niche {ade4}R Documentation

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 B Dufour dufour@biomserv.univ-lyon1.fr
Stephane Dray dray@biomserv.univ-lyon1.fr

References

Dol├ędec, 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$mil, scale = TRUE, scan = FALSE, nf = 3)
nic1 <- niche(dudi1, doubs$poi, scann = FALSE)

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$ls, as.data.frame(doubs$poi[,i]),
    csub = 2, sub = names(doubs$poi)[i])

par(mfrow = c(1,1))
s.arrow(nic1$li, clab = 0.7)

par(mfrow = c(1,1))
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))

Worked out examples


> library(ade4)
> ### Name: niche
> ### Title: Method to Analyse a pair of tables : Environmental and Faunistic
> ###   Data
> ### Aliases: niche plot.niche print.niche niche.param rtest.niche
> ### Keywords: multivariate
> 
> ### ** Examples
> 
> data(doubs)
> dudi1 <- dudi.pca(doubs$mil, scale = TRUE, scan = FALSE, nf = 3)
> nic1 <- niche(dudi1, doubs$poi, scann = FALSE)
> 
> 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$ls, as.data.frame(doubs$poi[,i]),
+     csub = 2, sub = names(doubs$poi)[i])
> 
> par(mfrow = c(1,1))
> s.arrow(nic1$li, clab = 0.7)
> 
> par(mfrow = c(1,1))
> data(trichometeo)
> pca1 <- dudi.pca(trichometeo$meteo, scan = FALSE)
> nic1 <- niche(pca1, log(trichometeo$fau + 1), scan = FALSE)
> plot(nic1)
> niche.param(nic1)
      inertia         OMI       Tol     Rtol  omi  tol rtol
Che  6.433882  2.77316816 1.0214504 2.639263 43.1 15.9 41.0
Hyc 11.914482  4.44884944 2.3877161 5.077916 37.3 20.0 42.6
Hym 10.573796  0.09548554 2.5386420 7.939669  0.9 24.0 75.1
Hys  7.625791  0.63040842 0.7348512 6.260531  8.3  9.6 82.1
Psy 10.470153  0.43447855 3.9237418 6.111932  4.1 37.5 58.4
Aga  7.430579  1.29116377 1.5507447 4.588670 17.4 20.9 61.8
Glo 14.360078  6.17685139 4.7591657 3.424061 43.0 33.1 23.8
Ath 11.244671  1.79679264 2.7654073 6.682471 16.0 24.6 59.4
Cea 18.711518 12.23859181 4.1775853 2.295341 65.4 22.3 12.3
Ced 11.789951  0.87321186 3.2451344 7.671604  7.4 27.5 65.1
Set 12.607986  4.28597109 3.7224679 4.599547 34.0 29.5 36.5
All  6.805252  0.72091250 1.2144331 4.869906 10.6 17.8 71.6
Han 10.368865  1.20620645 3.3672977 5.795361 11.6 32.5 55.9
Hfo 17.543552  6.75786236 7.3444406 3.441250 38.5 41.9 19.6
Hsp 13.976515  2.89982751 5.6222008 5.454487 20.7 40.2 39.0
Hve 12.253601  4.59849113 3.5177233 4.137387 37.5 28.7 33.8
Sta  9.391826  0.58873968 2.5226450 6.280442  6.3 26.9 66.9
> rtest(nic1,19)
class: krandtest 
Monte-Carlo tests
Call: as.krandtest(sim = t(sim), obs = obs)

Test number:   18 
Permutation number:   19 
       Test         Obs    Std.Obs   Alter Pvalue
1       Che  2.77316816 -0.4300319 greater   0.60
2       Hyc  4.44884944  0.9686935 greater   0.20
3       Hym  0.09548554  1.5288828 greater   0.15
4       Hys  0.63040842 -1.2178823 greater   0.90
5       Psy  0.43447855 11.1164776 greater   0.05
6       Aga  1.29116377  5.6731334 greater   0.05
7       Glo  6.17685139 10.4320863 greater   0.05
8       Ath  1.79679264  3.2453635 greater   0.05
9       Cea 12.23859181  4.4209467 greater   0.05
10      Ced  0.87321186  6.4112531 greater   0.05
11      Set  4.28597109 12.7850679 greater   0.05
12      All  0.72091250  0.6687294 greater   0.20
13      Han  1.20620645  1.1363174 greater   0.15
14      Hfo  6.75786236  2.9347726 greater   0.10
15      Hsp  2.89982751  9.3821772 greater   0.05
16      Hve  4.59849113  1.5507335 greater   0.10
17      Sta  0.58873968  4.6965029 greater   0.05
18 OMI.mean  3.04805955  7.0232114 greater   0.05

other elements: NULL
> 
> data(rpjdl)
> plot(niche(dudi.pca(rpjdl$mil, scan = FALSE), rpjdl$fau, scan = FALSE))
> 
> 
> 
> 

[Package ade4 Index]