ordFacReg-package: Least Squares, Logistic, and Cox-Regression with Ordered...

In OrdFacReg: Least Squares, Logistic, and Cox-Regression with Ordered Predictors

Description

In biomedical studies, researchers are often interested in assessing the association between one or more ordinal explanatory variables and an outcome variable, at the same time adjusting for covariates of any type. The outcome variable may be continuous, binary, or represent censored survival times. In the absence of a precise knowledge of the response function, using monotonicity constraints on the ordinal variables improves efficiency in estimating parameters, especially when sample sizes are small. This package implements an active set algorithm that efficiently computes such estimators.

Details

Package:	OrdFacReg
Type:	Package
Version:	1.0.6
Date:	2015-07-03
License:	GPL (>=2)
LazyLoad:	yes

Use this package to get estimates in least squares, logistic, or Cox-regression where coefficients corresponding to dummy variables of ordered factors are estimated to be in non-decreasing order and at least 0. The package offers an active set algorithm implemented in the functions ordFacReg for least squares and logistic regression and ordFacRegCox for Cox-regression.

Author(s)

Kaspar Rufibach (maintainer)
[e-mail]
http://www.kasparrufibach.ch

References

Rufibach, K. (2010). An Active Set Algorithm to Estimate Parameters in Generalized Linear Models with Ordered Predictors. Comput. Statist. Data Anal., 54, 1442-1456.

See Also

Examples are given in the help files of the functions ordFacReg and ordFacRegCox.

OrdFacReg documentation built on May 1, 2019, 10:06 p.m.