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, ...)

Arguments

x

A loess objects returned by stats::loess().

data

A base::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 base::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.

se_fit

Logical indicating whether or not a .se.fit column should be added to the augmented output. For some models, this calculation can be somewhat time-consuming. Defaults to FALSE.

...

Additional arguments. Not used. Needed to match generic signature only. Cautionary note: Misspelled arguments will be absorbed in ..., where they will be ignored. If the misspelled argument has a default value, the default value will be used. For example, if you pass conf.level = 0.9, all computation will proceed using conf.level = 0.95. Additionally, if you pass newdata = my_tibble to an augment() method that does not accept a newdata argument, it will use the default value for the data argument.

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.

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.

See also

Value

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.

Examples

lo <- loess( mpg ~ hp + wt, mtcars, control = loess.control(surface = "direct") ) augment(lo)
#> # A tibble: 32 x 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 x 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 RX… 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 Datsun 7… 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 Hornet 4… 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 Hornet S… 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 3… 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 x 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 RX… 21 6 160 110 3.9 2.88 17.0 0 1 4 4 #> 3 Datsun 7… 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 #> 4 Hornet 4… 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 #> 5 Hornet S… 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>