fruits: Pair of Tables

In ade4: Analysis of Ecological Data : Exploratory and Euclidean Methods in Environmental Sciences

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))
}

ade4 documentation built on May 2, 2019, 5:50 p.m.