\inputencoding{latin1}
\HeaderA{plantTraits}{Plant Species Traits Data}{plantTraits}
\keyword{datasets}{plantTraits}
\begin{Description}\relax
This dataset constitutes a description of 136 plant species
according to biological attributes (morphological or reproductive)
\end{Description}
\begin{Usage}
\begin{verbatim}data(plantTraits)
\end{verbatim}
\end{Usage}
\begin{Format}\relax
A data frame with 136 observations on the following 31 variables.
\describe{
\item[\code{pdias}] Diaspore mass (mg)
\item[\code{longindex}] Seed bank longevity
\item[\code{durflow}] Flowering duration
\item[\code{height}] Plant height, an ordered factor with levels
\code{1} < \code{2} < \dots < \code{8}.

\item[\code{begflow}] Time of first flowering, an ordered factor with levels \code{1} < \code{2} < \code{3} < \code{4} < \code{5} < \code{6} < \code{7} < \code{8} < \code{9}

\item[\code{mycor}] Mycorrhizas, an ordered factor with levels \code{0}never < \code{1} sometimes< \code{2}always

\item[\code{vegaer}] aerial vegetative propagation, an ordered
factor with levels \code{0}never < \code{1} present but limited< \code{2}important.

\item[\code{vegsout}] underground vegetative propagation, an ordered
factor with 3 levels identical to \code{vegaer} above.

\item[\code{autopoll}] selfing pollination, an ordered factor with
levels \code{0}never < \code{1}rare < \code{2} often< the rule\code{3}

\item[\code{insects}] insect pollination, an ordered factor with 5 levels \code{0} < \dots < \code{4}.
\item[\code{wind}] wind pollination, an ordered factor with 5 levels \code{0} < \dots < \code{4}.
\item[\code{lign}] a binary factor with levels \code{0:1},
indicating if plant is woody.
\item[\code{piq}] a binary factor indicating if plant is thorny.
\item[\code{ros}] a binary factor indicating if plant is rosette.
\item[\code{semiros}] semi-rosette plant, a binary factor (\code{0}:
no; \code{1}: yes).
\item[\code{leafy}] leafy plant, a binary factor.
\item[\code{suman}] summer annual, a binary factor.
\item[\code{winan}] winter annual, a binary factor.
\item[\code{monocarp}] monocarpic perennial, a binary factor.
\item[\code{polycarp}] polycarpic perennial, a binary factor.
\item[\code{seasaes}] seasonal aestival leaves, a binary factor.
\item[\code{seashiv}] seasonal hibernal leaves, a binary factor.
\item[\code{seasver}] seasonal vernal leaves, a binary factor.
\item[\code{everalw}] leaves always evergreen, a binary factor.
\item[\code{everparti}] leaves partially evergreen, a binary factor.
\item[\code{elaio}] fruits with an elaiosome (dispersed by ants), a binary factor.
\item[\code{endozoo}] endozoochorous fruits, a binary factor.
\item[\code{epizoo}] epizoochorous fruits, a binary factor.
\item[\code{aquat}] aquatic dispersal fruits, a binary factor.
\item[\code{windgl}] wind dispersed fruits,  a binary factor.
\item[\code{unsp}] unspecialized mechanism of seed dispersal, a binary factor.
}
\end{Format}
\begin{Details}\relax
Most of factor attributes are not disjunctive. For example, a plant can be usually
pollinated by insects but sometimes self-pollination can occured.
\end{Details}
\begin{Source}\relax
Vallet, Jeanne (2005)
\emph{Structuration de communautés végétales et analyse comparative de
traits biologiques le long d'un gradient d'urbanisation}.
Mémoire de Master 2 'Ecologie-Biodiversité-Evolution';
Université Paris Sud XI, 30p.+ annexes (in french)
\end{Source}
\begin{Examples}
\begin{ExampleCode}
data(plantTraits)

## Calculation of a dissimilarity matrix
library(cluster)
dai.b <- daisy(plantTraits,
               type = list(ordratio = 4:11, symm = 12:13, asymm = 14:31))

## Hierarchical classification
agn.trts <- agnes(dai.b, method="ward")
plot(agn.trts, which.plots = 2, cex= 0.6)
plot(agn.trts, which.plots = 1)
cutree6 <- cutree(agn.trts, k=6)
cutree6

## Principal Coordinate Analysis
cmdsdai.b <- cmdscale(dai.b, k=6)
plot(cmdsdai.b[, 1:2], asp = 1, col = cutree6)
\end{ExampleCode}
\end{Examples}

