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Tue Apr 18 19:51:22 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 log 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. |
| The analytical derivatives are explicitly provided by the user. |
| 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: | -5836.619 |
| Final log likelihood: | -5231.453 |
| Likelihood ratio test: | 3466.421 |
| Rho-square: | 0.249 |
| Adjusted rho-square: | 0.248 |
| Final gradient norm: | +5.921e-04 |
| Diagnostic: | Normal termination. Obj: 6.05545e-06 Const: 6.05545e-06 |
| Iterations: | 17 |
| Run time: | 02:44 |
| 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 | 0.174 | 0.0582 | 2.99 | 0.00 | 0.0629 | 2.77 | 0.01 | ||
| ASC_SM | 0.00 | fixed | |||||||
| ASC_TRAIN | -0.346 | 0.0676 | -5.12 | 0.00 | 0.0733 | -4.72 | 0.00 | ||
| B_COST | -1.38 | 0.0746 | -18.51 | 0.00 | 0.0978 | -14.10 | 0.00 | ||
| B_TIME | 0.575 | 0.0653 | 8.81 | 0.00 | 0.0713 | 8.07 | 0.00 | ||
| B_TIME_S | 1.24 | 0.104 | 11.93 | 0.00 | 0.135 | 9.18 | 0.00 |
| Id | Name | Availability | Specification |
|---|---|---|---|
| 1 | A1_TRAIN | TRAIN_AV_SP | ASC_TRAIN * one + B_COST * TRAIN_COST_SCALED + -(exp(B_TIME [ B_TIME_S ] )) * TRAIN_TT_SCALED |
| 2 | A2_SM | SM_AV | ASC_SM * one + B_COST * SM_COST_SCALED + -(exp(B_TIME [ B_TIME_S ] )) * SM_TT_SCALED |
| 3 | A3_Car | CAR_AV_SP | ASC_CAR * one + B_COST * CAR_CO_SCALED + -(exp(B_TIME [ B_TIME_S ] )) * CAR_TT_SCALED |
| Name | Value | Std err | t-test | Robust Std err | Robust t-test |
|---|---|---|---|---|---|
| B_TIME_B_TIME_S | 1.53 | 0.257 | 5.96 |
| Coefficient1 | Coefficient2 | Covariance | Correlation | t-test | p-value | Rob. cov. | Rob. corr. | Rob. t-test | p-value | ||
|---|---|---|---|---|---|---|---|---|---|---|---|
| B_TIME | B_TIME_S | 0.000636 | 0.0938 | -5.65 | 0.00 | 0.000451 | 0.0468 | -4.43 | 0.00 | ||
| ASC_CAR | B_TIME | 0.00262 | 0.688 | -8.14 | 0.00 | 0.00314 | 0.700 | -7.63 | 0.00 | ||
| ASC_TRAIN | B_COST | -0.000695 | -0.138 | 9.63 | 0.00 | -0.00131 | -0.183 | 7.80 | 0.00 | ||
| ASC_CAR | B_TIME_S | 0.00157 | 0.259 | -10.13 | 0.00 | 0.00250 | 0.294 | -8.12 | 0.00 | ||
| ASC_TRAIN | B_TIME_S | 0.000476 | 0.0677 | -13.21 | 0.00 | 0.000264 | 0.0267 | -10.43 | 0.00 | ||
| ASC_CAR | ASC_TRAIN | 0.00277 | 0.703 | 10.56 | 0.00 | 0.00347 | 0.752 | 10.63 | 0.00 | ||
| ASC_CAR | B_COST | -0.000378 | -0.0872 | 15.77 | 0.00 | -0.00109 | -0.177 | 12.40 | 0.00 | ||
| B_COST | B_TIME_S | -0.00273 | -0.352 | -17.74 | 0.00 | -0.00472 | -0.357 | -13.57 | 0.00 | ||
| B_COST | B_TIME | -0.00156 | -0.321 | -17.18 | 0.00 | -0.00282 | -0.404 | -13.72 | 0.00 | ||
| ASC_TRAIN | B_TIME | 0.00342 | 0.773 | -20.54 | 0.00 | 0.00419 | 0.801 | -20.20 | 0.00 |
Smallest singular value of the hessian: 5.86902