partition_kmeans {sperrorest} | R Documentation |
Partition samples spatially using k-means clustering of the coordinates
Description
partition_kmeans
divides the study area into irregularly
shaped spatial partitions based on k-means (kmeans) clustering of
spatial coordinates.
Usage
partition_kmeans(
data,
coords = c("x", "y"),
nfold = 10,
repetition = 1,
seed1 = NULL,
return_factor = FALSE,
balancing_steps = 1,
order_clusters = TRUE,
...
)
Arguments
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. |
Value
A represampling object, see also partition_cv for details.
Note
Default parameter settings may change in future releases.
References
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.
See Also
sperrorest, partition_cv, partition_disc, partition_tiles, kmeans
Examples
data(ecuador)
resamp <- partition_kmeans(ecuador, nfold = 5, repetition = 2)
# plot(resamp, ecuador)