tidy.pam: Tidy a(n) pam object

In broom: Convert Statistical Objects into Tidy Tibbles

Tidy a(n) pam 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 class 'pam'
tidy(x, col.names = paste0("x", 1:ncol(x$medoids)), ...)

Arguments

x	An pam object returned from cluster::pam()
col.names	Column names in the input data frame. Defaults to the names of the variables in x.
...	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. Two exceptions here are: tidy() methods will warn when supplied an exponentiate argument if it will be ignored. augment() methods will warn when supplied a newdata argument if it will be ignored.

Details

For examples, see the pam vignette.

Value

A tibble::tibble() with columns:

size	Size of each cluster.
max.diss	Maximal dissimilarity between the observations in the cluster and that cluster's medoid.
avg.diss	Average dissimilarity between the observations in the cluster and that cluster's medoid.
diameter	Diameter of the cluster.
separation	Separation of the cluster.
avg.width	Average silhouette width of the cluster.
cluster	A factor describing the cluster from 1:k.

See Also

tidy(), cluster::pam()

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

Examples

# load libraries for models and data
library(dplyr)
library(ggplot2)
library(cluster)
library(modeldata)
data(hpc_data)

x <- hpc_data[, 2:5]
p <- pam(x, k = 4)

# summarize model fit with tidiers + visualization
tidy(p)
glance(p)
augment(p, x)

augment(p, x) %>%
  ggplot(aes(compounds, input_fields)) +
  geom_point(aes(color = .cluster)) +
  geom_text(aes(label = cluster), data = tidy(p), size = 10)

broom documentation built on Sept. 30, 2024, 9:29 a.m.