nclass: Best number of classes for categorizing a continuous variable

In babaknaimi/elsa: Entropy-Based Local Indicator of Spatial Association

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

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.

Value

An object with the same class as the input x

Author(s)

Babak Naimi [e-mail]

http://r-gis.net

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

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)

babaknaimi/elsa documentation built on Jan. 28, 2024, 10 a.m.