indscal              package:SensoMineR              R Documentation

_C_o_n_s_t_r_u_c_t _t_h_e _I_n_d_s_c_a_l _m_o_d_e_l _f_o_r _N_a_p_p_i_n_g _d_a_t_a _t_y_p_e

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

     This version of the Indscal model is specially adapted to Napping
     data type, i.e. products (stimuli) are positioned on a tableclothe
     by panelists, then their coordinates are used as input for the
     Indscal model.

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

     indscal(matrice, matrice.illu = NULL, maxit = 200, coord = c(1,2), 
         eps = 1/10^5)

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

 matrice: a data frame of dimension (_p,2j_), where _p_ represents the
          number of products and _j_ the number  of panelists (two
          coordinates per panelist)

matrice.illu: a data frame with illustrative variables (with the same
          row.names in common as in 'matrice')

   maxit: the maximum number of iterations until the algorithm stops

   coord: a length 2 vector specifying the components to plot

     eps: a threshold with respect to which the algorithm stops, i.e.
          when the difference between  the criterion function at step
          _n_ and _n+1_ is less than 'eps'

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

     Returns a list including: 

       W: a matrix with the subject coordinates

  points: a matrix with the stimuli (individuals) coordinates

  subvar: a vector with the strain between each configuration and the
          stimuli configuration

      r2: the strain criterion


     The functions returns the three following graphs: 
      A stimuli representation, ie. a representation of the products
      A representation of the weights computed by the Indscal model.
      A correlation circle of the variables enhanced by illustrative
     variables (supplementary columns)

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

     Peter Ellis 
      Franois Husson

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

     Carroll, J.D. & J.J. Chang (1970). Analysis of individual
     differences in multidimensional scaling via an N-way
     generalization of "Eckart-Young" decomposition. _Psychometrika_,
     35, 283-319.

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

     'nappeplot', 'pmfa'

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

     ## Not run: 
     data(napping)
     nappeplot(napping.don)
     resindscal<- indscal(napping.don, napping.words)
     get(getOption("device"))()
     prefpls(cbind(resindscal$points, napping.words))
     get(getOption("device"))()
     pmfa(napping.don, napping.words, mean.conf = resindscal$points)
     ## End(Not run)

