paneliperf            package:SensoMineR            R Documentation

_P_a_n_e_l_i_s_t_s' _p_e_r_f_o_r_m_a_n_c_e _a_c_c_o_r_d_i_n_g _t_o _t_h_e_i_r _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, Vtests, residuals, r-square for 
     each category of a given qualitative variable (e.g. the _panelist_
     variable);
      Computes he agreement between each panelist and the panel
     results;
      Gives the panel results (optional).

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

     paneliperf(donnee, formul, formul.j = "~Product", col.j, firstvar,
           lastvar = ncol(donnee), synthesis = FALSE, random = TRUE, 
           graph = FALSE)

_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 aov model used for the panel

formul.j: the aov model used for each panelist (no _panelist_ effect
          allowed)

   col.j: the position of the _panelist_ variable

firstvar: the position of the first endogenous variable

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

synthesis: boolean, the possibility to have the anova results for the
          panel model

  random: boolean, the status of the Panelist variable in the anova
          model for the panel

   graph: boolean, draws the PCA and MFA graphs

_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: 

prob.ind: a matrix which rows are the panelist, which columns are the
          endogenous variables (in most cases the sensory descriptors)
          and which entries are the P-values associated to the AOV
          model

vtest.ind: a matrix which rows are the panelist, which columns are the
          endogenous variables (in most cases the sensory descriptors)
          and which entries are the Vtests associated to the AOV model

 res.ind: a matrix which rows are the panelist, which columns are the
          endogenous variables (in most cases the sensory descriptors)
          and which entries are the residuals associated to the AOV
          model

  r2.ind: a matrix which rows are the panelist, which columns are the
          endogenous variables (in most cases the sensory descriptors)
          and which entries are the R-square associated to the AOV
          model

signif.ind: a vector with the number of significant descriptors per
          panelist

agree.ind: a matrix with as many rows as there are panelists and as
          many columns as there are descriptors and  the entries of
          this matrix are the correlation coefficients between the
          product coefficients for the panel  and for the panelists

complete: a matrix with the v-test corresponding to the p.value (see
          'p.values' below), the median of the agreement (see 'agree'
          upper), the standard deviation of the panel anova model (see
          'res' below)

 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


     The usual graphs when MFA is performed on the data.frame resulting
     from vtest.ind and agree.ind. 
      The PCA graphs for the complete output.

_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:

     'panelperf', 'aov'

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

     ## Not run: 
     data(chocolates)
     res<-paneliperf(sensochoc, formul = "~Product+Panelist+Session+
       Product:Panelist+Product:Session+Panelist:Session",
       formul.j = "~Product", col.j = 1, firstvar = 5, synthesis = TRUE)
     resprob<-magicsort(res$prob.ind, method = "median")
     coltable(resprob, level.lower = 0.05, level.upper = 1,
         main.title = "P-value of the F-test (by panelist)")
     hist(resprob,main="Histogram of the P-values",xlab="P-values")

     resr2<-magicsort(res$r2.ind, method = "median", ascending = FALSE)
     coltable(resr2, level.lower = 0.00, level.upper = 0.85,
         main.title = "Adjusted R-square (by panelist)")

     resagree<-magicsort(res$agree, sort.mat = res$r2.ind, method = "median")
     coltable(resagree, level.lower = 0.00, level.upper = 0.85,
         main.title = "Agreement between panelists")
     hist(resagree,main="Histogram of the agreement between panelist and panel",
         xlab="Correlation coefficient between the product effect for 
         panelist and panel")

     coltable(magicsort(res$p.value, sort.mat = res$p.value[,1], bycol = FALSE,
         method = "median"),
         main.title = "Panel performance (sorted by product P-value)")
     ## End(Not run)

