Method to Analyse a pair of tables : Environmental and Faunistic Data
performs a special multivariate analysis for ecological data.
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, ...)
dudiX |
a duality diagram providing from a function |
Y |
a data frame sites-species according to |
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 |
... |
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 |
nrepet |
the number of permutations for the testing procedure |
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 |
Daniel Chessel
Anne-Béatrice Dufour anne-beatrice.dufour@univ-lyon1.fr
Stéphane Dray stephane.dray@univ-lyon1.fr
Dolédec, S., Chessel, D. and Gimaret, C. (2000) Niche separation in community analysis: a new method. Ecology, 81, 2914–1927.
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))Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.