Relative proximities of several subsets of variables Yj with another set X. SUCCESSIVE SOLUTIONS
1  concorcano(x,y,py,r)

x 
is a n x p matrix of p centered variables 
y 
is a n x q matrix of q centered variables 
py 
is a row vector which contains the numbers qi, i=1,...,ky, of the ky subsets yi of y : ∑_i q_i=sum(py)=q. py is the partition vector of y 
r 
is the wanted number of successive solutions 
The first solution calculates a standardized canonical component cx[,1] of x associated to ky standardized components cyi[,1] of yi by maximizing ∑_i ρ(cx[,1],cy_i[,1])^2.
The second solution is obtained from the same criterion, with ky orthogonality constraints for having rho(cyi[,1],cyi[,2])=0 (that implies rho(cx[,1],cx[,2])=0). For each of the 1+ky sets, the r canonical components are 2 by 2 zero correlated.
The ky matrices (cx)'*cyi are triangular.
This function uses concor function.
list with following components
cx 
is n x r matrix of the r canonical components of x 
cy 
is n.ky x r matrix. The ky blocks cyi of the rows n*(i1)+1 : n*i contain the r canonical components relative to Yi 
rho2 
is a ky x r matrix; each column k contains ky squared canonical correlations ρ(cx[,k],cy_i[,k])^2 
Hanafi & Lafosse (2001) Generalisation de la regression lineaire simple pour analyser la dependance de K ensembles de variables avec un K+1 eme. Revue de Statistique Appliquee vol.49, n.1
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