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

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

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 [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))

Example output

      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
class: krandtest lightkrandtest 
Monte-Carlo tests
Call: as.krandtest(sim = t(sim), obs = obs)

Number of tests:   18 

Adjustment method for multiple comparisons:   none 
Permutation number:   19 
       Test         Obs    Std.Obs   Alter Pvalue
1       Che  2.77316816 -0.1572811 greater   0.45
2       Hyc  4.44884944  0.7661069 greater   0.20
3       Hym  0.09548554  1.2392122 greater   0.20
4       Hys  0.63040842 -0.7311433 greater   0.75
5       Psy  0.43447855 12.3952869 greater   0.05
6       Aga  1.29116377  3.7319112 greater   0.05
7       Glo  6.17685139  7.5021112 greater   0.05
8       Ath  1.79679264  1.3946424 greater   0.15
9       Cea 12.23859181  2.8432704 greater   0.10
10      Ced  0.87321186  5.5837561 greater   0.05
11      Set  4.28597109  9.1767053 greater   0.05
12      All  0.72091250  1.6950531 greater   0.15
13      Han  1.20620645  1.5862793 greater   0.10
14      Hfo  6.75786236  3.2295993 greater   0.05
15      Hsp  2.89982751 11.5134692 greater   0.05
16      Hve  4.59849113  4.4095625 greater   0.05
17      Sta  0.58873968  3.9256050 greater   0.05
18 OMI.mean  3.04805955  8.9053104 greater   0.05

ade4 documentation built on May 2, 2019, 5:50 p.m.