18a. Ordinal logit model

Example of an ordinal logit model. This is just to illustrate the syntax, as the data are not ordered. But the example assume, for the sake of it, that the alternatives are ordered as 1->2->3

Michel Bierlaire, EPFL Thu Jun 26 2025, 15:52:21

from IPython.core.display_functions import display

import biogeme.biogeme_logging as blog
from biogeme.biogeme import BIOGEME
from biogeme.expressions import Beta, OrderedLogLogit
from biogeme.results_processing import (
    EstimationResults,
    get_pandas_estimated_parameters,
)

See the data processing script: Data preparation for Swissmetro.

from swissmetro_data import CHOICE, TRAIN_COST_SCALED, TRAIN_TT_SCALED, database

logger = blog.get_screen_logger(level=blog.INFO)
logger.info('Example b18a_ordinal_logit.py')

Example b18a_ordinal_logit.py

Parameters to be estimated

b_time = Beta('b_time', 0, None, None, 0)
b_cost = Beta('b_cost', 0, None, None, 0)

Threshold parameters for the ordered logit.

\(\tau_1 \leq 0\).

tau1 = Beta('tau1', -1, None, 0, 0)

\(\delta_2 \geq 0\).

delta2 = Beta('delta2', 2, 0, None, 0)

\(\tau_2 = \tau_1 + \delta_2\)

tau2 = tau1 + delta2

Utility.

utility = b_time * TRAIN_TT_SCALED + b_cost * TRAIN_COST_SCALED

Associate each discrete indicator with an interval.

1. \(-\infty \to \tau_1\),

2. \(\tau_1 \to \tau_2\),

3. \(\tau_2 \to +\infty\).

log_probability = OrderedLogLogit(
    eta=utility,
    cutpoints=[tau1, tau2],
    y=CHOICE,
    categories=[1, 2, 3],
    neutral_labels=[],
)

Create the Biogeme object.

the_biogeme = BIOGEME(database, log_probability)
the_biogeme.model_name = 'b18a_ordinal_logit'

Biogeme parameters read from biogeme.toml.

Estimate the parameters.

try:
    results = EstimationResults.from_yaml_file(
        filename=f'saved_results/{the_biogeme.model_name}.yaml'
    )
except FileNotFoundError:
    results = the_biogeme.estimate()

print(results.short_summary())

Results for model b18a_ordinal_logit
Nbr of parameters:              4
Sample size:                    6768
Excluded data:                  3960
Final log likelihood:           -5789.309
Akaike Information Criterion:   11586.62
Bayesian Information Criterion: 11613.9

pandas_results = get_pandas_estimated_parameters(estimation_results=results)
display(pandas_results)

{'Estimated parameters':      Name     Value  Robust std err.  Robust t-stat.  Robust p-value
0  b_time -0.022080         0.040060       -0.551179        0.581511
1  b_cost  1.262900         0.058542       21.572537        0.000000
2    tau1 -1.030101         0.067968      -15.155756        0.000000
3  delta2  3.193022         0.046336       68.909624        0.000000}