ordinal (version 2010.03-04)

addterm.clm: Try all one-term additions to and deletions from a model

Description

Try fitting all models that differ from the current model by adding or deleting a single term from those supplied while maintaining marginality.

Usage

## S3 method for class 'clm':
addterm(object, scope, scale = 0, test = c("none", "Chisq"),
        k = 2, sorted = FALSE, trace = FALSE,
        which = c("location", "scale"), ...)
## S3 method for class 'clm':
dropterm(object, scope, scale = 0, test = c("none", "Chisq"),
        k = 2, sorted = FALSE, trace = FALSE,
        which = c("location", "scale"), ...)

Arguments

object

A clm object.

scope

for addterm: a formula specifying a maximal model which should include the current one. All additional terms in the maximal model with all marginal terms in the original model are tried. For dropterm: a formul

scale

used in the definition of the AIC statistic for selecting the models. Specifying scale asserts that the dispersion is known.

test

should the results include a test statistic relative to the original model? The Chisq test is a likelihood-ratio test.

k

the multiple of the number of degrees of freedom used for the penalty. Only k=2 gives the genuine AIC: k = log(n) is sometimes referred to as BIC or SBC.

sorted

should the results be sorted on the value of AIC?

trace

if TRUE additional information may be given on the fits as they are tried.

which

should additions or deletions occur in location or scale models?

...

arguments passed to or from other methods.

Value

• A table of class "anova" containing columns for the change in degrees of freedom, AIC and the likelihood ratio statistic. If test = "Chisq" a column also contains the p-value from the Chisq test.

Details

The definition of AIC is only up to an additive constant because the likelihood function is only defined up to an additive constant.

See Also

clm, anova, addterm.default and dropterm.default

Examples

options(contrasts = c("contr.treatment", "contr.poly"))
data(soup)
mB1 <- clm(SURENESS ~ PROD + GENDER + SOUPTYPE,
           scale = ~ COLD, data = soup, link = "probit",
           Hess = FALSE)
dropterm(mB1, test = "Chi")       # or
dropterm(mB1, which = "location", test = "Chi")
dropterm(mB1, which = "scale", test = "Chi")
addterm(mB1, scope = ~.^2, test = "Chi", which = "location")
addterm(mB1, scope = ~ . + GENDER + SOUPTYPE,
        test = "Chi", which = "scale")
addterm(mB1, scope = ~ . + AGEGROUP + SOUPFREQ,
        test = "Chi", which = "location")

## Fit model from polr example:
data(housing, package = "MASS")
fm1 <- clm(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
addterm(fm1, ~ Infl + Type + Cont, test= "Chisq", which = "scale")
dropterm(fm1, test = "Chisq")