woangers {ade4}R Documentation

Plant assemblages in woodlands of the conurbation of Angers (France)

Description

This data set gives the presence of plant species in relev├ęs of woodlands in the conurbation of Angers; and their biological traits.

Usage

data(woangers)

Format

woangers is a list of 2 components.

  1. flo: is a data frame that contains the presence/absence of species in each sample site. In the codes for the sample sites (first column of the data frame), the first three letters provide the code of the woodland and the numbers represent the 5 quadrats sampled in each site. Codes for the woodlands are based on either their local name when they have one or on the name of the nearest locality.

  2. traits: is a data frame that contains the values of the 13 functional traits considered in the paper. One trait can be encoded by several columns. The codes are as follows:

Source

Pavoine, S., Vallet, J., Dufour, A.-B., Gachet, S. and Daniel, H. (2009) On the challenge of treating various types of variables: Application for improving the measurement of functional diversity. Oikos, 118, 391–402.

Examples

# Loading the data
data(woangers)

# Preparating of the traits
traits <- woangers$traits
# Nominal variables 'li', 'pr', 'lp' and 'le'
# (see table 1 in the main text for the codes of the variables)
tabN <- traits[, c(1:2, 7, 8)]
# Circular variable 'fo'
tabC <- traits[3]
tabCp <- prep.circular(tabC, 1, 12)
# The levels of the variable lie between 1 (January) and 12 (December).
# Ordinal variables 'he', 'ae' and 'un'
tabO <- traits[, 4:6]
# Fuzzy variables 'mp', 'pe' and 'di'
tabF <- traits[, 9:19]
tabFp <- prep.fuzzy(tabF, c(3, 3, 5), labels = c("mp", "pe", "di"))
# 'mp' has 3 levels, 'pe' has 3 levels and 'di' has 5 levels.
# Quantitative variables 'lo' and 'lf'
tabQ <- traits[, 20:21]

# Combining the traits
ktab1 <- ktab.list.df(list(tabN, tabCp, tabO, tabFp, tabQ))
## Not run: 
# Calculating the distances for all traits combined
distrait <- dist.ktab(ktab1, c("N", "C", "O", "F", "Q"))
is.euclid(distrait)

# Calculating the contribution of each trait in the combined distances
contrib <- kdist.cor(ktab1, type = c("N", "C", "O", "F", "Q"))
contrib
dotchart(sort(contrib$glocor), labels = rownames(contrib$glocor)[order(contrib$glocor[, 1])])

## End(Not run)

[Package ade4 version 1.7-4 Index]