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 (2021)' , 'Ugba et al. (2021)' and 'Tutz and Gertheiss (2016)' .


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install.packages("serp")

0.2.3 by Ejike R. Ugba, 3 months 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, crayon, stats

Suggests covr, testthat, VGAM


See at CRAN