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 cross 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 = paste0("x", 1:ncol(x$centers)), ...)A kmeans object created by stats::kmeans().
Dimension names. Defaults to 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. 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.
A tibble::tibble with one row per cluster, and columns:
Number of points in cluster
The within-cluster sum of squares
A factor describing the cluster from 1:k
For examples, see the kmeans vignette.
Other kmeans tidiers:
augment.kmeans(),
glance.kmeans()