spatial_clustering_cv {spatialsample} | R Documentation |
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.
spatial_clustering_cv(data, coords, v = 10, ...)
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 |
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.
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
.
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.
data(ames, package = "modeldata") spatial_clustering_cv(ames, coords = c(Latitude, Longitude), v = 5)