Compute all the single terms in the
scope argument that
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, ...)
further arguments passed to or from other methods.
These functions are a direct adaptation of
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.
. means ‘what is
these functions are simpler, e.g., there is no
Cp, F and Rao (score) tests,
Most models do not have a deviance, however twice the
log-likelihood differences are used to test the significance
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
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.
object is of the form
vglm(..., constraints = list(x1 = cm1, x2 = cm2))
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
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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