panellipse            package:SensoMineR            R Documentation

_C_o_n_f_i_d_e_n_c_e _e_l_l_i_p_s_e_s _a_r_o_u_n_d _p_r_o_d_u_c_t_s _b_a_s_e_d _o_n _p_a_n_e_l_i_s_t_s _d_e_s_c_r_i_p_t_i_o_n_s

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

     Virtual panels are generated using Boostrap techniques in order to
     display confidence ellipses around products.

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

     panellipse(donnee, col.p, col.j, firstvar, lastvar = ncol(donnee), 
         alpha = 0.05, coord = c(1,2), scale.unit = TRUE, nbsimul = 500, 
         nbchoix = NULL, group = NULL, name.group = NULL, 
         level.search.desc = 0.5, centerbypanelist = TRUE, 
         scalebypanelist = FALSE, name.panelist = FALSE, cex = 1, 
         color = NULL)

_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)

   col.p: the position of the _product_ variable

   col.j: the position of the _panelist_ variable

firstvar: the position of the first sensory descriptor

 lastvar: the position of the last sensory descriptor (by default the
          last column of 'donnee')

   alpha: the confidence level of the ellipses

   coord: a length 2 vector specifying the components to plot

scale.unit: boolean, if T the descriptors are scaled to unit variance

 nbsimul: the number of simulations (corresponding to the number of
          virtual panels) used to compute the ellipses

 nbchoix: the number of panelists forming a virtual panel, by default
          the number of panelists in the original panel

   group: the number of variables in each group of variables when
          multiple factor analysis is performed (by default this
          parameter equals NULL and a PCA is performed)

name.group: the names of the groups of variables when mfa is performed
          (if 'group' differs from NULL)

level.search.desc: the threshold above which a descriptor is not
          considered as discriminant according to AOV model
          '"descriptor=Product+Panelist"'

centerbypanelist: boolean, if T center the data by panelist before the
          construction of the axes

scalebypanelist: boolean, if T scale the data by panelist before the
          construction of the axes (by default, FALSE is assigned to
          that parameter)

name.panelist: boolean, if T then the name of each panelist is
          displayed on the 'plotpanelist' graph (by default, FALSE is
          assigned to that parameter)

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

   color: a vector with the colors used; by default there are 35 colors
          defined

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

     Panellipse, step by step:
      Step 1 Performs a selection of discriminating descriptors with
     respect to a threshold set by users 
      Step 2 Virtual panels are generated using Boostrap techniques;
     the number of panels as well as their size are set by users with
     the _nbsimul_ and _nbchoix_ parameters 
      Step 3 Coordinates of the products with respect to each virtual
     panels are computed 
      Step 4 Each product is then circled by its confidence ellipse
     generated by virtual panels and comprising (1-alpha)*100 percent
     of the virtual products

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

     A list containing the following elements: 

     eig: a matrix with the component of the factor analysis (in row)
          and the eigenvalues, the inertia and the cumulative inertia
          for each component

coordinates: a list with: the coordinates of the products with respect
          to the panel and to each panelists and the coordinates of the
          _partial_ products with respect to the panel and to each
          panelists

hotelling: Returns a matrix with the P-values of the Hotelling's T2
          tests for each pair of products: this matrix allows to find
          the product which are significatnly different for the
          2-components sensory description; if an MFA is done,
          _hotelling_ returns as many matrices  as there are group,
          these matrices allows to find the product which are
          significantly different for the 2-components sensory
          description of the group, and it returns also a _global_
          matrix corresponding to the P-values for the tests
          corresponding to the mean product.


     Returns a graph of the products as well as a correlation circle of
     the descriptors.

     Returns a graph where each product is displayed with respect to a
     panel and to each panelist composing the panel; products described
     by the panel are displayed as square, they are displayed as circle
     when they are described by each panelist.

     Returns a graph where each product is circled by its confidence
     ellipse generated by virtual panels. When a Multiple Factor
     Analysis is performed, returns a graph where each partial product
     is circled by its confidence ellipse generated by virtual panels.

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

     Franois Husson

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

     Husson F., Le Dien S. & Pags J.  (2005). Confidence ellipse for
     the sensory profiles obtained by Principal Components Analysis.
     _Food Quality and Preference_.  16 (3), 245-250. 
      Pags J. & Husson F. (2005). Multiple Factor Analysis with
     confidence ellipses: a methodology to study the relationships
     between sensory and instrumental data. To be published in _Journal
     of Chemometrics_.

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

     'panellipse.session', 'panelmatch'

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

     ## Not run: 
     ## Example 1: PCA
     data(chocolates)
     res <- panellipse(sensochoc, col.p = 4, col.j = 1, firstvar = 5)
     coltable(res$hotelling, main.title = "P-values for the Hotelling's T2 tests")

     ## If we consider only 12 panelists in a virtual panel, 
     ## what would be the size of the ellipses
     res2 <- panellipse(sensochoc, col.p = 4, col.j = 1, nbchoix = 12, firstvar = 5)
     coltable(res2$hotelling, main.title = "P-values for the Hotelling's T2 tests")

     ## If we want the confidence ellipses around the individual descriptions
     panellipse(sensochoc, col.p = 4, col.j = 1, nbchoix = 1, firstvar = 5)

     ## Example 2: MFA
     data(chocolates)
     res <- panellipse(sensochoc, col.p = 4, col.j = 1, firstvar = 5, 
         group = c(6,8), name.group = c("G1","G2"))
     for (i in 1:dim(res$hotelling$bygroup)[3]) coltable(res$hotelling$bygroup[,,i], 
         main.title = paste("P-values for the Hotelling's T2 tests (",
         dimnames(res$hotelling$bygroup)[3][[1]][i],")",sep=""))
     ## End(Not run)

