The relation between baseline logit and conditional logit modelsΒΆ

Baseline-category logit models can be expressed as particular form of conditional logit models. In a conditional logit model (without random effects) the probability that individual i chooses alternative j from choice set \mathcal{S}_i is

\pi_{ij} = \frac{\exp(\eta_{ij})}{\sum_{k\in\mathcal{S}_i}\exp(\eta_{ik})}


\eta_{ij} = \alpha_1x_{1ij}+\cdots+\alpha_qx_{qij}

In a baseline-category logit model, the set of alternatives is the same for all individuals i that is \mathcal{S}_i = {1,\ldots,q} and the linear part of the model can be written like:

\eta_{ij} = \beta_{j0}+\beta_{j1}x_{i1}+\cdots+\beta_{jr}x_{ri}

where the coefficients in the equation for baseline category j are all zero, i.e.

\beta_{10} = \cdots = \beta_{1r} = 0

After setting

x_{(g\times(j-1))ij} = d_{gj}, \quad
x_{(g\times(j-1)+h)ij} = d_{gj}x_{hi}, \qquad
\text{with }d_{gj}=
0&\text{for } j\neq g\text{ or } j=g\text{ and } j=0\\
1&\text{for } j=g \text{ and } j\neq0\\

we have for the log-odds:


where \alpha_1=\beta_{21}, \alpha_2=\beta_{22}, etc.

That is, the baseline-category logit model is translated into a conditional logit model where the alternative-specific values of the attribute variables are interaction terms composed of alternativ-specific dummes and individual-specific values of characteristics variables.

Analogously, the random-effects extension of the baseline-logit model can be translated into a random-effects conditional logit model where the random intercepts in the logit equations of the baseline-logit model are translated into random slopes of category-specific dummy variables.