The neighborweights package provides a collection of functions for
constructing adjacency matrices based on spatial and feature-based
similarity between data points. It enables users to analyze and
visualize complex data relationships by creating spatial and
feature-weighted adjacency matrices using various methods.
You can install the neighborweights package from GitHub with:
# install.packages("devtools")
devtools::install_github("bbuchsbaum/neighborweights")##Usage
Here’s a basic example demonstrating how to create a spatial adjacency matrix using the spatial_adjacency function:
library(neighborweights)
# Generate random coordinates
coord_mat <- matrix(runif(100), nrow=10, ncol=2)
#> Warning in matrix(runif(100), nrow = 10, ncol = 2): data length differs from
#> size of matrix: [100 != 10 x 2]
# Calculate the spatial adjacency matrix
spatial_mat <- spatial_adjacency(coord_mat, nnk=5, sigma=1)
# Inspect the resulting matrix
print(spatial_mat)
#> 10 x 10 sparse Matrix of class "dgCMatrix"
#>
#> [1,] 0.2 0.1 . . 0.2 . 0.2 0.2 . 0.2
#> [2,] 0.1 0.2 . . 0.2 0.2 . . 0.2 .
#> [3,] . . 0.2 0.2 . 0.1 . . 0.1 0.1
#> [4,] . . 0.2 0.2 . 0.2 . . 0.2 0.1
#> [5,] 0.2 0.2 . . 0.2 . 0.1 0.1 . .
#> [6,] . 0.2 0.1 0.2 . 0.2 . . 0.2 0.1
#> [7,] 0.2 . . . 0.1 . 0.2 0.2 0.1 0.2
#> [8,] 0.2 . . . 0.1 . 0.2 0.2 0.1 0.2
#> [9,] . 0.2 0.1 0.2 . 0.2 0.1 0.1 0.2 0.2
#> [10,] 0.2 . 0.1 0.1 . 0.1 0.2 0.2 0.2 0.2For more advanced usage and additional examples, please refer to the package documentation and vignettes (coming soon).