clustering.md
In rsample: General Resampling Infrastructure

bad args

Code
  clustering_cv(dat1)
Condition
  Error in `clustering_cv()`:
  ! `vars` is required and must contain at least one variable in `data`.

Code
  clustering_cv(iris, Sepal.Length, v = -500)
Condition
  Error in `clustering_cv()`:
  ! `v` must be a whole number larger than or equal to 2, not the number -500.

Code
  clustering_cv(iris, Sepal.Length, v = 500)
Condition
  Error in `clustering_cv()`:
  ! The number of rows is less than `v` = 500.

Code
  clustering_cv(iris, Sepal.Length, cluster_function = "not an option")
Condition
  Error in `clustering_cv()`:
  ! `cluster_function` must be one of "kmeans" or "hclust", not "not an option".

Code
  clustering_cv(Orange, v = 1, vars = "Tree")
Condition
  Error in `clustering_cv()`:
  ! `v` must be a whole number larger than or equal to 2, not the number 1.

Code
  clustering_cv(Orange, repeats = 0)
Condition
  Error in `clustering_cv()`:
  ! `repeats` must be a whole number larger than or equal to 1, not the number 0.

Code
  clustering_cv(Orange, repeats = NULL)
Condition
  Error in `clustering_cv()`:
  ! `repeats` must be a whole number, not `NULL`.

Code
  clustering_cv(mtcars, mpg, v = nrow(mtcars))
Condition
  Error in `clustering_cv()`:
  ! Leave-one-out cross-validation is not supported by this function.
  x You set `v` to `nrow(data)`, which would result in a leave-one-out cross-validation.
  i Use `loo_cv()` in this case.

printing

Code
  clustering_cv(dat1, c, v = 2)
Output
  # 2-cluster cross-validation 
  # A tibble: 2 x 2
    splits         id   
    <list>         <chr>
  1 <split [5/15]> Fold1
  2 <split [15/5]> Fold2