Best number of classes for categorizing a continuous variable
This function explores the best number of classes to categorize (discretize) a continuous variable.
nclass(x,th,...)
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 |
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.
An object with the same class as the input x
Babak Naimi naimi.b@gmail.com
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.
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.