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), ...)
x | A |
---|---|
col.names | Dimension names. Defaults to the names of the variables
in x. Set to NULL to get names |
... | Additional arguments. Not used. Needed to match generic
signature only. Cautionary note: Misspelled arguments will be
absorbed in |
For examples, see the kmeans vignette.
Other kmeans tidiers:
augment.kmeans()
,
glance.kmeans()
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.
if (FALSE) { library(cluster) library(dplyr) library(modeldata) data(hpc_data) x <- hpc_data[, 2:5] fit <- pam(x, k = 4) tidy(fit) glance(fit) augment(fit, x) }