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Wed Jul 6 20:04:40 2016
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: | -5214.843 |
| Likelihood ratio test: | 3499.640 |
| Rho-square: | 0.251 |
| Adjusted rho-square: | 0.251 |
| Final gradient norm: | +8.940e-05 |
| Diagnostic: | Normal termination. Obj: 6.05545e-06 Const: 6.05545e-06 |
| Iterations: | 19 |
| Run time: | 01:06 |
| 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 | 0.0518 | 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 | 0.0630 | -20.39 | 0.00 | 0.0863 | -14.90 | 0.00 | ||
| B_TIME | -2.26 | 0.119 | -18.96 | 0.00 | 0.117 | -19.27 | 0.00 | ||
| B_TIME_S | -1.66 | 0.139 | -11.96 | 0.00 | 0.132 | -12.54 | 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 | 5.98 |
| Coefficient1 | Coefficient2 | Covariance | Correlation | t-test | p-value | Rob. cov. | Rob. corr. | Rob. t-test | p-value | ||
|---|---|---|---|---|---|---|---|---|---|---|---|
| B_COST | B_TIME_S | 0.00259 | 0.296 | 2.79 | 0.01 | 0.00320 | 0.280 | 2.75 | 0.01 | ||
| B_TIME | B_TIME_S | 0.0128 | 0.774 | -6.79 | 0.00 | 0.0115 | 0.742 | -6.62 | 0.00 | ||
| ASC_TRAIN | B_COST | -0.000137 | -0.0343 | 9.71 | 0.00 | -0.000300 | -0.0528 | 7.94 | 0.00 | ||
| ASC_TRAIN | B_TIME_S | -0.00182 | -0.207 | 7.66 | 0.00 | -0.00146 | -0.168 | 7.99 | 0.00 | ||
| B_COST | B_TIME | 0.00238 | 0.317 | 8.42 | 0.00 | 0.00371 | 0.367 | 8.31 | 0.00 | ||
| ASC_CAR | ASC_TRAIN | 0.00204 | 0.623 | 10.56 | 0.00 | 0.00223 | 0.655 | 10.67 | 0.00 | ||
| ASC_CAR | B_TIME_S | -0.00299 | -0.417 | 10.76 | 0.00 | -0.00286 | -0.418 | 11.16 | 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_COST | 0.000182 | 0.0559 | 17.95 | 0.00 | 0.000131 | 0.0294 | 14.33 | 0.00 | ||
| ASC_CAR | B_TIME | -0.00395 | -0.642 | 15.23 | 0.00 | -0.00388 | -0.638 | 15.42 | 0.00 |
Smallest singular value of the hessian: 5.55505