decat               package:SensoMineR               R Documentation

_D_E_s_c_r_i_p_t_i_o_n _o_f _C_A_T_e_g_o_r_i_e_s

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

     This function is designed to point out the variables that are the
     most characteristic according  to the set of products in its
     whole, and to each of the products in particular. 

     This function is designed to test the main effect of a categorical
     variable (F-test)  and the significance of its coefficients
     (T-test) for a set of endogenous variables  and a given analysis
     of variance model. In most cases, the main effect is the product
     effect and the endogenous variables are the sensory descriptors.

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

     decat(donnee, formul, firstvar, lastvar = length(colnames(donnee)), 
         proba = 0.05, graph = TRUE)

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

  donnee: a data frame made up of at least two qualitative variables 
          (_product_, _panelist_) and a set of quantitative variables
          (sensory descriptors)

  formul: the model that is to be tested

firstvar: the position of the first endogenous variable

 lastvar: the position of the last endogenous variable (by default the
          last column of 'donnee')

   proba: the significance threshold considered for the analyses of
          variance (by default 0.05)

   graph: a boolean, if TRUE a barplot of the P-values associated with
          the F-test of the product effet is displayed

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

     The 'formul' parameter must be filled in by an analysis of
     variance model and must  begin with the categorical variable of
     interest (e.g. the product effect) followed by the different other
     factors of interest (and their combinations). E.g.:'formul =
     "~Product+Panelist+Session"'.

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

     A list containing the following elements: 

    tabF: the V-test and the P-value of the F-test for each descriptor
          resulting from the analysis of variance model

    tabT: a (products,descriptors) data frame, in which each cell is
          the Vtest for a given product and a given descriptor

   coeff: a (products,descriptors) data frame, in which each cell is
          the coefficient resulting from the analysis of variance model
          for a given product and a given descriptor

    resF: the V-test and the P-value for each descriptor resulting from
          the analysis of variance model, sorted in ascending order

    resT: a list which elements are data frames, one data frame per
          product:  the coefficient, the P-value and the Vtest for each
          significant descriptor resulting from the analysis of
          variance model, sorted in descending order

     A barplot of the P-values associated with the F-test of the
     product effet.

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

     Franois Husson

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

     P. Lea, T. Naes, M. Rodbotten. _Analysis of variance for sensory
     data_.
      H. Sahai, M. I. Ageel. _The analysis of variance_.

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

     'aov'

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

     ### Example 1
     data(chocolates)
     ## model (AOV): " descriptor = product + panelist "
     resdecat<-decat(sensochoc, formul="~Product+Panelist", firstvar = 5)
     coltable(magicsort(resdecat$tabT), level.lower = -1.96, 
         level.upper = 1.96, main.title="Products' description")
     barrow(resdecat$tabT)
     barrow(t(resdecat$tabT), numr = 3, numc = 3)
     barrow(resdecat$coeff, color = "orange") 

     ### Example 2
     data(chocolates)
     ## model (AOV): " descriptor = product + panelist "
     res2 <-decat(sensochoc, formul="~Product+Panelist", firstvar = 5,proba=1)
     coltable(magicsort(res2$adjmean,res2$tabT), magicsort(res2$tabT), 
       level.lower =-1.96, level.upper = 1.96, main.title="Products' description")

