| add1.vglm | R Documentation |
Compute all the single terms in the scope argument that
can be
added to or
dropped from the model, fit those models and compute a
table of the changes in fit.
## S3 method for class 'vglm'
add1(object, scope, test = c("none", "LRT"), k = 2, ...)
## S3 method for class 'vglm'
drop1(object, scope, test = c("none", "LRT"), k = 2, ...)
object |
a fitted |
scope, k |
See |
test |
Same as |
... |
further arguments passed to or from other methods. |
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).
An object of class "anova" summarizing the differences
in fit between the models.
In general, the same warnings in
add1.glm and
drop1.glm
apply here.
Furthermore, these functions have not been rigorously tested
for all models, so treat the results cautiously and please
report any bugs.
Care is needed to check that the constraint matrices of added
terms are correct.
Also, if object is of the form
vglm(..., constraints = list(x1 = cm1, x2 = cm2))
then add1.vglm may fail because the
constraints argument needs to have the constaint
matrices for all terms.
Most VGAM family functions do not compute a deviance,
but instead the likelihood function is evaluated at the MLE.
Hence a column name "Deviance" only appears for a
few models; and almost always there is a column labelled
"logLik".
step4vglm,
vglm,
extractAIC.vglm,
trim.constraints,
anova.vglm,
backPain2,
update.
data("backPain2", package = "VGAM")
summary(backPain2)
fit1 <- vglm(pain ~ x2 + x3 + x4, propodds, data = backPain2)
coef(fit1)
add1(fit1, scope = ~ x2 * x3 * x4, test = "LRT")
drop1(fit1, test = "LRT")
fit2 <- vglm(pain ~ x2 * x3 * x4, propodds, data = backPain2)
drop1(fit2)
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