categorize: Categorizing continious data
In elsa: Entropy-Based Local Indicator of Spatial Association

Description Usage Arguments Details Value Author(s) References Examples

A function to categorize (discretize or binning) numerical data in the form of a raster layer, or a vector.

1	categorize(x,nc,probs,...)

`x`	A `RasterLayer` or a `numerical vector`
`nc`	Number of classes, if missing, it will be automatically detected
`probs`	a two-item numeric vector of the lower and upper probabilities within the range of [0,1], e.g., c(0.025, 0.975) (default: NULL) to consider a quantile range of values in categorization (to avoid the effects of outliers); if `NULL` or `FALSE`, then the whole range is used.
`...`	Additional parameters for `writeRaster` function (`filename` should be specified as additional argument)

If nc is not specified, the function calls the function nclass to find the best number of classes. For the details of how nclass works, see the reference.

When outliers do exist in x, it is likely that they affect the quality of categorization. To avoid their affects, a quantile with the specified probabilities can be used. default is recommended: c(0.025,0.975), but to ignore it, you can turn the probs argument to NULL.

`RasterLayer`	if x is a `RasterLayer`
`numeric vector`	if x is a numeric `vector`

Babak Naimi naimi.b@gmail.com

http://r-gis.net

Naimi, B., Hamm, N. A., Groen, T. A., Skidmore, A. K., Toxopeus, A. G., & Alibakhshi, S. (2019). ELSA: Entropy-based local indicator of spatial association. Spatial statistics, 29, 66-88.

categorize(1:10,3) # categorizeing a numeric vector into 3 categories

file <- system.file('external/dem_example.grd',package='elsa')
r <- raster(file)
plot(r,main='a continuous raster map')
rc <- categorize(r,nc=4)
plot(rc, main='categorized map')