Calculate Kernel Weights

Create a weights list using a kernel function.

Usage

st_kernel_weights(
  nb,
  geometry,
  kernel = "uniform",
  threshold = critical_threshold(geometry),
  adaptive = FALSE,
  self_kernel = FALSE
)

Arguments

nb

an object of class nb e.g. created by st_contiguity() or st_knn().

geometry

the geometry an sf object.

kernel

One of "uniform", "gaussian", "triangular", "epanechnikov", or "quartic". See kernels for more.

threshold

a scaling threshold to be used in calculating

adaptive

default FALSE. If TRUE uses the maximum neighbor distance for each region as the threshold. Suppresses the threshold argument.

self_kernel

default FALSE. If TRUE applies the kernel function to the observed region.

Value

a list where each element is a numeric vector.

Details

By default st_kernel_weight() utilizes a critical threshold of the maximum neighbor distance using critical_threshold(). If desired, the critical threshold can be specified manually. The threshold will be passed to the underlying kernel.

See also

Other weights: st_inverse_distance(), st_nb_dists(), st_weights()

Examples

geometry <- sf::st_geometry(guerry)
nb <- st_contiguity(geometry)
nb <- include_self(nb)
res <- st_kernel_weights(nb, geometry)
#> ! Polygon provided. Using point on surface.
head(res, 3)
#> [[1]]
#> [1] 1.0 0.5 0.5 0.5 0.5
#> 
#> [[2]]
#> [1] 1.0 0.5 0.5 0.5 0.5 0.5 0.5
#> 
#> [[3]]
#> [1] 1.0 0.5 0.5 0.5 0.5 0.5 0.5
#>