Constructs a symmetric dissimilarity matrix that accounts for missing-data patterns. Within blocks where both observations share a modality, standard Euclidean distances are used. Optionally, for observations without shared observed features (based on modality), a rank-based dissimilarity is computed (if skip = 0).
Blockdist(data, m, n, d, ptn_list, mod_id, modality, mod_bound, skip = 1)Numeric symmetric matrix (N × N) of pairwise dissimilarities.
List with X and Y matrices.
Integer. Number of rows (observations) in X.
Integer. Number of rows in Y.
Integer. Number of features (columns).
List of integer vectors: each element indexes observations sharing the same missing pattern.
Binary matrix (N × modality) indicating modality membership per observation.
Integer. Number of modalities.
Integer vector. Feature indices boundaries per modality block.
Integer (0 or 1). If set to 1, dissimilarity for modality-disjoint pairs is skipped. If 0, computed rank-based distances are used.