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
glance(x, ...)
A smooth.spline
object returned from stats::smooth.spline()
.
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()
Other smoothing spline tidiers:
augment.smooth.spline()
A tibble::tibble()
with exactly one row and columns:
Minimized criterion
Cross-validation score
Degrees of freedom used by the model.
Choice of lambda corresponding to `spar`.
Number of observations used.
Penalized criterion.
Smoothing parameter.
# 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))