canocor              package:calibrate              R Documentation

_C_a_n_o_n_i_c_a_l _c_o_r_r_e_l_a_t_i_o_n _a_n_a_l_y_s_i_s

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

     'canocor' performs canonical correlation analysis on the basis of
     the standardized variables and stores extensive output in a list
     object.

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

     canocor(X, Y)

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

       X: a matrix containing the X variables 

       Y: a matrix containing the Y variables 

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

     'canocor' computes the solution by a singular value  decomposition
     of the transformed between set correlation matrix.

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

     Returns a list with the following results 

   ccor : the canonical correlations 

      A : canonical weights of the x variables 

      B : canonical weights of the y variables 

      U : canonical x variates 

      V : canonical y variates 

     Fs : biplot markers for x variables (standard coordinates) 

     Gs : biplot markers for y variables (standard coordinates) 

     Fp : biplot markers for x variables (principal coordinates) 

     Gp : biplot markers for y variables (principal coordinates) 

 fitRxy : goodness of fit of the between-set correlation matrix 

  fitXs : adequacy coefficients of x variables 

  fitXp : redundancy coefficients of x variables 

  fitYs : adequacy coefficients of y variables 

  fitYp : redundancy coefficients of y variables

_A_u_t_h_o_r(_s):

     Jan Graffelman jan.graffelman@upc.edu

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

     Hotelling, H. (1935) The most predictable criterion. Journal of
     Educational  Psychology (26) pp. 139-142.

     Hotelling, H. (1936) Relations between two sets of variates.
     Biometrika (28) pp. 321-377.

     Johnson, R. A. and Wichern, D. W. (2002) Applied Multivariate
     Statistical Analysis. New Jersey: Prentice Hall.

_S_e_e _A_l_s_o:

     'cancor'

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

     set.seed(123)
     X <- matrix(runif(75),ncol=3)
     Y <- matrix(runif(75),ncol=3)
     cca.results <- canocor(X,Y)

