paneliperf: Panelists' performance according to their capabilities to...

In SensoMineR: Sensory data analysis with R

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

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".

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.

Author(s)

Fran<e7>ois Husson, S<e9>bastien L<ea>

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

## 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)

Example output

Loading required package: FactoMineR
dev.new(): using pdf(file="Rplots1.pdf")
dev.new(): using pdf(file="Rplots2.pdf")
dev.new(): using pdf(file="Rplots3.pdf")

SensoMineR documentation built on May 2, 2019, 5:56 p.m.