  There are two popular general approaches for the analysis and visualization
of a contingency table and a compositional data set: Correspondence analysis
(CA) and log ratio analysis (LRA). LRA includes two independently well
developed methods: association models and compositional data analysis. The
application of either CA or LRA to a contingency table or to compositional data
set includes a preprocessing centering step. In CA the centering step is
multiplicative, while in LRA it is log bi-additive. A preprocessed matrix is
double-centered, so it is a residuel matrix; which implies that it affects the
final results of the analysis. This paper introduces a novel index named the
intrinsic measure of the quality of the signs of the residuals (QSR) for the
choice of the preprocessing, and consequently of the method. The criterion is
based on taxicab singular value decomposition (TSVD) on which the package
TaxicabCA in R is developed. We present a minimal R script that can be executed
to obtain the numerical results and the maps in this paper. Three relatively
small sized data sets available freely on the web are used as examples.
