clustering_cv | R Documentation |
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.
clustering_cv(
data,
vars,
v = 10,
repeats = 1,
distance_function = "dist",
cluster_function = c("kmeans", "hclust"),
...
)
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 |
cluster_function |
Which function should be used for clustering?
Options are either |
... |
Extra arguments passed on to |
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.
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
.
data(ames, package = "modeldata")
clustering_cv(ames, vars = c(Sale_Price, First_Flr_SF, Second_Flr_SF), v = 2)
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