optiscale (version 1.2.2)
Description
Optimal scaling of a data vector, relative to a set of targets, is
obtained through a least-squares transformation subject to appropriate measurement
constraints. The targets are usually predicted values from a statistical
model. If the data are nominal level, then the transformation must be
identity-preserving. If the data are ordinal level, then the
transformation must be monotonic. If the data are discrete, then tied data
values must remain tied in the optimal transformation. If the data are
continuous, then tied data values can be untied in the optimal
transformation.