View source: R/sperrorest_resampling.R
partition_kmeans | R Documentation |
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)
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