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 across 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 loess
augment(x, data = model.frame(x), newdata = NULL, se_fit = FALSE, ...)A loess objects returned by stats::loess().
A base::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.
A base::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.
Logical indicating whether or not a .se.fit column should be
added to the augmented output. For some models, this calculation can be
somewhat time-consuming. Defaults to FALSE.
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in ..., 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
proceed using conf.level = 0.95. Additionally, if you pass
newdata = my_tibble to an augment() method that does not
accept a newdata argument, it will use the default value for
the data argument.
A tibble::tibble() with columns:
Fitted or predicted value.
The difference between observed and fitted values.
Standard errors of fitted values.
When the modeling was performed with na.action = "na.omit"
(as is the typical default), rows with NA in the initial data are omitted
entirely from the augmented data frame. When the modeling was performed
with na.action = "na.exclude", one should provide the original data
as a second argument, at which point the augmented data will contain those
rows (typically with NAs in place of the new columns). If the original data
is not provided to augment() and na.action = "na.exclude", a
warning is raised and the incomplete rows are dropped.
Note that loess objects by default will not predict on data
outside of a bounding hypercube defined by the training data unless the
original loess object was fit with
control = loess.control(surface = \"direct\")). See
stats::predict.loess() for details.
# NOT RUN {
lo <- loess(
mpg ~ hp + wt,
mtcars,
control = loess.control(surface = "direct")
)
augment(lo)
# with all columns of original data
augment(lo, mtcars)
# with a new dataset
augment(lo, newdata = head(mtcars))
# }
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