carto               package:SensoMineR               R Documentation

_P_r_e_f_e_r_e_n_c_e _M_a_p_p_i_n_g _T_e_c_h_n_i_q_u_e_s

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

     Performs preference mapping techniques based on multidimensional
     exploratory data analysis.

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

     carto(Mat, MatH, 
           level = 0, regmod = 1, coord = c(1, 2), asp = 1, 
           cex = 1.3, col = "steelblue4", font = 2, clabel = 0.8,
           label.j = FALSE, resolution = 200, nb.clusters = 0)

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

     Mat: a data frame corresponding to the axes of the map

    MatH: a data frame in which each row represent a product and each
          column represent the hedonic scores of a given  consumer for
          the products

   level: the number of standard deviations used in the calculation of
          the preference response surface for all the consumers

  regmod: the type of regression model used in the calculation of the
          preference response surface for all the consumers. 'regmod' =
          1: quadratic model,  'regmod' = 2: vector model, 'regmod' =
          3: circular model, 'regmod' = 4: elliptical model

   coord: a vector of length 2, the rank of the axis used to display
          the results if '"manual"' is not assigned to the 'option'
          parameter

     asp: if 1 is assigned to that parameter, the graphic displays are
          output in an orthonormal coordinate system

     cex: cf. function 'par' in the 'graphics' package

     col: cf. function 'par' in the 'graphics' package

    font: cf. function 'par' in the 'graphics' package

  clabel: cf. the 'ade4' package

 label.j: boolean, if T then the labels of the panelists who gave the
          hedonic scores are displayed

resolution: resolution of the map

nb.clusters: number of clusters to use (by default, 0 and the optimal
          numer of clusters is calculated

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

     The preference mapping methods are commonly used in the fields of
     market research  and research and development to explore and
     understand the structure and tendencies of consumer preferences, 
     to link consumer preference information to other data and to
     predict the behavior of consumers  in terms of acceptance of a
     given product.
      This function refers to the method introduced by M. Danzart. A
     response surface is computed per consumer; then according to
     certain threshold preference zones are delimited and finally
     superimposed.

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

     Franois Husson Franois.Husson@agrocampus-rennes.fr 
      Sbastien L Sebastien.Le@agrocampus-rennes.fr

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

     Danzart M., Sieffermann J.M., Delarue J. (2004). New developments
     in preference mapping techniques:  finding out a consumer optimal
     product, its sensory profile and the key sensory attributes.  _7th
     Sensometrics Conference, July 27-30, 2004, Davis, CA._

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

     'MFA', 'GPA'

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

     ## Example 1: carto for the sensory descriptors
     data(cocktail)
     res.pca <- PCA(senso.cocktail)
     carto(res.pca$ind$coord[,1:2], hedo.cocktail)

     ## Example 2
     ## Not run: 
     data(cocktail)
     res.mfa <- MFA(cbind.data.frame(senso.cocktail,compo.cocktail),
         group=c(ncol(senso.cocktail),ncol(compo.cocktail)),
         name.group=c("senso","compo"))
     carto(res.mfa$ind$coord[,1:2], hedo.cocktail)
     ## End(Not run)

