tests and docs

Author: bbuchsbaum

R/distcalc.R

@@ -293,68 +293,6 @@ pairwise_dist.robustmahadist <- function(obj, X) {
 }
 
 
-#' Compute Second-Order Similarity Scores
-#'
-#' Calculates correlation-based \emph{second order similarity} between:
-#' \itemize{
-#'   \item A \strong{full NxN distance matrix} computed from \code{X} via \code{distfun}, and 
-#'   \item A \code{Dref} matrix (the "reference" dissimilarities).
-#' }
-#' For each row \code{i}, this excludes same-block comparisons by selecting \code{which(block_var != block_var[i])}.
-#'
-#' @param distfun An S3 distance object (see \code{\link{create_dist}}) 
-#'   specifying how to compute a pairwise distance matrix from \code{X}.
-#' @param X A numeric matrix (rows = observations, columns = features).
-#' @param Dref A numeric NxN reference matrix of dissimilarities (e.g., from an ROI mask or a prior).
-#' @param block_var A vector indicating block/group memberships for each row in \code{X}.
-#' @param method Correlation method: "pearson" or "spearman".
-#'
-#' @return A numeric vector of length \code{nrow(X)}, where each entry is
-#' the correlation (using \code{method}) between \code{distance_matrix[i, valid]} and
-#' \code{Dref[i, valid]}, with \code{valid = which(block_var != block_var[i])}.
-#'
-#' @details
-#' This function first calls \code{pairwise_dist(distfun, X)}, obtaining an NxN matrix 
-#' of \emph{all} pairwise distances. It does not do block-based exclusion internally. 
-#' Instead, for each row \code{i}, it excludes same-block rows from the correlation 
-#' by subsetting the distances to \code{valid_indices}.
-#'
-#' @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")
-#'
-#' @export
-second_order_similarity <- function(distfun, X, Dref, block_var, method=c("pearson", "spearman")) {
-  method <- match.arg(method)
-  
-  # 1) Compute a full NxN distance matrix from X
-  distance_matrix <- pairwise_dist(distfun, X)
-  
-  n <- nrow(X)
-  scores <- numeric(n)
-  
-  # 2) For each row i, exclude same-block comparisons
-  for (i in seq_len(n)) {
-    valid_indices <- which(block_var != block_var[i])
-    if (length(valid_indices) > 0) {
-      x_vec <- distance_matrix[i, valid_indices]
-      ref_vec <- Dref[i, valid_indices]
-      if (length(x_vec) > 1) {
-        scores[i] <- cor(x_vec, ref_vec, method = method)
-      } else {
-        scores[i] <- NA
-      }
-    } else {
-      scores[i] <- NA
-    }
-  }
-  
-  scores
-}