concorscano {concor} | R Documentation |
concorgmcano with the set of r solutions simultaneously optimized
concorscano(x,px,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 |
px |
is a row vector which contains the numbers pi, i=1,...,kx, of the kx subsets xi of x : sum_i p_i=sum(px)=p. px is the partition vector of x |
py |
is the partition vector of y with ky subsets yj, j=1,...,ky |
r |
is the wanted number of successive solutions rmax <= min(min(px),min(py),n) |
This function uses the concors function
list with following components
cx |
is a n.kx x r matrix of kx row blocks cxi (n x r). Each row block contains r partial canonical components |
cy |
is a n.ky x r matrix of ky row blocks cyj (n x r). Each row block contains r partial canonical components |
rho2 |
is a kx x ky x r array; for a fixed solution k, rho2[,,k] contains kxky squared correlations rho(cx[n*(i-1)+1:n*i,k],cy[n*(j-1)+1:n*j,k])^2, simultaneously calculated between all the yj with all the xi |
See svdbips
x<-matrix(runif(50),10,5);y<-matrix(runif(90),10,9) x<-scale(x);y<-scale(y) cca<-concorscano(x,c(2,3),y,c(3,2,4),2) diag(t(cca$cx[1:10,])%*%cca$cy[1:10,]/10)^2 cca$rho2[1,1,]