Description Usage Arguments Details Value Author(s) References See Also Examples

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

1 2 3 4 5 6 | ```
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)
``` |

`donnee` |
a data frame made up of at least two qualitative variables
( |

`col.p` |
the position of the |

`col.j` |
the position of the |

`col.s` |
the position of the |

`firstvar` |
the position of the first sensory descriptor |

`lastvar` |
the position of the last sensory descriptor (by default the last column of |

`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 |

`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 |

`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 |

`color` |
a vector with the colors used; by default there are 35 colors defined |

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

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 |

`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.

F Husson, S Le

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.

1 2 3 4 5 6 7 8 9 10 | ```
## 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)
``` |

```
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 Dec. 13, 2017, 9:04 a.m.

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