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, ...)A nlrq object returned from quantreg::nlrq().
Arguments passed on to augment.nls
dataA data.frame() or 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 data argument.
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.
newdataA data.frame() or tibble::tibble() containing all
the original predictors used to create x. Defaults to NULL, indicating
that nothing has been passed to newdata. If newdata is specified,
the data argument will be ignored.
A tibble::tibble() containing the data passed to augment,
and additional columns:
The predicted response for that observation.
The residual for a particular point. Present only when
data has been passed to augment via the data argument.
Other quantreg tidiers:
augment.rqs(),
augment.rq(),
glance.nlrq(),
glance.rq(),
tidy.nlrq(),
tidy.rqs(),
tidy.rq()