Cluster cross-validation splits the data into V groups of disjointed sets using k-means clustering of some variables. A resample of the analysis data consists of V-1 of the folds/clusters while the assessment set contains the final fold/cluster. In basic cross-validation (i.e. no repeats), the number of resamples is equal to V.

## Usage

clustering_cv(
data,
vars,
v = 10,
repeats = 1,
distance_function = "dist",
cluster_function = c("kmeans", "hclust"),
...
)

## Arguments

data

A data frame.

vars

A vector of bare variable names to use to cluster the data.

v

The number of partitions of the data set.

repeats

The number of times to repeat the clustered partitioning.

distance_function

Which function should be used for distance calculations? Defaults to stats::dist(). You can also provide your own function; see Details.

cluster_function

Which function should be used for clustering? Options are either "kmeans" (to use stats::kmeans()) or "hclust" (to use stats::hclust()). You can also provide your own function; see Details.

...

Extra arguments passed on to cluster_function.

## Value

A tibble with classes rset, tbl_df, tbl, and data.frame. The results include a column for the data split objects and an identification variable id.

## Details

The variables in the vars argument are used for k-means clustering of the data into disjointed sets or for hierarchical clustering of the data. These clusters are used as the folds for cross-validation. Depending on how the data are distributed, there may not be an equal number of points in each fold.

You can optionally provide a custom function to distance_function. The function should take a data frame (as created via data[vars]) and return a stats::dist() object with distances between data points.

You can optionally provide a custom function to cluster_function. The function must take three arguments:

• dists, a stats::dist() object with distances between data points

• v, a length-1 numeric for the number of folds to create

• ..., to pass any additional named arguments to your function

The function should return a vector of cluster assignments of length nrow(data), with each element of the vector corresponding to the matching row of the data frame.

## Examples

data(ames, package = "modeldata")
clustering_cv(ames, vars = c(Sale_Price, First_Flr_SF, Second_Flr_SF), v = 2)
#> # 2-cluster cross-validation
#> # A tibble: 2 × 2
#>   splits             id
#>   <list>             <chr>
#> 1 <split [441/2489]> Fold1
#> 2 <split [2489/441]> Fold2