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 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 or predicted value.
The difference between observed and fitted values.
Standard errors of fitted values.
lo <- loess( mpg ~ hp + wt, mtcars, control = loess.control(surface = "direct") ) augment(lo) #> # A tibble: 32 × 6 #> .rownames mpg hp wt .fitted .resid #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21 110 2.62 21.4 -0.435 #> 2 Mazda RX4 Wag 21 110 2.88 20.9 0.0976 #> 3 Datsun 710 22.8 93 2.32 24.7 -1.88 #> 4 Hornet 4 Drive 21.4 110 3.22 19.6 1.76 #> 5 Hornet Sportabout 18.7 175 3.44 16.7 2.02 #> 6 Valiant 18.1 105 3.46 18.9 -0.833 #> 7 Duster 360 14.3 245 3.57 14.9 -0.641 #> 8 Merc 240D 24.4 62 3.19 25.1 -0.695 #> 9 Merc 230 22.8 95 3.15 21.4 1.43 #> 10 Merc 280 19.2 123 3.44 18.4 0.801 #> # … with 22 more rows # with all columns of original data augment(lo, mtcars) #> # A tibble: 32 × 14 #> .rownames mpg cyl disp hp drat wt qsec vs am gear carb #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 0 1 4 4 #> 2 Mazda RX4 … 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 Hornet 4 D… 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 Hornet Spo… 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 #> 6 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 #> 7 Duster 360 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 #> 8 Merc 240D 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 #> 9 Merc 230 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 #> 10 Merc 280 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 #> # … with 22 more rows, and 2 more variables: .fitted <dbl>, .resid <dbl> # with a new dataset augment(lo, newdata = head(mtcars)) #> # A tibble: 6 × 14 #> .rownames mpg cyl disp hp drat wt qsec vs am gear carb #> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1 Mazda RX4 21 6 160 110 3.9 2.62 16.5 0 1 4 4 #> 2 Mazda RX4 W… 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 Datsun 710 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 Hornet 4 Dr… 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 Hornet Spor… 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 #> 6 Valiant 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 #> # … with 2 more variables: .fitted <dbl>, .resid <dbl>