Tidy summarizes information about the components of a model.
A model component might be a single term in a regression, a single
hypothesis, a cluster, or a class. Exactly what tidy considers to be a
model component varies cross models but is usually self-evident.
If a model has several distinct types of components, you will need to
specify which components to return.
# S3 method for nlrq
augment(x, data = NULL, newdata = NULL, ...)
nlrq object returned from
tibble::tibble() containing the original
data that was used to produce the object
x. Defaults to
stats::model.frame(x) so that
augment(my_fit) returns the augmented
original data. Do not pass new data to the
Augment will report information such as influence and cooks distance for
data passed to the
data argument. These measures are only defined for
the original training data.
tibble::tibble() containing all
the original predictors used to create
x. Defaults to
that nothing has been passed to
newdata is specified,
data argument will be ignored.
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
..., where they will be ignored. If the misspelled
argument has a default value, the default value will be used.
For example, if you pass
conf.lvel = 0.9, all computation will
conf.level = 0.95. Additionally, if you pass
newdata = my_tibble to an
augment() method that does not
newdata argument, it will use the default value for