rda                package:calibrate                R Documentation

_R_e_d_u_n_d_a_n_c_y _a_n_a_l_y_s_i_s

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

     'rda' performs redundancy analysis and stores extensive output in
     a list object.

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

     rda(X, Y, scaling = 1)

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

       X: a matrix of x variables

       Y: a matrix of y variables

 scaling: scaling used for x and y variables. 0: x and y only centered.
          1: x and y standardized

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

     Results are computed by doing a principal component analyis of the
     fitted values of the regression of y on x.

     Plotting the first two columns of Gxs and Gyp, or of Gxp and Gys
     provides a biplots of  the matrix of regression coefficients.

     Plotting the first two columns of Fs and Gp or of Fp and Gs
     provides a biplot of the matrix of fitted values.

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

     Returns a list with the following results 

      Yh: fitted values of the regression of y on x 

       B: regression coefficients of the regresson of y on x 

   decom: variance decomposition/goodness of fit of the fitted values
          AND of the regression coefficients 

      Fs: biplot markers of the rows of Yh (standard coordinates) 

      Fp: biplot markers of the rows of Yh (principal coordinates) 

     Gys: biplot markers for the y variables (standard coordinates) 

     Gyp: biplot markers for the y variables (principal coordinates) 

     Gxs: biplot markers for the x variables (standard coordinates) 

     Gxp: biplot markers for the x variables (principal coordinates) 

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

     Jan Graffelman (jan.graffelman@upc.edu)

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

     Van den Wollenberg, A.L. (1977) Redundancy Analysis, an
     alternative for canonical  correlation analysis. Psychometrika
     42(2): pp. 207-219.

     Ter Braak, C. J. F. and Looman, C. W. N. (1994) Biplots in
     Reduced-Rank Regression. Biometrical Journal 36(8): pp. 983-1003.

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

     'princomp','canocor','biplot'

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

     X <- matrix(rnorm(75),ncol=3)
     Y <- matrix(rnorm(75),ncol=3)
     rda.results <- rda(X,Y)

