kernel_knn_weights: K-NN Kernel Spatial Weights

In rgeoda: R Library for Spatial Data Analysis

K-NN Kernel Spatial Weights

Description

Create a kernel weights by specifying k-nearest neighbors and a kernel method

Usage

kernel_knn_weights(
  sf_obj,
  k,
  kernel_method,
  adaptive_bandwidth = TRUE,
  use_kernel_diagonals = FALSE,
  power = 1,
  is_inverse = FALSE,
  is_arc = FALSE,
  is_mile = TRUE
)

Arguments

sf_obj	An sf (simple feature) object
k	a positive integer number for k-nearest neighbors
kernel_method	a string value, which has to be one of 'triangular', 'uniform', 'epanechnikov', 'quartic', 'gaussian'
adaptive_bandwidth	(optional) TRUE (default) or FALSE: TRUE use adaptive bandwidth calculated using distance of k-nearest neithbors, FALSE use max distance of all observation to their k-nearest neighbors
use_kernel_diagonals	(optional) FALSE (default) or TRUE, apply kernel on the diagonal of weights matrix
power	(optional) The power (or exponent) of a number says how many times to use the number in a multiplication.
is_inverse	(optional) FALSE (default) or TRUE, apply inverse on distance value
is_arc	(optional) FALSE (default) or TRUE, compute arc distance between two observations
is_mile	(optional) TRUE (default) or FALSE, convert distance unit from mile to km.

Value

An instance of Weight-class

Examples

library(sf)
guerry_path <- system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry <- st_read(guerry_path)
adptkernel_w = kernel_knn_weights(guerry, 6, "uniform")
summary(adptkernel_w)

rgeoda documentation built on April 3, 2025, 10:55 p.m.