  In binary and ordinal regression one can distinguish between a location
component and a scaling component. While the former determines the location
within the range of the response categories, the scaling indicates variance
heterogeneity. In particular since it has been demonstrated that misleading
effects can occur if one ignores the presence of a scaling component it is
important to account for potential scaling effects in the regression model,
which is not possible in available recursive partitioning methods. The proposed
recursive partitioning method yields two trees, one for the location and one
for the scaling. They show in a simple interpretable way how variables interact
to determine the binary or ordinal response. The developed algorithm controls
for the global significance level and automatically selects the variables that
have an impact on the response. The modelling approach is illustrated by
several real-world applications.
