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 kmeans
tidy(x, col.names = colnames(x$centers), ...)A tibble::tibble() with columns:
A factor describing the cluster from 1:k.
Number of points assigned to cluster.
The within-cluster sum of squares.
A kmeans object created by stats::kmeans().
Dimension names. Defaults to the names of the variables
in x. Set to NULL to get names x1, x2, ....
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.
tidy(), stats::kmeans()
Other kmeans tidiers:
augment.kmeans(),
glance.kmeans()