README.md

In bbuchsbaum/neighborweights: Constructing Adjacency Matrices Based on Spatial and Feature Similarity

neighborweights

Overview

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.

Installation

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 0.2

For more advanced usage and additional examples, please refer to the package documentation and vignettes (coming soon).

bbuchsbaum/neighborweights documentation built on April 1, 2024, 8:41 p.m.