HMFA               package:FactoMineR               R Documentation

_H_i_e_r_a_r_c_h_i_c_a_l _M_u_l_t_i_p_l_e _F_a_c_t_o_r _A_n_a_l_y_s_i_s

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

     Performs a hierarchical multiple factor analysis, using an object
     of class 'list' of 'data.frame'.

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

     HMFA(X,H,type = rep("s", length(H[[1]])), ncp = 5, graph = TRUE)

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

       X: a 'data.frame'

       H: a list with one vector for each hierarchical level; in each
          vector the number of variables or the number of group
          constituting the group

    type: the type of variables in each group in the first partition;
          three possibilities: "c" or "s" for quantitative variables
          (the difference is that for "s", the variables are scaled in
          the program), "n" for qualitative variables; by default, all
          the variables are quantitative and the variables are scaled
          unit

     ncp: number of dimensions kept in the results (by default 5)

   graph: boolean, if TRUE a graph is displayed

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

     Returns a list including: 

     eig: a numeric vector with the all eigenvalues

   group: a list of matrices with all the results for the groups (Lg
          and RV coefficients, coordinates, square cosine,
          contributions, distance to the origin, the correlations
          between each group and each factor)

     ind: a list of matrices with all the results for the active
          individuals (coordinates, square cosine, contributions)

quanti.var: a list of matrices with all the results for the
          quantitative variables (coordinates, correlation between
          variables and axes)

quali.var: a list of matrices with all the results for the
          supplementary qualitative variables (coordinates of each
          categories of each variables, and v.test which is a criterion
          with a Normal distribution)

 partial: a list of arrays with the coordinates of the partial points
          for each partition

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

     Sbastien L, Franois Husson

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

     Le Dien, S. & Pags, J. (2003) Hierarchical Multiple factor
     analysis: application to the comparison of sensory profiles, _Food
     Quality and Preferences_, *18 (6)*, 453-464.

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

      
     data(wine)
     hierar <- list(c(2,5,3,10,9,2), c(4,2))
     res.hmfa <- HMFA(wine, H = hierar, type=c("n",rep("s",5)))

