lm
and
glm
and the default method will work for many other classes.
dropterm(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.nxt <- update(quine.hi, . ~ . - Eth:Sex:Age:Lrn) dropterm(quine.nxt, test = "F") quine.stp <- stepAIC(quine.nxt, scope = list(upper = ~Eth*Sex*Age*Lrn, lower = ~1), trace = F) dropterm(quine.stp, test = "F") quine.3 <- update(quine.stp, . ~ . - Eth:Age:Lrn) dropterm(quine.3, test = "F") quine.4 <- update(quine.3, . ~ . - Eth:Age) dropterm(quine.4, test = "F") quine.5 <- update(quine.4, . ~ . - Age:Lrn) dropterm(quine.5, test = "F") house.glm0 <- glm(Freq ~ Infl*Type*Cont + Sat, family = poisson, data = housing) house.glm1 <- update(house.glm0, . ~ . + Sat*(Infl+Type+Cont)) dropterm(house.glm1, test = "Chisq")