ade4: divcmax – R documentation

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divcmax

Maximal value of Rao's diversity coefficient also called quadratic entropy

Description

For a given dissimilarity matrix, this function calculates the maximal value of Rao's diversity coefficient over all frequency distribution. It uses an optimization technique based on Rosen's projection gradient algorithm and is verified using the Kuhn-Tucker conditions.

Usage

divcmax(dis, epsilon, comment)

Arguments

`dis`	an object of class `dist` containing distances or dissimilarities among elements.
`epsilon`	a tolerance threshold : a frequency is non null if it is higher than epsilon.
`comment`	a logical value indicating whether or not comments on the optimization technique should be printed.

Value

Returns a list

`value`	the maximal value of Rao's diversity coefficient.
`vectors`	a data frame containing four frequency distributions : `sim` is a simple distribution which is equal to D1/1^tD1, `pro` is equal to z/1^tz1, where z is the nonnegative eigenvector of the matrix containing the squared dissimilarities among the elements, `met` is equal to z^2, `num` is a frequency vector maximizing Rao's diversity coefficient.

Author(s)

Stéphane Champely Stephane.Champely@univ-lyon1.fr
Sandrine Pavoine pavoine@mnhn.fr

References

Rao, C.R. (1982) Diversity and dissimilarity coefficients: a unified approach. Theoretical Population Biology, 21, 24–43.

Gini, C. (1912) Variabilità e mutabilità. Universite di Cagliari III, Parte II.

Simpson, E.H. (1949) Measurement of diversity. Nature, 163, 688.

Champely, S. and Chessel, D. (2002) Measuring biological diversity using Euclidean metrics. Environmental and Ecological Statistics, 9, 167–177.

Pavoine, S., Ollier, S. and Pontier, D. (2005) Measuring diversity from dissimilarities with Rao's quadratic entropy: are any dissimilarities suitable? Theoretical Population Biology, 67, 231–239.

Examples

data(elec88)

# Dissimilarity matrix.
d0 <- dist(elec88$xy/100)

# Frequency distribution maximizing spatial diversity in France
# according to Rao's quadratic entropy.
France.m <- divcmax(d0)
w0 <- France.m$vectors$num
v0 <- France.m$value
idx <- (1:94) [w0 > 0]

if(!adegraphicsLoaded()) {
  # Smallest circle including all the 94 departments.
  # The squared radius of that circle is the maximal value of the
  # spatial diversity.
  w1 <- elec88$xy[idx, ]/100
  w.c <- apply(w1 * w0[idx], 2, sum)
  plot(elec88$xy[, 1]/100, elec88$xy[, 2]/100, asp=1)
  symbols(w.c[1], w.c[2], circles = sqrt(v0), inches = FALSE, add = TRUE)
  s.value(elec88$xy/100, w0, add.plot = TRUE)
}

ade4

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

v1.7-16

GPL (>= 2)

Authors

Stéphane Dray <stephane.dray@univ-lyon1.fr>, Anne-Béatrice Dufour <anne-beatrice.dufour@univ-lyon1.fr>, and Jean Thioulouse <jean.thioulouse@univ-lyon1.fr>, with contributions from Thibaut Jombart, Sandrine Pavoine, Jean R. Lobry, Sébastien Ollier, Daniel Borcard, Pierre Legendre, Stéphanie Bougeard and Aurélie Siberchicot. Based on earlier work by Daniel Chessel.

Initial release