MASS (version 7.3-56)

addterm: Try All One-Term Additions to a Model

Description

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

This function is generic; there exist methods for classes lm and glm and the default method will work for many other classes.

Usage

addterm(object, …)
# S3 method for default
addterm(object, scope, scale = 0, test = c("none", "Chisq"),
        k = 2, sorted = FALSE, trace = FALSE, …)
# S3 method for lm
addterm(object, scope, scale = 0, test = c("none", "Chisq", "F"),
        k = 2, sorted = FALSE, …)
# S3 method for glm
addterm(object, scope, scale = 0, test = c("none", "Chisq", "F"),
        k = 2, sorted = FALSE, trace = FALSE, …)

Arguments

object

An object fitted by some model-fitting function.

scope

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.

scale

used in the definition of the AIC statistic for selecting the models, currently only for lm, aov and glm models. Specifying scale asserts that the residual standard error or dispersion is known.

test

should the results include a test statistic relative to the original model? The F test is only appropriate for lm and aov models, and perhaps for some over-dispersed glm models. The Chisq test can be an exact test (lm models with known scale) or a likelihood-ratio test depending on the method.

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.

…

arguments passed to or from other methods.

Value

A table of class "anova" containing at least columns for the change in degrees of freedom and AIC (or Cp) for the models. Some methods will give further information, for example sums of squares, deviances, log-likelihoods and test statistics.

Details

The definition of AIC is only up to an additive constant: when appropriate (lm models with specified scale) the constant is taken to be that used in Mallows' Cp statistic and the results are labelled accordingly.

References

Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.

See Also

dropterm, stepAIC

Examples

# NOT RUN {
quine.hi <- aov(log(Days + 2.5) ~ .^4, quine)
quine.lo <- aov(log(Days+2.5) ~ 1, quine)
addterm(quine.lo, quine.hi, test="F")

house.glm0 <- glm(Freq ~ Infl*Type*Cont + Sat, family=poisson,
                   data=housing)
addterm(house.glm0, ~. + Sat:(Infl+Type+Cont), test="Chisq")
house.glm1 <- update(house.glm0, . ~ . + Sat*(Infl+Type+Cont))
addterm(house.glm1, ~. + Sat:(Infl+Type+Cont)^2, test = "Chisq")
# }