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, ...)
x |
A |
data |
A base::data.frame or |
newdata |
A |
se_fit |
Logical indicating whether or not a |
... |
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
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 |
Fitted or predicted value. |
.resid |
The difference between observed and fitted values. |
.se.fit |
Standard errors of fitted values. |
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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