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), ...)
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.level = 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.
# NOT RUN {# }# NOT RUN {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)
# }