A constrained generalized additive model is fitted by the cgam routine. Given a set of predictors, each of which may have a shape or order restrictions, the maximum likelihood estimator for the constrained generalized additive model is found using an iteratively re-weighted cone projection algorithm. The ShapeSelect routine chooses a subset of predictor variables and describes the component relationships with the response. For each predictor, the user need only specify a set of possible shape or order restrictions. A model selection method chooses the shapes and orderings of the relationships as well as the variables. The cone information criterion (CIC) is used to select the best combination of variables and shapes. A genetic algorithm may be used when the set of possible models is large. In addition, the wps routine implements a two-dimensional isotonic regression without additivity assumptions.
|Depends:||coneproj (≥ 1.11), svDialogs (≥ 0.9-57), R (≥ 3.0.2)|
|Suggests:||stats, MASS, graphics, grDevices, utils, SemiPar|
|Author:||Mary C. Meyer and Xiyue Liao|
|Maintainer:||Xiyue Liao <xiyue at rams.colostate.edu>|
|License:||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]|
|CRAN checks:||cgam results|
|Windows binaries:||r-devel: cgam_1.6.zip, r-release: cgam_1.6.zip, r-oldrel: cgam_1.6.zip|
|OS X El Capitan binaries:||r-release: cgam_1.6.tgz|
|OS X Mavericks binaries:||r-oldrel: cgam_1.6.tgz|
|Old sources:||cgam archive|
Please use the canonical form https://CRAN.R-project.org/package=cgam to link to this page.