.00470 | -0.609 | 11.21 | 0.00 | ||
| ASC_CAR | B_TIME_S | 0.00298 | 0.417 | -12.07 | 0.00 | 0.00285 | 0.417 | -12.68 | 0.00 | ||
| ASC_CAR | B_COST | 0.000182 | 0.0558 | 17.95 | 0.00 | 0.000128 | 0.0286 | 14.32 | 0.00 | ||
| ASC_TRAIN | B_TIME_S | 0.00182 | 0.207 | -14.73 | 0.00 | 0.00146 | 0.168 | -15.01 | 0.00 | ||
| ASC_CAR | B_TIME | -0.00395 | -0.642 | 15.24 | 0.00 | -0.00387 | -0.638 | 15.43 | 0.00 | ||
| B_COST | B_TIME_S | -0.00259 | -0.297 | -17.49 | 0.00 | -0.00322 | -0.283 | -16.64 | 0.00 | ||
| B_TIME | B_TIME_S | -0.0128 | -0.774 | -16.15 | 0.00 | -0.0115 | -0.741 | -16.85 | 0.00 |
Smallest singular value of the hessian: 5.55496