A similarity index for comparing coupled matrices

Sammendrag

Application of different multivariate measurement technologies to the same set
of samples is an interesting challenge in many fields of applied data analysis.
Our proposal is a 2‐stage similarity index framework for comparing 2 matrices
in this type of situation. The first step is to identify factors (and associated
subspaces) of the matrices by methods such as principal component analysis
or partial least squares regression to provide good (low ‐dimensional) summa-
ries of their information content. Thereafter, statistical significances are
assigned to the similarity values obtained at various factor subset combinations
by considering orthogonal projections or Procrustes rotations and how to
express the results compactly in correspond ing summary plots. Applications
of the methodology include the investigation of redundancy in spectroscopic
data and the investigation of assessor consistency or deviations in sensory
science. The proposed methodology is implemented in the R‐package
“MatrixCorrelation ” available online from CRAN.