ade4 (version 1.04)

fruits: Pair of Tables

Description

28 batches of fruits -two types- are judged by two different ways. They are classified in order of preference, without ex aequo, by 16 individuals. 15 quantitative variables described the batches of fruits.

Usage

data(fruits)

format

fruits is a list of 3 components:

1. typ

{is a vector returning the type of the 28 batches of fruits (peaches or nectarines).} jug{is a data frame of 28 rows et 16 columns (judges).} var{is a data frame of 28 rows et 16 measures (average of 2 judgements).}

source

Kervella, J. (1991) Analyse de l'attrait d'un produit : exemple d'une comparaison de lots de p�ches. Agro-Industrie et m�thodes statistiques. Compte-rendu des secondes journ�es europ�ennes. Nantes 13-14 juin 1991 . Association pour la Statistique et ses Utilisations, Paris, 313--325.

Details

fruits$var is a data frame of 15 variables:

1. 1{\_}taches

{: quantity of cork blemishes (0=absent - maximum 5)} 2{_}stries{: quantity of stria (1/none - maximum 4)} 3{_}ab{_}muc{: abundance of mucron (1/absent - 4)} 4{_}ir{_}for{: shape irregularity (0/none - 3)} 5{_}allong{: length of the fruit (1/round fruit - 4)} 6{_}su{_}rou{: percentage of the red surface (minimum 40% - maximum 90%)} 7{_}ho{_}lot{: homogeneity of the intra-batch coloring (1/strong - 4)} 8{_}ho{_}fru{: homogeneity of the intra-fruit coloring (1/strong - 4)} 9{_}pubes{: pubescence (0/none - 4)} 10{_}vert{: intensity of green in red area (1/none - 4)} 11{_}fonc{: intensity of dark area (0/pink - 4)} 12{_}col{_}mu{: intensity of the mucron color (1=no contrast - 4/dark)} 13{_}impres{: kind of impression (1/watched - 4/pointill�)} 14{_}col{_}dom{: intensity of the predominating color (0/clear - 4)} 15{_}calib{: grade (1/<90g 5="" -="">200g)}

Examples

data(fruits)
par(mfrow = c(2,2))
pcajug <- dudi.pca(fruits$jug, scann = FALSE)
s.corcircle(pcajug$co)
s.class(pcajug$li, fac = fruits$type)

pcavar <- dudi.pca(fruits$var, scann = FALSE)
s.corcircle(pcavar$co)
s.class(pcavar$li, fac = fruits$type)

par(mfrow = c(1,1))
plot(coinertia(pcajug, pcavar, scan = FALSE))