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Thu Jul 7 17:54:18 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 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: | -5231.175 |
| Likelihood ratio test: | 3466.976 |
| Rho-square: | 0.249 |
| Adjusted rho-square: | 0.248 |
| Final gradient norm: | +6.639e-04 |
| Diagnostic: | Normal termination. Obj: 6.05545e-06 Const: 6.05545e-06 |
| Iterations: | 18 |
| Run time: | 02:46 |
| 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 | 0.175 | 0.0580 | 3.02 | 0.00 | 0.0623 | 2.81 | 0.00 | ||
| ASC_SM | 0.00 | fixed | |||||||
| ASC_TRAIN | -0.345 | 0.0676 | -5.10 | 0.00 | 0.0733 | -4.71 | 0.00 | ||
| B_COST | -1.38 | 0.0747 | -18.51 | 0.00 | 0.0978 | -14.13 | 0.00 | ||
| B_TIME | 0.577 | 0.0652 | 8.84 | 0.00 | 0.0712 | 8.10 | 0.00 | ||
| B_TIME_S | 1.24 | 0.100 | 12.34 | 0.00 | 0.126 | 9.81 | 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 | 1.54 | 0.249 | 6.17 |
| Coefficient1 | Coefficient2 | Covariance | Correlation | t-test | p-value | Rob. cov. | Rob. corr. | Rob. t-test | p-value | ||
|---|---|---|---|---|---|---|---|---|---|---|---|
| B_TIME | B_TIME_S | 0.000529 | 0.0807 | -5.75 | 0.00 | 0.000219 | 0.0244 | -4.62 | 0.00 | ||
| ASC_CAR | B_TIME | 0.00260 | 0.687 | -8.16 | 0.00 | 0.00310 | 0.699 | -7.66 | 0.00 | ||
| ASC_TRAIN | B_COST | -0.000685 | -0.136 | 9.66 | 0.00 | -0.00128 | -0.179 | 7.84 | 0.00 | ||
| ASC_CAR | B_TIME_S | 0.00142 | 0.243 | -10.32 | 0.00 | 0.00208 | 0.264 | -8.49 | 0.00 | ||
| ASC_CAR | ASC_TRAIN | 0.00276 | 0.703 | 10.56 | 0.00 | 0.00344 | 0.753 | 10.67 | 0.00 | ||
| ASC_TRAIN | B_TIME_S | 0.000381 | 0.0560 | -13.44 | 0.00 | 6.06e-05 | 0.00654 | -10.88 | 0.00 | ||
| ASC_CAR | B_COST | -0.000357 | -0.0825 | 15.84 | 0.00 | -0.00102 | -0.167 | 12.52 | 0.00 | ||
| B_COST | B_TIME | -0.00156 | -0.320 | -17.21 | 0.00 | -0.00280 | -0.402 | -13.77 | 0.00 | ||
| B_COST | B_TIME_S | -0.00261 | -0.347 | -18.14 | 0.00 | -0.00427 | -0.346 | -14.20 | 0.00 | ||
| ASC_TRAIN | B_TIME | 0.00341 | 0.772 | -20.55 | 0.00 | 0.00418 | 0.801 | -20.20 | 0.00 |
Smallest singular value of the hessian: 5.86836