These functions are a direct adaptation of
`add1.glm`

and
`drop1.glm`

for `vglm-class`

objects.
For `drop1`

methods, a missing `scope`

is taken to
be all terms in the model. The hierarchy is respected when
considering terms to be added or dropped: all main effects
contained in a second-order interaction must remain, and so on.
In a `scope`

formula `.`

means ‘what is
already there’.

Compared to
`add1.glm`

and
`drop1.glm`

these functions are simpler, e.g., there is no
*Cp*, F and Rao (score) tests,
`x`

and `scale`

arguments.
Most models do not have a deviance, however twice the
log-likelihood differences are used to test the significance
of terms.

The default output table gives AIC, defined as minus twice log
likelihood plus \(2p\) where \(p\) is the rank of the model (the
number of effective parameters). This is only defined up to an
additive constant (like log-likelihoods).