augment.loess {broom} | 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 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.

### Usage

    ## S3 method for class 'loess'
    augment(x, data = stats::model.frame(x), newdata, ...)

### Arguments

`x` | A `loess` objects returned by `stats::loess()`.

`data`

A `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 `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.

`...`

Arguments passed on to `stats:::predict.loess`

object

  an object fitted by `loess`.

newdata

  an optional data frame in which to look for variables with which to predict, or a matrix or vector containing exactly the variables needs for prediction. If missing, the original data points are used.

se

  should standard errors be computed?

na.action

  function determining what should be done with missing values in data frame `newdata`. The default is to predict `NA`.

### 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.

### Value

When `newdata` is not supplied `augment.loess` returns one row for each observation with three columns added to the original data:

`.fitted` | Fitted values of model
`.se.fit` | Standard errors of the fitted values
`.resid` | Residuals of the fitted values

When `newdata` is supplied `augment.loess` returns one row for each observation with one additional column:

`.fitted` | Fitted values of model
`.se.fit` | Standard errors of the fitted values

### See Also

na.action

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

### Examples

    lo <- loess(mpg ~ wt, mtcars)
    augment(lo)

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

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

[Package broom version 0.5.2 Index]