cpa                package:SensoMineR                R Documentation

_C_o_n_s_u_m_e_r_s' _P_r_e_f_e_r_e_n_c_e_s _A_n_a_l_y_s_i_s

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

     Performs preference mapping techniques based on multidimensional
     exploratory data analysis. This methodology is oriented towards
     consumers' preferences; here consumers are pictured according only
     to their preferences.  In this manner, the distance between two
     consumers is very natural and easy to interpret, and a clustering
     of  the consumers is also very easy to obtain.

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

     cpa(senso, hedo, coord=c(1,2), center = TRUE, scale = TRUE, 
         nb.clusters = 0, scale.unit = FALSE, name.panelist = TRUE,
         col = terrain.colors(45)[1:41])

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

   senso: a data frame of dimension (_p,k_), where _p_ is the number of
          products and _k_ the number of sensory descriptors

    hedo: a data frame of dimension (_p,j_), where _p_ is the number of
          products and _j_ the number of consumers or panelists

   coord: a length 2 vector specifying the components to plot

  center: boolean, if TRUE then data are mean centered

   scale: boolean, if TRUE then data are scaled to unit variance

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

scale.unit: boolean, if TRUE then PCA is made on scaled data

name.panelist: boolean, if TRUE then the name of the panelist is
          written

     col: color palette

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

     This methodology is oriented towards consumers' preferences; here,
     consumers are pictured according only to their preferences.  In
     this manner, the distance between two consumers is very natural
     and easy to interpret,  and a clustering of the consumers is also
     very easy to obtain using a classic hierarchical  clustering
     procedure performed on Euclidian distances with the Ward's minimum
     variance criterion.  The originality of the representation is that
     the characteristics of the products  are also superimposed to the
     former picture.

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

     Return the following results: 

clusters: the cluster number allocated to each consumer

  result: the coordinates of the panelists, of the clusters, of the
          archetypes

prod.clusters: a list with as many elements as there are clusters; each
          element of the list gathers the specific products for its
          corresponding cluster

des.clusters: the correlation coefficients between the average hedonic
          scores per cluster and the sensory descriptors

     A dendogram which highlight the clustering, a correlation circle
     that displays the hedonic scores, a graph of the consumers such as
     two consumers are all  the more close that they do like the same
     products, as many graphs as there are variables: for a given
     variable,  each consumer is colored according to the coefficient
     of correlation based on his hedonic scores and the variable.

_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:

     S. L, F. Husson, J. Pags (2005). Another look at sensory data:
     how to "have your salmon and eat it, too!".  _6th Pangborn sensory
     science symposium, August 7-11, 2005, Harrogate, UK._

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

     ## Not run: 
     data(cocktail)
     res.cpa = cpa(cbind(compo.cocktail, senso.cocktail), hedo.cocktail)
     ## If you prefer a graph in black and white and with 3 clusters
     res.cpa = cpa(cbind(compo.cocktail, senso.cocktail), hedo.cocktail, 
         name.panelist = TRUE, col = gray((50:1)/50), nb.clusters = 3)
     ## End(Not run)

