smof-package {smof}

Scoring Methodology for Ordered Factors

Description

Starting from a given object representing a fitted model (within a certain set of model classes) whose linear predictor includes some ordered factor(s) among the explanatory variables, a new model is constructed and fitted where each named factor is replaced by a single numeric score, suitably chosen so that the new variable produces a fit comparable with the standard methodology based on a set of polynomial contrasts.

Details

The DESCRIPTION file:

Package:	smof
Type:	Package
Title:	Scoring Methodology for Ordered Factors
Version:	1.1.0
Date:	2024-03-04
Authors@R:	person(given = "Adelchi", family = "Azzalini", email = "[e-mail]", role = c("aut", "cre"), comment = c(ORCID = "0000-0002-7583-1269"))
Maintainer:	Adelchi Azzalini <[e-mail]>
Depends:	R (>= 4.0.0)
Imports:	stats, methods
Suggests:	ggplot2, survival
ByteCompile:	yes
NeedsCompilation:	no
Description:	Starting from a given object representing a fitted model (within a certain set of model classes) whose linear predictor includes some ordered factor(s) among the explanatory variables, a new model is constructed and fitted where each named factor is replaced by a single numeric score, suitably chosen so that the new variable produces a fit comparable with the standard methodology based on a set of polynomial contrasts. Reference: Azzalini (2023) <doi:10.1002/sta4.624>.
License:	GPL-2 | GPL-3
Encoding:	UTF-8
Author:	Adelchi Azzalini [aut, cre] (<https://orcid.org/0000-0002-7583-1269>)

Index of help topics:

print.smof              Methods for 'smof' objects
smof                    Scoring Methodology for Ordered Factors
smof-package            Scoring Methodology for Ordered Factors

Author(s)

Author: Adelchi Azzalini [aut, cre] (<https://orcid.org/0000-0002-7583-1269>) Maintainer: Adelchi Azzalini <[e-mail]>

References

Azzalini, A. (2023). On the use of ordered factors as explanatory variables. Stat 12, e624. doi:10.1002/sta4.624

Examples

library(datasets)
data(esoph)
contrasts(esoph$agegp, 2) <- contr.poly(6) # optional
contrasts(esoph$tobgp, 1) <- contr.poly(4) # optional
obj1 <- glm(cbind(ncases, ncontrols) ~ agegp + tobgp + alcgp, family=binomial(), data=esoph)        
out0 <- smof(obj1, esoph, "alcgp", distr.type="gh")
print(summary(out0$object))