concorscano              package:concor              R Documentation

"_s_i_m_u_l_t_a_n_e_o_u_s _c_o_n_c_o_r_g_m_c_a_n_o"

_D_e_s_c_r_i_p_t_i_o_n:

     concorgmcano with the set of r solutions simultaneously optimized

_U_s_a_g_e:

     concorscano(x,px,y,py,r)

_A_r_g_u_m_e_n_t_s:

       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)

_D_e_t_a_i_l_s:

     This function uses the concors function

_V_a_l_u_e:

     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

_R_e_f_e_r_e_n_c_e_s:

     See svdbips

_E_x_a_m_p_l_e_s:

     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,]

