second_order_similarity: Compute Second-Order Similarity Scores

In bbuchsbaum/rMVPA: Multivoxel Pattern Analysis in R

Compute Second-Order Similarity Scores

Description

Calculates correlation-based second order similarity between:

• A full NxN distance matrix computed from X via distfun, and

• A Dref matrix (the "reference" dissimilarities).

For each row i, this excludes same-block comparisons by selecting which(block_var != block_var[i]).

Usage

second_order_similarity(
  distfun,
  X,
  Dref,
  block_var,
  method = c("pearson", "spearman")
)

Arguments

distfun	An S3 distance object (see create_dist) specifying how to compute a pairwise distance matrix from X.
X	A numeric matrix (rows = observations, columns = features).
Dref	A numeric NxN reference matrix of dissimilarities (e.g., from an ROI mask or a prior).
block_var	A vector indicating block/group memberships for each row in X.
method	Correlation method: "pearson" or "spearman".

Details

This function first calls pairwise_dist(distfun, X), obtaining an NxN matrix of all pairwise distances. It does not do block-based exclusion internally. Instead, for each row i, it excludes same-block rows from the correlation by subsetting the distances to valid_indices.

Value

A numeric vector of length nrow(X), where each entry is the correlation (using method) between distance_matrix[i, valid] and Dref[i, valid], with valid = which(block_var != block_var[i]).

Examples

# Suppose we have X (10x5), a reference D (10x10), block var, and a correlation distfun:
X <- matrix(rnorm(50), 10, 5)
D <- matrix(runif(100), 10, 10)
block <- rep(1:2, each=5)
dist_obj <- cordist(method="pearson")
scores <- second_order_similarity(dist_obj, X, D, block, method="spearman")

bbuchsbaum/rMVPA documentation built on June 10, 2025, 8:23 p.m.