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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 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.
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
. Two exceptions here are:
tidy()
methods will warn when supplied an exponentiate
argument if
it will be ignored.
augment()
methods will warn when supplied a newdata
argument if it
will be ignored.
augment()
, stats::smooth.spline()
Other smoothing spline tidiers:
augment.smooth.spline()
# fit model
spl <- smooth.spline(mtcars$wt, mtcars$mpg, df = 4)
# summarize model fit with tidiers
augment(spl, mtcars)
# calls original columns x and y
augment(spl)
library(ggplot2)
ggplot(augment(spl, mtcars), aes(wt, mpg)) +
geom_point() +
geom_line(aes(y = .fitted))
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