elsa (version 1.1-28)

nclass: Best number of classes for categorizing a continuous variable

Description

This function explores the best number of classes to categorize (discretize) a continuous variable.

Usage

nclass(x,th,...)

Arguments

x

a RasterLayer or a numeric vector

th

A threshold (default = 0.005) used to find the best number of classes

...

Additional arguments; currently probs implemented that specifies which extreme values (outliers) should be ignored; specified as a percentile probabilities, e.g., c(0.005,0.995), default is NULL

Value

An object with the same class as the input x

Details

The function uses an approach introduced in Naimi et al. (under review), to find the best number of classes (categories) when a continuous variable is discretizing. The threhold is corresponding to the acceptable level of information loose through discretizing procedure. For the details, see the reference.

References

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.

Examples

# NOT RUN {
file <- system.file('external/dem_example.grd',package='elsa')
r <- raster(file)
plot(r,main='a continuous raster map')

nclass(r)

nclass(r, th=0.01)

nclass(r, th=0.1)
# }