SensoMineR (version 1.0)

panellipse: Confidence ellipses around products based on panelists descriptions

Description

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

Usage

panellipse(donnee,col.p,col.j,firstvar,lastvar=ncol(donnee),alpha=0.05,coord=c(1,2),scale.unit=TRUE,nbsimul=500,nbchoix=NULL,bloc=NULL,name.bloc=NULL,level.search.desc=0.5,centerbypanelist=TRUE,scalebypanelist=FALSE)

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

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

bloc

the number of groups of variables when multiple factor analysis is performed (by default this parameter equals NULL and a PCA is performed)

name.bloc

the names of the groups of variables when mfa is performed (if bloc differs from NULL)

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)

Value

• 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. When a Multiple Factor Analysis is performed, returns a graph where each partial product is circled by its confidence ellipse generated by virtual panels

Details

Panellipse, step by step: Step 1 Performs a selection of discriminating descriptors with respect to a threshold set by users Step 2 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 3 Coordinates of the products with respect to each virtual panels are computed Step 4 Each product is then circled by its confidence ellipse generated by virtual panels and comprising (1-alpha)*100 percent of the virtual products

References

Husson F., Le Dien S. & Pag�s J. (2005). Confidence ellipse for the sensory profiles obtained by Principal Components Analysis. Food Quality and Preference. 16 (3), 245-250. Pag�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.

See Also

panellipse.session

Examples

## Example 1: PCA
data(chocolates)
panellipse(chocolates,col.p=4,col.j=1,firstvar=5)

## And if we consider only 12 panelists in a virtual panel, what would be the size of the ellipses
panellipse(chocolates,col.p=4,col.j=1,nbchoix=12, firstvar=5)

## And if we want the confidence ellipses around the individual descriptions
panellipse(chocolates,col.p=4,col.j=1,nbchoix=1, firstvar=5)


## Example 2: MFA
data(chocolates)
panellipse(chocolates,col.p=4,col.j=1,firstvar=5,bloc=c(6,8),name.bloc=c("G1","G2"))