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Kernel density estimator of points

Source: R/kernel.r
kernel.Rd

kernel() creates a raster using a kernel density estimator of the density of points in a "points" GVector.

Usage

# S4 method for class 'GVector'
kernel(x, y, kernel = "Epanechnikov", optimize = TRUE, h = NULL)

Arguments

x

A "points" GVector.

y

A GRaster: The extent and resolution of this raster will be used to create the density raster. Otherwise, values in this raster are ignored.

kernel

Character: Name of the kernel function to use. Possible values include:

  • "Epanechnikov" (default)

  • "Gaussian"

  • "uniform"

  • "triangular"

  • "quartic"

  • "triweight"

  • "cosine"

Partial matching is used, and case is ignored.

optimize

Logical: If TRUE (default), then attempt to find the optimal radius less than or equal to the radius value using the "Gaussian" kernel. If FALSE, use the radius value as-is.

h

Numeric or NULL (default): Smoothing bandwidth of kernel estimator.

If this is NULL, the Epanechnikov kernel is used, and optimize is TRUE, then Silverman's rule-of-thumb is used to estimate the optimal value of h: $$h = 0.9 * min(\sigma_x / n^1/6, \sigma_y / n^1/6)$$

If the Gaussian kernel is used, and optimize is TRUE, then the GRASS v.kernel function will attempt to identify the optimal bandwidth, up to the value of h, if h is defined.

Otherwise, if h is NULL, then the value will be arbitrarily set at 1/5th of the shorter of the distance of the x- and y-extent of the points.

Value

A GRaster.

Examples

if (grassStarted()) {

# Setup
library(sf)
library(terra)

# Elevation raster, plant specimen collections, rivers vector,
# outline of area vector
madElev <- fastData("madElev")
madDypsis <- fastData("madDypsis")

# Convert to fasterRaster format:
elev <- fast(madElev)
dypsis <- fast(madDypsis)

# Kernel density estimation:
kde <- kernel(dypsis, elev)
plot(kde)
plot(dypsis, add = TRUE, pch = 1)

}