Tidy a(n) smooth.spine object

Source: R/stats-smooth.spline-tidiers.R

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, ...)

Arguments

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.

See also

augment(), stats::smooth.spline()

Other smoothing spline tidiers: augment.smooth.spline()

Value

A tibble::tibble() with exactly one row and columns:

crit

Minimized criterion

cv.crit

Cross-validation score

df

Degrees of freedom used by the model.

lambda

Choice of lambda corresponding to `spar`.

nobs

Number of observations used.

pen.crit

Penalized criterion.

spar

Smoothing parameter.

Examples


spl <- smooth.spline(mtcars$wt, mtcars$mpg, df = 4)
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>
augment(spl) # calls original columns x and y
#> # 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))