spatialsample (version 0.1.0)

spatial_clustering_cv: Spatial or Cluster Cross-Validation

Description

Spatial or cluster cross-validation splits the data into V groups of disjointed sets using k-means clustering of some variables, typically spatial coordinates. 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 spatial cross-validation (i.e. no repeats), the number of resamples is equal to V.

Usage

spatial_clustering_cv(data, coords, v = 10, ...)

Arguments

data

A data frame.

coords

A vector of variable names, typically spatial coordinates, to partition the data into disjointed sets via k-means clustering.

v

The number of partitions of the data set.

...

Extra arguments passed on to stats::kmeans().

Value

A tibble with classes spatial_cv, 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 coords argument are used for k-means clustering of the data into disjointed sets, as outlined in Brenning (2012). These clusters are used as the folds for cross-validation. Depending on how the data are distributed spatially, there may not be an equal number of points in each fold.

References

A. Brenning, "Spatial cross-validation and bootstrap for the assessment of prediction rules in remote sensing: The R package sperrorest," 2012 IEEE International Geoscience and Remote Sensing Symposium, Munich, 2012, pp. 5372-5375, doi: 10.1109/IGARSS.2012.6352393.

Examples

# NOT RUN {
data(ames, package = "modeldata")
spatial_clustering_cv(ames, coords = c(Latitude, Longitude), v = 5)

# }