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.
# S3 method for loess
augment(x, data = stats::model.frame(x), newdata, ...)A loess objects returned by stats::loess().
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.
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 the loess predict method.
When newdata is not supplied augment.loess
returns one row for each observation with three columns added
to the original data:
Fitted values of model
Standard errors of the fitted values
Residuals of the fitted values
When newdata is supplied augment.loess returns one row for each observation with one additional column:
Fitted values of model
Standard errors of the 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.
# NOT RUN {
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))
# }
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