partition_kmeans
divides the study area into irregularly shaped
spatial partitions based on k-means (kmeans) clustering
of spatial coordinates.
partition_kmeans(data, coords = c("x", "y"), nfold = 10, repetition = 1, seed1 = NULL, return_factor = FALSE, balancing_steps = 1, order_clusters = TRUE, ...)
data |
|
---|---|
coords | vector of length 2 defining the variables in |
nfold | number of cross-validation folds, i.e. parameter k in k-means clustering. |
repetition | numeric vector: cross-validation repetitions
to be generated. Note that this is not the number of repetitions,
but the indices of these repetitions. E.g., use |
seed1 |
|
return_factor | if |
balancing_steps | if |
order_clusters | if |
... | additional arguments to kmeans. |
A represampling object, see also partition_cv for details.
Default parameter settings may change in future releases.
Brenning, A., Long, S., & Fieguth, P. (2012). Detecting rock glacier flow structures using Gabor filters and IKONOS imagery. Remote Sensing of Environment, 125, 227-237. doi:10.1016/j.rse.2012.07.005
Russ, G. & A. Brenning. 2010a. Data mining in precision agriculture: Management of spatial information. In 13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010; Dortmund; 28 June - 2 July 2010. Lecture Notes in Computer Science, 6178 LNAI: 350-359.
sperrorest, partition_cv, partition_disc, partition_tiles, kmeans
data(ecuador) resamp <- partition_kmeans(ecuador, nfold = 5, repetition = 2) # plot(resamp, ecuador)