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 smooth.spline
augment(x, data = x$data, ...)
A smooth.spline
object returned from stats::smooth.spline()
.
A base::data.frame or tibble::tibble()
containing the original
data that was used to produce the object x
. Defaults to
stats::model.frame(x)
so that augment(my_fit)
returns the augmented
original data. Do not pass new data to the data
argument.
Augment will report information such as influence and cooks distance for
data passed to the data
argument. These measures are only defined for
the original training data.
Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in ...
, where they will be ignored. If the misspelled
argument has a default value, the default value will be used.
For example, if you pass conf.lvel = 0.9
, all computation will
proceed using conf.level = 0.95
. Additionally, if you pass
newdata = my_tibble
to an augment()
method that does not
accept a newdata
argument, it will use the default value for
the data
argument.
augment()
, stats::smooth.spline()
,
stats::predict.smooth.spline()
Other smoothing spline tidiers:
glance.smooth.spline()
A tibble::tibble()
with columns:
Fitted or predicted value.
The difference between observed and fitted values.
# fit model
spl <- smooth.spline(mtcars$wt, mtcars$mpg, df = 4)
# summarize model fit with tidiers
augment(spl, mtcars)
#> # A tibble: 32 × 13
#> mpg cyl disp hp drat wt qsec vs am gear carb .fitted
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 22.9
#> 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 21.1
#> 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 25.3
#> 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 19.1
#> 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 17.8
#> 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 17.7
#> 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 17.1
#> 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 19.2
#> 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 19.5
#> 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 17.8
#> # … with 22 more rows, and 1 more variable: .resid <dbl>
# calls original columns x and y
augment(spl)
#> # A tibble: 32 × 5
#> x y w .fitted .resid
#> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 2.62 21 1 22.9 -1.87
#> 2 2.88 21 1 21.1 -0.117
#> 3 2.32 22.8 1 25.3 -2.48
#> 4 3.22 21.4 1 19.1 2.33
#> 5 3.44 18.7 1 17.8 0.928
#> 6 3.46 18.1 1 17.7 0.437
#> 7 3.57 14.3 1 17.1 -2.79
#> 8 3.19 24.4 1 19.2 5.19
#> 9 3.15 22.8 1 19.5 3.35
#> 10 3.44 19.2 1 17.8 1.43
#> # … with 22 more rows
library(ggplot2)
ggplot(augment(spl, mtcars), aes(wt, mpg)) +
geom_point() +
geom_line(aes(y = .fitted))