biogeme 3.2.8 [2021-07-26]
Python package
Home page: http://biogeme.epfl.ch
Submit questions to https://groups.google.com/d/forum/biogeme
Michel Bierlaire, Transport and Mobility Laboratory, Ecole Polytechnique Fédérale de Lausanne (EPFL)
This file has automatically been generated on 2021-07-26 21:50:13.354513
Report file: | 13panelNormalized.html |
Database name: | swissmetro |
Number of estimated parameters: | 7 |
Sample size: | 752 |
Observations: | 6768 |
Excluded observations: | 3960 |
Init log likelihood: | -3624.332 |
Final log likelihood: | -3570.304 |
Likelihood ratio test for the init. model: | 108.0559 |
Rho-square for the init. model: | 0.0149 |
Rho-square-bar for the init. model: | 0.013 |
Akaike Information Criterion: | 7154.608 |
Bayesian Information Criterion: | 7186.967 |
Final gradient norm: | 3.8479E-03 |
Number of draws: | 100000 |
Draws generation time: | 0:02:06.837103 |
Types of draws: | ['ASC_CAR_RND: NORMAL_ANTI', 'ASC_TRAIN_RND: NORMAL_ANTI', 'B_TIME_RND: NORMAL_ANTI'] |
Nbr of threads: | 36 |
Algorithm: | Newton with trust region for simple bound constraints |
Proportion analytical hessian: | 100.0% |
Relative projected gradient: | 3.52328e-06 |
Relative change: | 0.0012882856706175807 |
Number of iterations: | 17 |
Number of function evaluations: | 40 |
Number of gradient evaluations: | 12 |
Number of hessian evaluations: | 12 |
Cause of termination: | Relative gradient = 3.5e-06 <= 6.1e-06 |
Optimization time: | 2:56:05.015334 |
Name | Value | Std err | t-test | p-value | Rob. Std err | Rob. t-test | Rob. p-value |
---|---|---|---|---|---|---|---|
ASC_CAR | 0.421 | 0.207 | 2.03 | 0.0421 | 0.223 | 1.89 | 0.0587 |
ASC_CAR_S | 4.03 | 0.234 | 17.2 | 0 | 0.27 | 14.9 | 0 |
ASC_TRAIN | -0.46 | 0.217 | -2.12 | 0.0337 | 0.229 | -2.01 | 0.0445 |
ASC_TRAIN_S | 2.59 | 0.234 | 11.1 | 0 | 0.243 | 10.6 | 0 |
B_COST | -3.78 | 0.205 | -18.4 | 0 | 0.274 | -13.8 | 0 |
B_TIME | -6.14 | 0.309 | -19.9 | 0 | 0.348 | -17.6 | 0 |
B_TIME_S | 3.78 | 0.332 | 11.4 | 0 | 0.384 | 9.85 | 0 |
Coefficient1 | Coefficient2 | Covariance | Correlation | t-test | p-value | Rob. cov. | Rob. corr. | Rob. t-test | Rob. p-value |
---|---|---|---|---|---|---|---|---|---|
ASC_CAR_S | ASC_CAR | -0.002 | -0.0413 | 11.3 | 0 | -0.00764 | -0.127 | 9.72 | 0 |
ASC_TRAIN | ASC_CAR | 0.0101 | 0.226 | -3.34 | 0.000837 | 0.0164 | 0.321 | -3.35 | 0.000819 |
ASC_TRAIN | ASC_CAR_S | 0.00646 | 0.128 | -15.1 | 0 | 0.0143 | 0.231 | -14.4 | 0 |
ASC_TRAIN_S | ASC_CAR | -0.00147 | -0.0304 | 6.84 | 8.09e-12 | -0.0055 | -0.101 | 6.26 | 3.75e-10 |
ASC_TRAIN_S | ASC_CAR_S | 0.000786 | 0.0144 | -4.39 | 1.15e-05 | -0.00373 | -0.0568 | -3.86 | 0.000114 |
ASC_TRAIN_S | ASC_TRAIN | -0.0243 | -0.479 | 7.87 | 3.55e-15 | -0.0295 | -0.529 | 7.38 | 1.57e-13 |
B_COST | ASC_CAR | 0.00551 | 0.13 | -15.4 | 0 | 0.0086 | 0.141 | -12.8 | 0 |
B_COST | ASC_CAR_S | -0.0318 | -0.663 | -19.5 | 0 | -0.0192 | -0.259 | -18.1 | 0 |
B_COST | ASC_TRAIN | -0.0029 | -0.0653 | -10.8 | 0 | -0.00186 | -0.0295 | -9.15 | 0 |
B_COST | ASC_TRAIN_S | -0.00169 | -0.0351 | -20.1 | 0 | -0.00398 | -0.0596 | -16.9 | 0 |
B_TIME | ASC_CAR | -0.0161 | -0.251 | -15.9 | 0 | -0.0268 | -0.345 | -13.9 | 0 |
B_TIME | ASC_CAR_S | -0.0356 | -0.491 | -21.6 | 0 | -0.0486 | -0.517 | -18.8 | 0 |
B_TIME | ASC_TRAIN | -0.0271 | -0.404 | -12.8 | 0 | -0.0372 | -0.465 | -11.4 | 0 |
B_TIME | ASC_TRAIN_S | -0.00137 | -0.0189 | -22.3 | 0 | 0.0057 | 0.0673 | -21.2 | 0 |
B_TIME | B_COST | 0.0291 | 0.458 | -8.37 | 0 | 0.0381 | 0.399 | -6.81 | 9.54e-12 |
B_TIME_S | ASC_CAR | 0.00819 | 0.119 | 9.1 | 0 | 0.0199 | 0.233 | 8.48 | 0 |
B_TIME_S | ASC_CAR_S | 0.0248 | 0.32 | -0.729 | 0.466 | 0.0321 | 0.309 | -0.625 | 0.532 |
B_TIME_S | ASC_TRAIN | 0.00576 | 0.0802 | 11.1 | 0 | 0.0128 | 0.145 | 10.2 | 0 |
B_TIME_S | ASC_TRAIN_S | -0.0169 | -0.218 | 2.68 | 0.00735 | -0.0216 | -0.231 | 2.39 | 0.0169 |
B_TIME_S | B_COST | -0.023 | -0.338 | 17 | 0 | -0.0273 | -0.259 | 14.4 | 0 |
B_TIME_S | B_TIME | -0.0444 | -0.433 | 18.3 | 0 | -0.0662 | -0.494 | 15.7 | 0 |
Smallest eigenvalue: 5.36908
Largest eigenvalue: 66.9177
Condition number: 12.4635