Smooth Effects on Response Penalty for 'CLM'

A regularization method for the cumulative link models. The 'smooth-effect-on-response penalty' ('SERP') provides flexible modelling of the ordinal model by enabling the smooth transition from the general cumulative link model to a coarser form of the same model. In other words, as the tuning parameter goes from zero to infinity, the subject-specific effects associated with each variable in the model tend to a unique global effect. The parameter estimates of the general cumulative model are mostly unidentifiable or at least only identifiable within a range of the entire parameter space. Thus, by maximizing a penalized rather than the usual non-penalized log-likelihood, this and other numerical problems common with the general model are to a large extent eliminated. Fitting is via a modified Newton's method. Several standard model performance and descriptive methods are also available. For more details on the penalty implemented here, see, 'Ugba et al. (2021)' and Tutz and Gertheiss (2016) .


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install.packages("serp")

0.2.1 by Ejike R. Ugba, 16 days ago


https://github.com/ejikeugba/serp


Report a bug at https://github.com/ejikeugba/serp/issues


Browse source code at https://github.com/cran/serp


Authors: Ejike R. Ugba [aut, cre, cph]


Documentation:   PDF Manual  


GPL-2 | file LICENSE license


Imports ordinal, stats

Suggests covr, testthat, VGAM


See at CRAN