lm
and
glm
and the default method will work for many other classes.
addterm(object, scope, scale = 0, test = c("none", "Chisq", "F"), k = 2, sorted = F, trace = F, ...)
lm
,
aov
and
glm
models. Specifying
scale
asserts that the residual standard error or dispersion is known.
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=2
gives the genuine AIC:
k = log(n)
is sometimes referred
to as BIC or SBC.
TRUE
additional information may be given on the fits as they are tried.
"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.
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.
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")