datasets

Maximum likelihood estimation of random utility discrete
choice models. The software is described in Croissant (2020)
<doi:10.18637/jss.v095.i11> and the underlying methods in
Train (2009) <doi:10.1017/CBO9780511805271>.

Yves Croissant

mlogit

Multinomial Logit Models

Car function

A dataframe containing :<ul>
<li>choice: choice of a vehicule amoung 6 propositions,</li>
<li>college: college education?,</li>
<li>hsg2: size of household greater than 2?</li>
<li>coml5: commulte lower than 5 miles a day?,</li>
<li>typez: body type, one of regcar (regular car), sportuv (sport utility vehicule), sportcar, stwagon (station wagon), truck, van, for each proposition z from 1 to 6,</li>
<li>fuelz: fuel for proposition z, one of gasoline, methanol, cng (compressed natural gas), electric.,</li>
<li>pricez: price of vehicule divided by the logarithme of income,</li>
<li>rangez: hundreds of miles vehicule can travel between refuelings/rechargings,</li>
<li>accz: acceleration, tens of seconds required to reach 30 mph from stop,</li>
<li>speedz: highest attainable speed in hundreds of mph,</li>
<li>pollutionz: tailpipe emissions as fraction of those for new gas vehicule,</li>
<li>sizez: 0 for a mini, 1 for a subcompact, 2 for a compact and 3 for a mid--size or large vehicule,</li>
<li>spacez: fraction of luggage space in comparable new gas vehicule,</li>
<li>costz: cost per mile of travel (tens of cents) : home recharging for electric vehicule, station refueling otherwise,</li>
<li>stationz: fraction of stations that can refuel/recharge vehicule.</li>
</ul>

Car: Stated Preferences for Car Choice

Description

Arguments

Format

References