augment.loess: Tidy a(n) loess object

View source: R/stats-loess-tidiers.R

augment.loessR Documentation

Tidy a(n) loess object


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 class '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 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. Two exceptions here are:

  • tidy() methods will warn when supplied an exponentiate argument if it will be ignored.

  • augment() methods will warn when supplied a newdata argument if it will be ignored.


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.


A tibble::tibble() with columns:


Fitted or predicted value.


The difference between observed and fitted values.

Standard errors of fitted values.

See Also


augment(), stats::loess(), stats::predict.loess()


lo <- loess(
  mpg ~ hp + wt,
  control = loess.control(surface = "direct")


# with all columns of original data
augment(lo, mtcars)

# with a new dataset
augment(lo, newdata = head(mtcars))

broom documentation built on July 9, 2023, 5:28 p.m.