fruits {ade4} | R Documentation

## 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:

typ

  is a vector returning the type of the 28 batches of fruits (peaches or nectarines).

jug

  is a data frame of 28 rows and 16 columns (judges).

var

  is a data frame of 28 rows and 16 measures (average of 2 judgements).

### Details

`fruits$var` is a data frame of 15 variables:

1. taches: quantity of cork blemishes (0=absent - maximum 5)

2. stries: quantity of stria (1/none - maximum 4)

3. abmucr: abundance of mucron (1/absent - 4)

4. irform: shape irregularity (0/none - 3)

5. allong: length of the fruit (1/round fruit - 4)

6. suroug: percentage of the red surface (minimum 40% - maximum 90%)

7. homlot: homogeneity of the intra-batch coloring (1/strong - 4)

8. homfru: homogeneity of the intra-fruit coloring (1/strong - 4)

9. pubesc: pubescence (0/none - 4)

10. verrou: intensity of green in red area (1/none - 4)

11. foncee: intensity of dark area (0/pink - 4)

12. comucr: intensity of the mucron color (1=no contrast - 4/dark)

13. impres: kind of impression (1/watched - 4/pointillé)

14. coldom: intensity of the predominating color (0/clear - 4)

15. calibr: grade (1/<90g - 5/>200g)

### Source

Kervella, J. (1991) Analyse de l'attrait d'un produit : exemple d'une comparaison de lots de pêches. Agro-Industrie et méthodes statistiques. Compte-rendu des secondes journées européennes. Nantes 13-14 juin 1991. Association pour la Statistique et ses Utilisations, Paris, 313–325.

### Examples

    data(fruits)
    pcajug <- dudi.pca(fruits$jug, scann = FALSE)
    pcavar <- dudi.pca(fruits$var, scann = FALSE)

    if(adegraphicsLoaded()) {
      g1 <- s.corcircle(pcajug$co, plot = FALSE)
      g2 <- s.class(pcajug$li, fac = fruits$type, plot = FALSE)
      g3 <- s.corcircle(pcavar$co, plot = FALSE)
      g4 <- s.class(pcavar$li, fac = fruits$type, plot = FALSE)

      G1 <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2))
      G2 <- plot(coinertia(pcajug, pcavar, scan = FALSE))

    } else {
      par(mfrow = c(2,2))
      s.corcircle(pcajug$co)
      s.class(pcajug$li, fac = fruits$type)
      s.corcircle(pcavar$co)
      s.class(pcavar$li, fac = fruits$type)

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

[Package ade4 version 1.7-13 Index]