woangers {ade4} | R Documentation |
This data set gives the presence of plant species in relevés of woodlands in the conurbation of Angers; and their biological traits.
data(woangers)
woangers
is a list of 2 components.
li
, nominal variable that indicates the presence (y) or absence (n) of
ligneous structures;pr
, nominal variable that indicates the presence (y) or absence (n) of
prickly structures;fo
, circular variable that indicates the month when the flowering period
starts (from 1 January to 9 September);he
, ordinal variable that indicates the maximum height of the leaf
canopy;ae
, ordinal variable that indicates the degree of aerial vegetative
multiplication;un
, ordinal variable that indicates the degree of underground vegetative
multiplication;lp
, nominal variable that represents the leaf position by 3 levels (ros
=
rosette, semiros
= semi-rosette and leafy
= leafy stem);le
, nominal variable that represents the mode of leaf persistence by 5
levels (seasaes
= seasonal aestival, seashib
= seasonal hibernal, seasver
=
seasonal vernal, everalw
= always evergreen, everparti
= partially evergreen);auto
= autopollination, insects
= pollination by insects, wind
=
pollination by wind); this fuzzy variable is expressed as proportions, i.e. for each
row, the sum of the three columns equals 1;elaio
= dispersion by ants, endozoo
= injection by animals, epizoo
=
external transport by animals, wind
= transport by wind, unsp
= unspecialized
transport); this fuzzy variable is expressed as proportions, i.e. for each row, the
sum of the three columns equals 1;lo
, quantitative variable that provides the seed bank longevity index;lf
, quantitative variable that provides the length of the flowering
period.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.
# 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)