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 pam
tidy(x, col.names = paste0("x", 1:ncol(x$medoids)), ...)



An pam object returned from cluster::pam()


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.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.


For examples, see the pam vignette.

See also

tidy(), cluster::pam()

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


A tibble::tibble() with columns:


Size of each cluster.


Maximal dissimilarity between the observations in the cluster and that cluster's medoid.


Average dissimilarity between the observations in the cluster and that cluster's medoid.


Diameter of the cluster.


Separation of the cluster.


Average silhouette width of the cluster.


A factor describing the cluster from 1:k.


if (FALSE) { library(dplyr) library(ggplot2) library(cluster) library(modeldata) data(hpc_data) x <- hpc_data[, 2:5] p <- pam(x, k = 4) 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) }