broom (version 0.8.0)

tidy.kmeans: Tidy a(n) kmeans object

Description

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.

Usage

# S3 method for kmeans
tidy(x, col.names = colnames(x$centers), ...)

Arguments

x

A kmeans object created by stats::kmeans().

col.names

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. 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.

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.

Details

For examples, see the kmeans vignette.

See Also

tidy(), stats::kmeans()

Other kmeans tidiers: augment.kmeans(), glance.kmeans()

Examples

# NOT RUN {
# feel free to ignore the following lines<U+2014>they allow {broom} to supply 
# examples without requiring the model/data-supplying package to be installed.
if (requireNamespace("cluster", quietly = TRUE)) {
  if (requireNamespace("modeldata", quietly = TRUE)) {
  
library(cluster)
library(modeldata)
library(dplyr)

data(hpc_data)

x <- hpc_data[, 2:5]

fit <- pam(x, k = 4)

tidy(fit)
glance(fit)
augment(fit, x)

  }
}
# }