`R/sperrorest_resampling.R`

`partition_kmeans.Rd`

`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
`data.frame`

containing at least the columns specified by`coords`

- coords
vector of length 2 defining the variables in

`data`

that contain the x and y coordinates of sample locations.- 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

`repetition = c(1:100)`

to obtain (the 'first') 100 repetitions, and`repetition = c(101:200)`

to obtain a different set of 100 repetitions.- seed1
`seed1+i`

is the random seed that will be used by set.seed in repetition`i`

(`i`

in`repetition`

) to initialize the random number generator before sampling from the data set.- return_factor
if

`FALSE`

(default), return a represampling object; if`TRUE`

(used internally by other sperrorest functions), return a`list`

containing factor vectors (see Value)- balancing_steps
if

`> 1`

, perform`nfold`

-means clustering`balancing_steps`

times, and pick the clustering that minimizes the Gini index of the sample size distribution among the partitions. The idea is that 'degenerate' partitions will be avoided, but this also has the side effect of reducing variation among partitioning repetitions. More meaningful constraints (e.g., minimum number of positive and negative samples within each partition should be added in the future.- order_clusters
if

`TRUE`

, clusters are ordered by increasing x coordinate of center point.- ...
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.

```
data(ecuador)
resamp <- partition_kmeans(ecuador, nfold = 5, repetition = 2)
# plot(resamp, ecuador)
```