panelperf             package:SensoMineR             R Documentation

_P_a_n_e_l'_s _p_e_r_f_o_r_m_a_n_c_e _a_c_c_o_r_d_i_n_g _t_o _i_t_s _c_a_p_a_b_i_l_i_t_i_e_s _t_o _d_i_c_r_i_m_i_n_a_t_e _b_e_t_w_e_e_n _p_r_o_d_u_c_t_s

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

     Computes automatically P-values associated with the F-test as well
     as the residual term for a given analysis of variance model.

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

     panelperf(donnee, formul, subset = NULL, firstvar, 
         lastvar = ncol(donnee), random = TRUE)

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

  donnee: a data frame

  formul: the model that is to be tested

  subset: cf. function 'lm' in the 'stats' package

firstvar: the position of the first endogenous variable

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

  random: boolean, effect should be possible as fixed or random
          (default as random)

_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+Session"'.

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

     A list containing the following components: 

 p.value: a matrix of dimension (_k,m_) of _P-values_ associated with
          the F-test for the  _k_ descriptors and the _m_ factors and
          their combinations considered in the analysis of variance
          model of interest

variability: a matrix of dimension (_k,m_) where the entries correspond
          to the percentages of variability due to the effects
          introduced in the analysis of variance model of interest

     res: a vector of dimension _k_ of residual terms for the analysis
          of variance model of interest

      r2: a vector of dimension _k_ of r-squared for the analysis of
          variance model of interest

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

     Franois Husson, Sbastien L

_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:

     'paneliperf', 'aov'

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

     data(chocolates)
     res=panelperf(sensochoc, firstvar = 5, formul = "~Product+Panelist+
         Session+Product:Panelist+Session:Product+Panelist:Session")
     ## Sort results by product p.values.
     coltable(magicsort(res$p.value, sort.mat = res$p.value[,1], bycol = FALSE,
         method = "median"), main.title = "Panel performance (sorted by product P-value)")

