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Sun Dec 18 15:28:23 2011
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 Multinomial Logit |
Number of draws: | 5 |
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: | -5322.913 |
Likelihood ratio test: | 3283.500 |
Rho-square: | 0.236 |
Adjusted rho-square: | 0.235 |
Final gradient norm: | +3.450e-04 |
Diagnostic: | Normal termination. Obj: 6.05545e-06 Const: 6.05545e-06 |
Iterations: | 15 |
Run time: | 00:02 |
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 | -0.130 | 0.0442 | -2.94 | 0.00 | 0.0541 | -2.40 | 0.02 | ||
ASC_SM | 0.00 | fixed | |||||||
ASC_TRAIN | -0.675 | 0.0558 | -12.10 | 0.00 | 0.0753 | -8.96 | 0.00 | ||
B_COST | -1.10 | 0.0530 | -20.78 | 0.00 | 0.0699 | -15.76 | 0.00 | ||
B_TIME | 0.256 | 0.0466 | 5.49 | 0.00 | 0.0779 | 3.29 | 0.00 | ||
B_TIME_S | -0.333 | 0.0616 | -5.41 | 0.00 | 0.130 | -2.56 | 0.01 |
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 | 0.111 | 0.0410 | 2.71 |
Coefficient1 | Coefficient2 | Covariance | Correlation | t-test | p-value | Rob. cov. | Rob. corr. | Rob. t-test | p-value | ||
---|---|---|---|---|---|---|---|---|---|---|---|
ASC_CAR | B_TIME_S | -0.000181 | -0.0665 | 2.60 | 0.01 | 0.000608 | 0.0864 | 1.49 | 0.14 | * | |
ASC_TRAIN | B_TIME_S | -1.65e-05 | -0.00480 | -4.10 | 0.00 | 0.00235 | 0.240 | -2.55 | 0.01 | ||
ASC_TRAIN | B_COST | -2.24e-05 | -0.00758 | 5.52 | 0.00 | -0.000465 | -0.0884 | 3.98 | 0.00 | ||
B_TIME | B_TIME_S | 0.000367 | 0.128 | 8.15 | 0.00 | 0.00427 | 0.421 | 4.90 | 0.00 | ||
ASC_CAR | B_TIME | 0.00120 | 0.581 | -9.26 | 0.00 | 0.00301 | 0.714 | -7.07 | 0.00 | ||
B_COST | B_TIME_S | 0.000216 | 0.0663 | -9.78 | 0.00 | 0.000395 | 0.0434 | -5.30 | 0.00 | ||
ASC_CAR | ASC_TRAIN | 0.00146 | 0.592 | 11.77 | 0.00 | 0.00313 | 0.769 | 11.28 | 0.00 | ||
ASC_CAR | B_COST | 0.000453 | 0.193 | 15.65 | 0.00 | 0.000228 | 0.0602 | 11.33 | 0.00 | ||
B_COST | B_TIME | -0.000461 | -0.186 | -17.67 | 0.00 | -0.00133 | -0.244 | -11.64 | 0.00 | ||
ASC_TRAIN | B_TIME | 0.00187 | 0.720 | -23.73 | 0.00 | 0.00496 | 0.845 | -21.78 | 0.00 |
Smallest singular value of the hessian: 5.96458