augment.loess: Tidy a(n) loess object
In broom: Convert Statistical Objects into Tidy Tibbles

augment.loess

R Documentation

Tidy a(n) loess object

Description

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.

Usage

## S3 method for class 'loess'
augment(x, data = model.frame(x), newdata = NULL, se_fit = FALSE, ...)

Arguments

`x`	A `loess` objects returned by `stats::loess()`.
`data`	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.
`newdata`	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.
`se_fit`	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`. 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.

Details

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.

Value

A tibble::tibble() with columns:

`.fitted`	Fitted or predicted value.
`.resid`	The difference between observed and fitted values.
`.se.fit`	Standard errors of fitted values.

Examples


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))

broom documentation built on Sept. 30, 2024, 9:29 a.m.