niche: Method to Analyse a pair of tables : Environmental and...

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

Method to Analyse a pair of tables : Environmental and Faunistic Data

Description

performs a special multivariate analysis for ecological data.

Usage

niche(dudiX, Y, scannf = TRUE, nf = 2)
## S3 method for class 'niche'
print(x, ...) 
## S3 method for class 'niche'
plot(x, xax = 1, yax = 2, ...)
niche.param(x)
## S3 method for class 'niche'
rtest(xtest,nrepet=99, ...)

Arguments

dudiX	a duality diagram providing from a function dudi.coa, dudi.pca, ... using an array sites-variables
Y	a data frame sites-species according to dudiX$tab with no columns of zero
scannf	a logical value indicating whether the eigenvalues bar plot should be displayed
nf	if scannf FALSE, an integer indicating the number of kept axes
x	an object of class niche
...	further arguments passed to or from other methods
xax, yax	the numbers of the x-axis and the y-axis
xtest	an object of class niche
nrepet	the number of permutations for the testing procedure

Value

Returns a list of the class niche (sub-class of dudi) containing :

rank	an integer indicating the rank of the studied matrix
nf	an integer indicating the number of kept axes
RV	a numeric value indicating the RV coefficient
eig	a numeric vector with the all eigenvalues
lw	a data frame with the row weigths (crossed array)
tab	a data frame with the crossed array (averaging species/sites)
li	a data frame with the species coordinates
l1	a data frame with the species normed scores
co	a data frame with the variable coordinates
c1	a data frame with the variable normed scores
ls	a data frame with the site coordinates
as	a data frame with the axis upon niche axis

Author(s)

Daniel Chessel
Anne-Béatrice Dufour [e-mail]
Stéphane Dray [e-mail]

References

Dolédec, S., Chessel, D. and Gimaret, C. (2000) Niche separation in community analysis: a new method. Ecology, 81, 2914–1927.

Examples

data(doubs)
dudi1 <- dudi.pca(doubs$env, scale = TRUE, scan = FALSE, nf = 3)
nic1 <- niche(dudi1, doubs$fish, scann = FALSE)

if(adegraphicsLoaded()) {
  g1 <- s.traject(dudi1$li, plab.cex = 0, plot = FALSE)
  g2 <- s.traject(nic1$ls, plab.cex = 0, plot = FALSE)
  g3 <- s.corcircle(nic1$as, plot = FALSE)
  g4 <- s.arrow(nic1$c1, plot = FALSE)
  G1 <- ADEgS(list(g1, g2, g3, g4), layout = c(2, 2))
  
  glist <- list()
  for(i in 1:ncol(doubs$fish))
    glist[[i]] <- s.distri(nic1$ls, dfdistri = doubs$fish[, i], psub.text = names(doubs$fish)[i], 
      plot = FALSE, storeData = TRUE)
  G2 <- ADEgS(glist, layout = c(5, 6))
  
  G3 <- s.arrow(nic1$li, plab.cex = 0.7)  
    
} else {
  par(mfrow = c(2, 2))
  s.traject(dudi1$li, clab = 0)
  s.traject(nic1$ls, clab = 0)
  s.corcircle(nic1$as)
  s.arrow(nic1$c1)

  par(mfrow = c(5, 6))
  for (i in 1:27) s.distri(nic1$ls, as.data.frame(doubs$fish[,i]),
    csub = 2, sub = names(doubs$fish)[i])
    
  par(mfrow = c(1, 1))
  s.arrow(nic1$li, clab = 0.7)

}

data(trichometeo)
pca1 <- dudi.pca(trichometeo$meteo, scan = FALSE)
nic1 <- niche(pca1, log(trichometeo$fau + 1), scan = FALSE)
plot(nic1)
niche.param(nic1)
rtest(nic1,19)

data(rpjdl)
plot(niche(dudi.pca(rpjdl$mil, scan = FALSE), rpjdl$fau, scan = FALSE))

ade4 documentation built on Feb. 16, 2023, 7:58 p.m.