feature_weighted_spatial_constraints: Construct Feature-Weighted Spatial Constraints for Data...

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

Construct Feature-Weighted Spatial Constraints for Data Blocks

Description

This function creates a sparse matrix of feature-weighted spatial constraints for a set of data blocks. The feature-weighted spatial constraints matrix is useful in applications like image segmentation and analysis, where both spatial and feature information are crucial for identifying different regions in the image.

Usage

feature_weighted_spatial_constraints(
  coords,
  feature_mats,
  sigma_within = 5,
  sigma_between = 3,
  wsigma_within = 0.73,
  wsigma_between = 0.73,
  alpha_within = 0.5,
  alpha_between = 0.5,
  shrinkage_factor = 0.1,
  nnk_within = 27,
  nnk_between = 27,
  maxk_within = nnk_within,
  maxk_between = nnk_between,
  weight_mode_within = "heat",
  weight_mode_between = "binary",
  variable_weights = rep(1, ncol(coords) * length(feature_mats)),
  verbose = FALSE
)

Arguments

coords	The spatial coordinates as a matrix with rows as objects and columns as dimensions.
feature_mats	A list of feature matrices, one for each data block.
sigma_within	The bandwidth of the within-block smoother. Default is 5.
sigma_between	The bandwidth of the between-block smoother. Default is 3.
wsigma_within	The bandwidth of the within-block feature weights. Default is 0.73.
wsigma_between	The bandwidth of the between-block feature weights. Default is 0.73.
alpha_within	The scaling factor for within-block feature weights. Default is 0.5.
alpha_between	The scaling factor for between-block feature weights. Default is 0.5.
shrinkage_factor	The amount of shrinkage towards the spatial block average. Default is 0.1.
nnk_within	The maximum number of nearest neighbors for within-block smoother. Default is 27.
nnk_between	The maximum number of nearest neighbors for between-block smoother. Default is 27.
maxk_within	The maximum number of nearest neighbors for within-block computation. Default is 'nnk_within'.
maxk_between	The maximum number of nearest neighbors for between-block computation. Default is 'nnk_between'.
weight_mode_within	The within-block nearest neighbor weight mode ("heat" or "binary"). Default is "heat".
weight_mode_between	The between-block nearest neighbor weight mode ("heat" or "binary"). Default is "binary".
variable_weights	A vector of per-variable weights. Default is a vector of ones with length equal to the product of the number of columns in the 'coords' matrix and the length of 'feature_mats'.
verbose	A boolean indicating whether to print progress messages. Default is FALSE.

Value

A sparse matrix representing the feature-weighted spatial constraints for the provided data blocks.

Details

The function computes within-block and between-block constraints based on the provided coordinates, feature matrices, and other input parameters. It balances the within-block and between-block constraints using a shrinkage factor, and normalizes the resulting matrix by the first eigenvalue. The function also takes into account the weights of the variables in the provided feature matrices.

Examples

coords <- as.matrix(expand.grid(1:10, 1:10))
fmats <- replicate(20, matrix(rnorm(100*10), 10, 100), simplify=FALSE)
conmat <- feature_weighted_spatial_constraints(coords, fmats)

conmat <- feature_weighted_spatial_constraints(coords, fmats, maxk_between=4, maxk_within=2,sigma_between=5, nnk_between=60)

bbuchsbaum/graphweights documentation built on April 4, 2024, 7:19 p.m.