GPA                package:FactoMineR                R Documentation

_G_e_n_e_r_a_l_i_s_e_d _P_r_o_c_r_u_s_t_e_s _A_n_a_l_y_s_i_s

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

     Performs Generalised Procrustes Analysis (GPA) that takes into
     account missing values.

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

     GPA(df, tolerance=10^-10, nbiteration=200, scale=TRUE, 
         coord=c(1,2), group, name.group = NULL, graph = TRUE)

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

      df: a data frame with _n_ rows (individuals) and _p_ columns
          (quantitative varaibles)

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

nbiteration: the maximum number of iterations until the algorithm stops

   scale: a boolean, if TRUE (which is the default value) scaling is
          required

   coord: a length 2 vector specifying the components to plot

   group: a vector indicating the number of variables in each group

name.group: a vector indicating the name of the groups (the groups are
          successively named  group.1, group.2 and so on, by default)

   graph: boolean, if TRUE a graph is displayed

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

     Performs a Generalised Procrustes Analysis (GPA) that takes into
     account missing values: some data frames of 'df' may have non
     described or non evaluated rows, i.e. rows with missing  values
     only.
      The algorithm used here is the one developed by Commandeur.

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

     A list containing the following components: 

      RV: a matrix of RV coefficients between partial configurations

     RVs: a matrix of standardized RV coefficients between partial
          configurations

    simi: a matrix of Procrustes similarity indexes between partial
          configurations

 scaling: a vector of isotropic scaling factors

     dep: an array of initial partial configurations 

consensus: a matrix of consensus configuration

    Xfin: an array of partial configurations after transformations

correlations: correlation matrix between initial partial configurations
          and consensus dimensions

  PANOVA: a list of "Procrustes Analysis of Variance" tables, per
          assesor (config), per product(objet), per dimension
          (dimension)

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

     Elisabeth Morand

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

     Commandeur, J.J.F (1991) _Matching configurations_.DSWO press,
     Leiden University.
      Dijksterhuis, G. & Punter, P. (1990) Interpreting generalized
     procrustes analysis "Analysis of Variance" tables,  _Food Quality
     and Preference_, *2*, 255-265
      Gower, J.C (1975) Generalized Procrustes analysis,
     _Psychometrika_, *40*, 33-50 
      Kazi-Aoual, F., Hitier, S., Sabatier, R., Lebreton, J.-D., (1995)
     Refined approximations to permutations tests  for multivariate
     inference. Computational Statistics and Data Analysis, *20*,
     643-656 
      Qannari, E.M., MacFie, H.J.H, Courcoux, P. (1999) Performance
     indices and isotropic scaling factors in sensory profiling, _Food
     Quality and Preference_, *10*, 17-21

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

     ## Not run: 
     data(wine)
     res.gpa <- GPA(wine[,-(1:2)], group=c(5,3,10,9,2),
         name.group=c("olf","vis","olfag","gust","ens"))

     ### If you want to construct the partial points for some individuals only
     plot.GPApartial (res.gpa)
     ## End(Not run)

