Converts multinomial logit data into a combination of several binary logit data sets, in order to analyze it via the Begg & Gray approximation using a binary logistic regression.
Using the methodology of Bayesian Model Averaging in the BMA package, the variable selection problem is applied to multinomial logit models in which coefficients can be estimated relative to a base alternative.
Provides a modified function bic.glm of the BMA package that can be applied to multinomial logit (MNL) data. The data is converted to binary logit using the Begg & Gray approximation. The package also contains functions for maximum likelihood estimation of MNL models.
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