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)), ...)

## Arguments

x 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.

## Details

For examples, see the kmeans vignette.

tidy(), stats::kmeans()

Other kmeans tidiers: augment.kmeans, glance.kmeans

## Value

A tibble::tibble() with columns:

cluster

A factor describing the cluster from 1:k

size

Number of points assigned to cluster.

withinss

The within-cluster sum of squares