Virtual panels are generated using Boostrap techniques in order to display confidence ellipses around products.
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,
graph.type = c("ggplot","classic"))A list containing the following elements:
the data by session
a matrix with the component of the factor analysis (in row) and the eigenvalues, the inertia and the cumulative inertia for each component
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
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
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.
a data frame made up of at least two qualitative variables (product, panelist) and a set of quantitative variables (sensory descriptors)
the position of the product variable
the position of the panelist variable
the position of the session variable
the position of the first sensory descriptor
the position of the last sensory descriptor (by default the last column of donnee)
the confidence level of the ellipses
a length 2 vector specifying the components to plot
boolean, if T the descriptors are scaled to unit variance
the number of simulations (corresponding to the number of virtual panels) used to compute the ellipses
the number of panelists forming a virtual panel, by default the number of panelists in the original panel
the threshold above which a descriptor is not considered as discriminant according to AOV model "descriptor=Product+Panelist"
boolean, if T center the data by panelist before the construction of the axes
boolean, if T scale the data by panelist before the construction of the axes (by default, FALSE is assigned to that parameter)
boolean, if T then the name of each panelist is displayed on the plotpanelist graph (by default, FALSE
is assigned to that parameter)
boolean, if T a plot with the variability of the variable is drawn and a confidence intervals of the correlations between descriptors are calculated
cf. function par in the graphics package
a vector with the colors used; by default there are 35 colors defined
a character that gives the type of graph used: "ggplot" or "classic"
F Husson, S Le
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
Husson F., Le Dien S. & Pages J. (2005). Confidence ellipse for the sensory profiles obtained by Principal Components Analysis. Food Quality and Preference. 16 (3), 245-250.
Pages 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., Le S. & Pages 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.
panellipse
if (FALSE) {
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=""))
}
Run the code above in your browser using DataLab