augment.loess {broom} | R Documentation |
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 class 'loess' augment(x, data = stats::model.frame(x), newdata, ...)
x |
A |
data |
A |
newdata |
A |
... |
Arguments passed on to
|
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.
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 |
augment()
, stats::loess()
, stats::predict.loess()
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))