Create a kernel weights by specifying k-nearest neighbors and a kernel method
Usage
st_kernel_knn_weights(
sfj,
k,
kernel = "gaussian",
power = 1,
adaptive_bandwidth = TRUE,
use_kernel_diagonals = FALSE,
is_inverse = FALSE,
unit = "km"
)Arguments
- sfj
An sf (simple feature) object.
- k
A positive integer number for k-nearest neighbors.
- kernel
(optional) A string value, which has to be one of 'triangular', 'uniform', 'epanechnikov', 'quartic', 'gaussian'(default).
- power
(optional) The power (or exponent) of a number indicates how many times to use the number in a multiplication.Default is 1.
- 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.
- is_inverse
(optional) FALSE (default) or TRUE, apply inverse on distance value.
- unit
(optional) The unit for calculating spatial distance, can be 'km'(default) or 'mile'.
Author
Wenbo Lv lyu.geosocial@gmail.com
Examples
guerry_path = system.file("extdata", "Guerry.shp", package = "rgeoda")
guerry = sf::read_sf(guerry_path)
st_kernel_knn_weights(guerry,6)
#> Reference class object of class "Weight"
#> Field "gda_w":
#> An object of class "p_GeoDaWeight"
#> Slot "pointer":
#> <pointer: 0x55c286e9b720>
#>
#> Field "is_symmetric":
#> [1] FALSE
#> Field "sparsity":
#> [1] 0.07058824
#> Field "min_neighbors":
#> [1] 6
#> Field "max_neighbors":
#> [1] 6
#> Field "num_obs":
#> [1] 85
#> Field "mean_neighbors":
#> [1] 6
#> Field "median_neighbors":
#> [1] 6
#> Field "has_isolates":
#> [1] FALSE