panellipse.session: Repetability of panelists descriptions studied by confidence...

In SensoMineR: Sensory data analysis with R

Description

Virtual panels are generated using Boostrap techniques in order to display confidence ellipses around products.

Usage

panellipse.session(donnee, col.p, col.j, col.s, firstvar, 
    lastvar = ncol(donnee), alpha = 0.05, coord = c(1,2), 
    scale.unit = TRUE, nbsimul = 500, nbchoix = NULL, 
    level.search.desc = 0.2,  centerbypanelist = TRUE, 
    scalebypanelist = FALSE, name.panelist = FALSE,
    variability.variable = FALSE, cex = 1, color= NULL)

Arguments

donnee	a data frame made up of at least two qualitative variables (product, panelist) and a set of quantitative variables (sensory descriptors)
col.p	the position of the product variable
col.j	the position of the panelist variable
col.s	the position of the session variable
firstvar	the position of the first sensory descriptor
lastvar	the position of the last sensory descriptor (by default the last column of donnee)
alpha	the confidence level of the ellipses
coord	a length 2 vector specifying the components to plot
scale.unit	boolean, if T the descriptors are scaled to unit variance
nbsimul	the number of simulations (corresponding to the number of virtual panels) used to compute the ellipses
nbchoix	the number of panelists forming a virtual panel, by default the number of panelists in the original panel
level.search.desc	the threshold above which a descriptor is not considered as discriminant according to AOV model "descriptor=Product+Panelist"
centerbypanelist	boolean, if T center the data by panelist before the construction of the axes
scalebypanelist	boolean, if T scale the data by panelist before the construction of the axes (by default, FALSE is assigned to that parameter)
name.panelist	boolean, if T then the name of each panelist is displayed on the plotpanelist graph (by default, FALSE is assigned to that parameter)
variability.variable	boolean, if T a plot with the variability of the variable is drawn and a confidence intervals of the correlations between descriptors are calculated
cex	cf. function par in the graphics package
color	a vector with the colors used; by default there are 35 colors defined

Details

panellipse.session, step by step:
Step 1 Construct a data frame by session
Step 2 Performs a selection of discriminating descriptors with respect to a threshold set by users
Step 3 MFA is computed with one group for one session
Step 4 Virtual panels are generated using Boostrap techniques; the number of panels as well as their size are set by users with the nbsimul and nbchoix parameters
Step 5 Coordinates of the products with respect to each virtual panels are computed
Step 6 Each product is then circled by its confidence ellipse generated by virtual panels and comprising (1-alpha)*100 percent of the virtual products

Value

A list containing the following elements:

bysession	the data by session
eig	a matrix with the component of the factor analysis (in row) and the eigenvalues, the inertia and the cumulative inertia for each component
coordinates	a list with: the coordinates of the products with respect to the panel and to each panelists and the coordinates of the partial products with respect to the panel and to each panelists
hotelling	returns a matrix with the P-values of the Hotelling's T2 tests for each pair of products: this matrix allows to find the product which are significatnly different for the 2-components sensory description
variability	returns an index of the sessions' reproductibility: the first eigenvalue of the separate PCA performed on homologous descriptors

Returns a graph of the products as well as a correlation circle of the descriptors.

Returns a graph where each product is displayed with respect to a panel and to each panelist composing the panel; products described by the panel are displayed as square, they are displayed as circle when they are described by each panelist.

Returns a graph where each product is circled by its confidence ellipse generated by virtual panels.

Returns a graph where each partial product is circled by its confidence ellipse generated by virtual panels.

Returns a graph where the variability of each variable is drawn on the correlation circle graph.

Author(s)

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

References

Husson F., Le Dien S. & Pag<e8>s J. (2005). Confidence ellipse for the sensory profiles obtained by Principal Components Analysis. Food Quality and Preference. 16 (3), 245-250.
Pag<e8>s J. & Husson F. (2005). Multiple Factor Analysis with confidence ellipses: a methodology to study the relationships between sensory and instrumental data. To be published in Journal of Chemometrics.
Husson F., L<ea> S. & Pag<e8>s J. Variability of the representation of the variables resulting from PCA in the case of a conventional sensory profile. Food Quality and Preference. 16 (3), 245-250.

See Also

panellipse

Examples

## Not run: 
data(chocolates)
res <- panellipse.session(sensochoc, col.p = 4, col.j = 1, col.s = 2, 
    firstvar = 5)
magicsort(res$variability)
for (i in 1:dim(res$hotelling$bysession)[3]) coltable(res$hotelling$bysession[,,i], 
    main.title = paste("P-values for the Hotelling's T2 tests (",
    dimnames(res$hotelling$bysession)[3][[1]][i],")",sep=""))

## 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")
                eig1 Reproductibility
Sticky      1.326885         66.34425
Granular    1.732049         86.60246
Astringency 1.866782         93.33911
Vanilla     1.919983         95.99913
CocoaA      1.925602         96.28008
Sweetness   1.927289         96.36446
Caramel     1.927345         96.36725
MilkA       1.937639         96.88194
Melting     1.956729         97.83644
MilkF       1.972064         98.60322
Bitterness  1.975258         98.76290
CocoaF      1.975552         98.77760
Crunchy     1.980369         99.01843
Acidity     1.991198         99.55990
dev.new(): using pdf(file="Rplots4.pdf")
dev.new(): using pdf(file="Rplots5.pdf")
dev.new(): using pdf(file="Rplots6.pdf")

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