The four functions svdcp (cp for column partitioned), svdbip or svdbip2 (bip for bi-partitioned), and svdbips (s for a simultaneous optimization of one set of r solutions), correspond to a "SVD by blocks" notion, by supposing each block depending on relative subspaces, rather than on two whole spaces as usual SVD does. The other functions, based on this notion, are relative to two column partitioned data matrices x and y defining two sets of subsets xi and yj of variables and amount to estimate a link between xi and yj for the pair (xi, yj) relatively to the links associated to all the other pairs.
|Author||R. Lafosse <firstname.lastname@example.org>|
|Date of publication||2012-10-29 08:58:27|
|Maintainer||S. Djean <email@example.com>|
concor: Relative links of several subsets of variables
concorcano: Canonical analysis of several sets with another set
concoreg: Redundancy of sets yj by one set x
concorgm: Analyzing a set of partial links between Xi and Yj
concorgmcano: Canonical analysis of subsets Yj with subsets Xi
concorgmreg: Regression of subsets Yj by subsets Xi
concors: "simultaneous concorgm"
concorscano: "simultaneous concorgmcano"
concorsreg: "simultaneous concorgmreg"
svdbip: SVD for one bipartitioned matrix x
svdbip2: SVD for bipartitioned matrix x
svdbips: SVD for bipartitioned matrix x
svdcp: SVD for a Column Partitioned matrix x