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.
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. 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.
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.
# feel free to ignore the following lines—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)
}
}
#> # A tibble: 4,331 × 5
#> compounds input_fields iterations num_pending .cluster
#> <dbl> <dbl> <dbl> <dbl> <fct>
#> 1 997 137 20 0 1
#> 2 97 103 20 0 1
#> 3 101 75 10 0 1
#> 4 93 76 20 0 1
#> 5 100 82 20 0 1
#> 6 100 82 20 0 1
#> 7 105 88 20 0 1
#> 8 98 95 20 0 1
#> 9 101 91 20 0 1
#> 10 95 92 20 0 1
#> # … with 4,321 more rows