SpatialPack (version 0.4-1)

codisp.ks: A Nadaraya-Watson Codispersion Coefficient

Description

Computes a nonparametric version of the codispersion coefficient between two spatial variables using a Nadaraya-Watson estimator.

Usage

codisp.ks(x, y, coords, lags, kernel = "epanech", bandwidths)

Value

A vector with the semivariogram for each variable, the crossed semivariogram and the codispersion coefficient.

Arguments

x

an \(n\)-dimensional vector of data values.

y

an \(n\)-dimensional vector of data values.

coords

an \(n\)-by-2 matrix containing coordinates of the \(n\) data locations in each row.

lags

a 2D vector of spatial lags.

kernel

character string which determines the smoothing kernel. kernel can be: "uniform" - a rectangular box. "epanech" - the Epanechnikov kernel or centred Beta(2,2) density (the default). "gaussian" - the Gaussian density function. "biweight" - quartic or biweight kernel. "triangular" - the triangular distribution.

bandwidths

a 3D vector with the kernel bandwidth smoothing parameters.

Details

The procedure computes the codispersion coefficient for two spatial variables which is based on a Nadaraya-Watson version of the codispersion coefficient through a suitable kernel.

References

Cuevas, F., Porcu, E., Vallejos, R. (2013). Study of spatial relationships between two sets of variables: A nonparametric approach. Journal of Nonparametric Statistics 25, 695-714.

Vallejos, R., Osorio, F., Bevilacqua, M. (2020). Spatial Relationships Between Two Georeferenced Variables: With Applications in R. Springer, Cham.

Examples

# Pinus Radiata dataset
data(radiata)

# defining basal-area and height variables from the Pinus Radiata dataset
x <- radiata$basal
y <- radiata$height

# extracting the coordinates from Pinus Radiata dataset
coords <- radiata[,1:2]

# computing the codispersion coefficient
bwds <- c(174, 247, 187)
cf <- codisp.ks(x, y, coords, lags = c(200,200), kernel = "epanech", bandwidths = bwds)
cf