SensoMineR (version 1.20)

paneliperf: Panelists' performance according to their capabilities to dicriminate between products

Description

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).

Usage

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

Arguments

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

Value

• 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.

Details

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".

References

P. Lea, T. Naes, M. Rodbotten. Analysis of variance for sensory data. H. Sahai, M. I. Ageel. The analysis of variance.

See Also

panelperf, aov

Examples

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)")